Artificial intelligence assisted wastewater treatment process optimization method, system and medium

Abstract

The invention provides an artificial intelligence-assisted wastewater treatment process optimization method and system, and a medium. The method comprises the following steps: acquiring wastewater component data, acquiring adaptive process type data according to the wastewater component data, determining effluent quality index demand data according to the adaptive process type data and wastewater recovery demand data, performing treatment to obtain adaptive process parameter data, and performing wastewater treatment according to the adaptive process parameter data. The method comprises the following steps: obtaining effluent quality evaluation indexes of different treatment links, determining a labeling link, processing according to operation deviation data of the labeling link to obtain link deviation grade data, processing according to the link deviation grade data to obtain optimized process parameter data, and carrying out process optimization on the labeling link according to the optimized process parameter data. Carrying out supply and demand matching degree evaluation and equipment stability evaluation, and carrying out process optimization reminding according to a supply and demand matching degree evaluation result and an equipment stability evaluation result; therefore, the purpose of optimizing the wastewater treatment process is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of big data and wastewater treatment process optimization, and specifically, to an artificial intelligence-assisted wastewater treatment process optimization method, system and medium. Background Art

[0002] The composition of wastewater is complex and changeable. Wastewater from different sources contains various pollutants such as organic matter and heavy metals. The concentrations and properties of these pollutants vary greatly, making it difficult for a single treatment process to meet the treatment needs of different types of wastewater. Traditional wastewater treatment processes usually rely on experience and fixed operating parameters, lack flexibility and adaptability, and are difficult to cope with dynamic changes in wastewater quality. Artificial intelligence technology can collect and analyze various data in the wastewater treatment process to achieve real-time monitoring and optimization of the treatment process. However, existing artificial intelligence-based wastewater treatment process optimization methods often fail to fully consider the uncertainty factors in the wastewater treatment process, such as equipment overload operation, resulting in low reliability of the optimization results and failure to fully consider the needs of wastewater recycling users.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of the present application is to provide an artificial intelligence-assisted wastewater treatment process optimization method, system and medium. First, the adaptation process type is obtained according to the wastewater composition data, and the effluent water quality index requirements are determined according to the adaptation process type and the wastewater recovery requirements, so as to obtain the adaptation process parameter data, and then the effluent water quality of different treatment links is evaluated, and then the links with abnormal water quality are marked, and then the link deviation level of the marked links is determined, and then the optimized process parameter data is obtained, and the supply and demand matching evaluation and equipment stability evaluation are performed, and the process optimization reminder is given according to the supply and demand matching evaluation results and the equipment stability evaluation results, so as to achieve the purpose of intelligent optimization of the wastewater treatment process.

[0005] The present application also provides an artificial intelligence-assisted wastewater treatment process optimization method, comprising the following steps: Obtain wastewater composition data, input the wastewater composition data into the preset wastewater treatment process control platform database for matching and identification, and obtain the adapted process type data; Acquire wastewater recycling demand data, determine effluent water quality index demand data according to the adaptation process type data and the wastewater recycling demand data, and obtain adaptation process parameter data according to the effluent water quality index demand data; Treat wastewater according to the adapted process parameter data, obtain effluent water quality index data of different treatment links, process according to the effluent water quality index data, obtain effluent water quality assessment index, and determine the marking link; The operation deviation data of the marking link is obtained, link deviation level data is obtained according to the operation deviation data processing, optimized process parameter data is obtained according to the link deviation level data processing, and the process of the marking link is optimized according to the optimized process parameter data.

[0006] Optionally, in the artificial intelligence-assisted wastewater treatment process optimization method described in the present application, the wastewater composition data is obtained, and the wastewater composition data is input into a preset wastewater treatment process flow control platform database for matching and identification to obtain the adaptation process type data, including: Obtain wastewater composition data, including organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data; The organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data are input into the preset wastewater treatment process flow control platform database for matching and identification to obtain the adapted process type data.

[0007] Optionally, in the artificial intelligence-assisted wastewater treatment process optimization method described in this application, the Obtain wastewater recycling demand data, determine effluent water quality index demand data according to the adaptation process type data and the wastewater recycling demand data, and obtain adaptation process parameter data according to the effluent water quality index demand data, including: Obtain wastewater recycling demand data, including turbidity demand data, heavy metal content demand data, salt content demand data, nutrient content demand data and microbial content demand data; Input the turbidity demand data, heavy metal content demand data, salt content demand data, nutrient content demand data, microbial content demand data and the adaptation process type data into a preset water quality index demand matching model for processing to obtain effluent water quality index demand data; The effluent water quality index demand data include chemical oxygen demand demand data, biochemical oxygen demand data, suspended solids content demand data, pH demand data, heavy metal content demand data and nitrogen and phosphorus content demand data; The organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data as well as the chemical oxygen demand data, biochemical oxygen demand data, suspended solids content demand data, pH demand data, heavy metal content demand data and nitrogen and phosphorus content demand data are input into a preset wastewater treatment process parameter prediction model for processing to obtain adapted process parameter data.

[0008] Optionally, in the artificial intelligence-assisted wastewater treatment process optimization method described in the present application, the wastewater treatment is performed according to the adapted process parameter data, effluent water quality index data of different treatment links are obtained, the effluent water quality index data is processed to obtain an effluent water quality assessment index, and the labeling link is determined, including: Perform wastewater treatment according to the adapted process parameter data to obtain effluent water quality index data of different treatment links, including chemical oxygen demand data, biochemical oxygen demand data, suspended solids content data, pH data, heavy metal content data, and nitrogen and phosphorus content data; The chemical oxygen demand data, biochemical oxygen demand data, suspended solids content data, pH data, heavy metal content data and nitrogen and phosphorus content data of different treatment links are calculated respectively to obtain the effluent water quality assessment index corresponding to different treatment links; The effluent water quality assessment index of different processing links is compared with the preset effluent water quality assessment index threshold of the corresponding link, and the processing link whose threshold comparison result does not meet the requirements of the preset threshold comparison result is marked as a link.

[0009] Optionally, in the artificial intelligence-assisted wastewater treatment process optimization method described in the present application, the obtaining of the operation deviation data of the marked link, obtaining link deviation level data according to the operation deviation data processing, obtaining optimized process parameter data according to the link deviation level data processing, and performing process optimization on the marked link according to the optimized process parameter data include: Obtaining the operation deviation data of the marking link, including water inlet speed deviation data, operation time deviation data, energy consumption deviation data and water outlet flow deviation data; Calculate the link deviation evaluation index based on the water inlet speed deviation data, the operating time deviation data, the energy consumption deviation data and the water outlet flow deviation data; The link deviation evaluation index is compared with a preset link deviation evaluation index threshold, and if the threshold comparison result does not meet the preset threshold comparison result requirement, the link deviation level data is determined according to the range level to which the threshold comparison result belongs; The link deviation level data is input into the preset wastewater treatment process control platform database for matching and identification to obtain optimized process parameter data; The marking process is optimized according to the optimized process parameter data.

[0010] Optionally, the artificial intelligence-assisted wastewater treatment process optimization method described in the present application further includes: Obtaining the effluent water quality evaluation index and effluent production data after process optimization, and using the effluent water quality evaluation index after process optimization as the optimized effluent water quality evaluation index; Inputting the optimized effluent water quality assessment index into a preset supply user matching database for matching identification to obtain recommended supply user data; Obtain wastewater usage demand data and effluent water quality index demand index for recommended supply users; Calculate the supply-demand matching evaluation index based on the effluent water quality evaluation index, the effluent water quality index demand index, the effluent production data and the wastewater usage demand data; The supply-demand matching evaluation index is compared with a preset supply-demand matching evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, a process optimization reminder is issued.

[0011] Optionally, the artificial intelligence-assisted wastewater treatment process optimization method described in the present application further includes: Conduct high-load tests on wastewater treatment equipment to obtain wastewater inflow data, equipment operating parameter deviation data, equipment energy consumption deviation data and their corresponding effluent water quality assessment index; Calculate the equipment stability evaluation index based on the optimized effluent water quality evaluation index and the wastewater inflow data, equipment operation parameter deviation data, equipment energy consumption deviation data and their corresponding effluent water quality evaluation index; The equipment stability evaluation index is compared with a preset equipment stability evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, a process optimization reminder is issued.

[0012] In a second aspect, the present application provides an artificial intelligence-assisted wastewater treatment process optimization system, the system comprising: a memory and a processor, the memory storing a program of an artificial intelligence-assisted wastewater treatment process optimization method, and the program of the artificial intelligence-assisted wastewater treatment process optimization method is executed by the processor to implement the following steps: Obtain wastewater composition data, input the wastewater composition data into the preset wastewater treatment process control platform database for matching and identification, and obtain the adapted process type data; Acquire wastewater recycling demand data, determine effluent water quality index demand data according to the adaptation process type data and the wastewater recycling demand data, and obtain adaptation process parameter data according to the effluent water quality index demand data; Treat wastewater according to the adapted process parameter data, obtain effluent water quality index data of different treatment links, process according to the effluent water quality index data, obtain effluent water quality assessment index, and determine the marking link; The operation deviation data of the marking link is obtained, link deviation level data is obtained according to the operation deviation data processing, optimized process parameter data is obtained according to the link deviation level data processing, and the process of the marking link is optimized according to the optimized process parameter data.

[0013] Optionally, in the artificial intelligence-assisted wastewater treatment process optimization system described in the present application, the wastewater composition data is obtained, and the wastewater composition data is input into a preset wastewater treatment process flow control platform database for matching and identification to obtain the adaptation process type data, including: Obtain wastewater composition data, including organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data; The organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data are input into the preset wastewater treatment process flow control platform database for matching and identification to obtain the adapted process type data.

[0014] In a third aspect, the present application also provides a computer-readable storage medium, which stores an artificial intelligence-assisted wastewater treatment process optimization method program. When the artificial intelligence-assisted wastewater treatment process optimization method program is executed by a processor, the steps of the artificial intelligence-assisted wastewater treatment process optimization method as described in any one of the above items are implemented.

[0015] From the above, it can be seen that the present application provides an artificial intelligence-assisted wastewater treatment process optimization method, system and medium, which first obtains the adaptation process type based on the wastewater composition data, determines the effluent water quality index requirements based on the adaptation process type and the wastewater recycling requirements, and then obtains the adaptation process parameter data, and then evaluates the effluent water quality of different treatment links, and then marks the links with abnormal water quality, and then determines the link deviation level of the marked links, and then obtains the optimized process parameter data, and performs supply and demand matching evaluation and equipment stability evaluation, and performs process optimization reminders based on the supply and demand matching evaluation results and the equipment stability evaluation results, thereby achieving the purpose of intelligent optimization of the wastewater treatment process.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1A flow chart of an artificial intelligence-assisted wastewater treatment process optimization method provided in an embodiment of the present application; Figure 2 A flow chart of obtaining adapted process type data for the artificial intelligence-assisted wastewater treatment process optimization method provided in an embodiment of the present application; Figure 3 A flow chart of obtaining adapted process parameter data for the artificial intelligence-assisted wastewater treatment process optimization method provided in an embodiment of the present application; Figure 4 A flowchart of the determination and labeling phase of the artificial intelligence-assisted wastewater treatment process optimization method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 The following is a flow chart of an artificial intelligence-assisted wastewater treatment process optimization method in some embodiments of the present application. The artificial intelligence-assisted wastewater treatment process optimization method is used in terminal devices, such as computers, mobile phone terminals, etc. The artificial intelligence-assisted wastewater treatment process optimization method comprises the following steps: S11. Obtain wastewater composition data, input the wastewater composition data into a preset wastewater treatment process control platform database for matching and identification, and obtain the adapted process type data; S12, obtaining wastewater recycling demand data, determining effluent water quality index demand data according to the adaptation process type data and the wastewater recycling demand data, and obtaining adaptation process parameter data according to the effluent water quality index demand data; S13, treating wastewater according to the adapted process parameter data, obtaining effluent water quality index data of different treatment links, performing processing according to the effluent water quality index data, obtaining an effluent water quality assessment index, and determining a marking link; S14, obtaining the operation deviation data of the marking link, obtaining link deviation level data according to the operation deviation data processing, obtaining optimized process parameter data according to the link deviation level data processing, and performing process optimization on the marking link according to the optimized process parameter data.

[0022] It should be noted that first, the adaptation process type is obtained according to the wastewater composition data, and the effluent water quality index requirements are determined according to the adaptation process type and the wastewater recycling requirements, and then the adaptation process parameter data is obtained, and then the effluent water quality of different treatment links is evaluated, and then the links with abnormal water quality are marked, and then the link deviation level of the marked link is determined, and then the optimized process parameter data is obtained, and the supply and demand matching evaluation and equipment stability evaluation are carried out. According to the supply and demand matching evaluation results and the equipment stability evaluation results, process optimization reminders are given, thereby achieving the purpose of intelligent optimization of the wastewater treatment process.

[0023] Please refer to Figure 2 , Figure 2 It is a flow chart of obtaining the adaptation process type data of the artificial intelligence-assisted wastewater treatment process optimization method in some embodiments of the present application. According to an embodiment of the present invention, the wastewater composition data is obtained, and the wastewater composition data is input into a preset input preset wastewater treatment process flow control platform database for matching and identification, and the adaptation process type data is obtained, including: S21. Obtain wastewater composition data, which includes organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data, and microbial content data; S22. Input the organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data into a preset wastewater treatment process control platform database for matching and identification to obtain adapted process type data.

[0024] It should be noted that different wastewater components are suitable for different process types. For wastewater containing high concentrations of heavy metals, chemical precipitation, ion exchange and other processes may be needed to effectively remove heavy metal ions. For wastewater with high organic content, biological treatment processes such as activated sludge and biofilm methods may be more suitable, using the metabolism of microorganisms to decompose organic matter into harmless substances. For wastewater containing difficult-to-degrade organic matter, special processes such as advanced oxidation technology may be required to destroy the structure of organic matter and improve biodegradability. Therefore, it is necessary to determine the appropriate process type based on the wastewater composition.

[0025] Please refer to Figure 3 , Figure 3The present invention is a flowchart of obtaining the adaptive process parameter data of the artificial intelligence-assisted wastewater treatment process optimization method in some embodiments of the present application. According to an embodiment of the present invention, the acquisition of wastewater recycling demand data, the determination of effluent water quality index demand data according to the adaptive process type data and the wastewater recycling demand data, and the acquisition of adaptive process parameter data according to the effluent water quality index demand data include: S31. Obtain wastewater recycling demand data, including turbidity demand data, heavy metal content demand data, salt content demand data, nutrient content demand data and microbial content demand data; S32, inputting the turbidity demand data, heavy metal content demand data, salt content demand data, nutrient content demand data, microbial content demand data and the adaptation process type data into a preset water quality index demand matching model for processing to obtain effluent water quality index demand data; S33, the effluent water quality index demand data includes chemical oxygen demand demand data, biochemical oxygen demand data, suspended solids content demand data, pH demand data, heavy metal content demand data and nitrogen and phosphorus content demand data; S34. Input the organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data as well as the chemical oxygen demand data, biochemical oxygen demand data, suspended solids content demand data, pH demand data, heavy metal content demand data and nitrogen and phosphorus content demand data into a preset wastewater treatment process parameter prediction model for processing to obtain adapted process parameter data.

[0026] It should be noted that the effluent water quality index requirement data is determined according to the adaptation process type and the wastewater recycling demand, and the adapted process parameters are obtained by processing the effluent water quality index requirement data. The preset water quality index requirement matching model is a model obtained by training with turbidity requirement data, heavy metal content requirement data, salt content requirement data, nutrient content requirement data and microbial content requirement data of a large number of historical samples as well as the adapted process type data and effluent water quality index requirement data. The corresponding output effluent water quality index requirement data can be obtained by inputting relevant information for processing. The preset wastewater treatment process parameter prediction model is a model obtained by training with organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data of a large number of historical samples as well as the chemical oxygen demand data, biochemical oxygen demand data, suspended solids content demand data, pH demand data, heavy metal content demand data, nitrogen and phosphorus content demand data and adapted process parameter data. The corresponding output adapted process parameter data can be obtained by inputting relevant information for processing.

[0027] Please refer to Figure 4 , Figure 4It is a flow chart of determining the marking link of the artificial intelligence-assisted wastewater treatment process optimization method in some embodiments of the present application. According to an embodiment of the present invention, the wastewater is treated according to the adapted process parameter data, the effluent water quality index data of different treatment links are obtained, the effluent water quality index data is processed, the effluent water quality evaluation index is obtained, and the marking link is determined, including: S41, treating wastewater according to the adapted process parameter data, and obtaining effluent water quality index data of different treatment links, including chemical oxygen demand data, biochemical oxygen demand data, suspended solids content data, pH data, heavy metal content data, and nitrogen and phosphorus content data; S42, respectively calculating the chemical oxygen demand data, biochemical oxygen demand data, suspended solids content data, pH data, heavy metal content data and nitrogen and phosphorus content data of different treatment links to obtain effluent water quality assessment indexes corresponding to different treatment links; S43, comparing the effluent water quality assessment index of different processing links with the preset effluent water quality assessment index thresholds of the corresponding links, and marking the processing links whose threshold comparison results do not meet the requirements of the preset threshold comparison results as the marking links.

[0028] It should be noted that the chemical oxygen demand data is an indicator used to measure the content of organic matter in the wastewater after treatment, and the biochemical oxygen demand data is an indicator used to measure the content of microorganisms in the wastewater after treatment. The effluent quality of different treatment links is tested to obtain the effluent quality assessment index corresponding to different treatment links, and the abnormal links are determined and marked according to the effluent quality assessment index; The calculation formula of the effluent water quality assessment index is: ; in, is the effluent water quality assessment index, is the chemical oxygen demand data, For biochemical oxygen demand data, is the suspended matter content data, is the pH data, is the heavy metal content data, is the nitrogen and phosphorus content data, , , , , and It is the preset characteristic coefficient (which can be obtained by querying the preset wastewater treatment process control platform database).

[0029] According to an embodiment of the present invention, the step of obtaining the operation deviation data of the marking link, obtaining link deviation level data according to the operation deviation data processing, obtaining optimized process parameter data according to the link deviation level data processing, and performing process optimization on the marking link according to the optimized process parameter data includes: Obtaining the operation deviation data of the marking link, including water inlet speed deviation data, operation time deviation data, energy consumption deviation data and water outlet flow deviation data; Calculate the link deviation evaluation index based on the water inlet speed deviation data, the operating time deviation data, the energy consumption deviation data and the water outlet flow deviation data; The link deviation evaluation index is compared with a preset link deviation evaluation index threshold, and if the threshold comparison result does not meet the preset threshold comparison result requirement, the link deviation level data is determined according to the range level to which the threshold comparison result belongs; The link deviation level data is input into the preset wastewater treatment process control platform database for matching and identification to obtain optimized process parameter data; The marking process is optimized according to the optimized process parameter data.

[0030] It should be noted that by evaluating the link deviation of the marking link, the link deviation level is obtained, and then the optimized process parameters are obtained to achieve process optimization; The calculation formula of the link deviation evaluation index is: ; in, is the link deviation assessment index, is the water inlet speed deviation data, is the running time deviation data, is the energy consumption deviation data, is the water flow deviation data, , , , and It is the preset characteristic coefficient (which can be obtained by querying the preset wastewater treatment process control platform database).

[0031] According to an embodiment of the present invention, it also includes: Obtaining the effluent water quality evaluation index and effluent production data after process optimization, and using the effluent water quality evaluation index after process optimization as the optimized effluent water quality evaluation index; Inputting the optimized effluent water quality assessment index into a preset supply user matching database for matching identification to obtain recommended supply user data; Obtain wastewater usage demand data and effluent water quality index demand index for recommended supply users; Calculate the supply-demand matching evaluation index based on the effluent water quality evaluation index, the effluent water quality index demand index, the effluent production data and the wastewater usage demand data; The supply-demand matching evaluation index is compared with a preset supply-demand matching evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, a process optimization reminder is issued.

[0032] It should be noted that the purified wastewater is suitable for different recycling purposes according to different water quality indicators. If the recycled wastewater is used in a specific industrial process, such as electronic chip manufacturing, the water quality requirements are relatively high. For general industrial water, such as cooling water or washing water, the water quality requirements are relatively low. Therefore, the appropriate supply users are determined based on the optimized effluent water quality evaluation index. The supply-demand matching evaluation index is data used to reflect the matching relationship between the user's demand for purified wastewater and the supply of purified wastewater. If the supply-demand matching evaluation index does not meet the preset requirements, it means that the supply and demand are not matched, and the process parameters need to be optimized; The calculation formula of the supply-demand matching evaluation index is: ; in, is the supply-demand matching evaluation index, is the effluent water quality assessment index, is the effluent water quality index demand index, is the water output data, For wastewater usage demand data, and It is the preset characteristic coefficient (which can be obtained by querying the preset wastewater treatment process control platform database).

[0033] According to an embodiment of the present invention, it also includes: Conduct high-load tests on wastewater treatment equipment to obtain wastewater inflow data, equipment operating parameter deviation data, equipment energy consumption deviation data and their corresponding effluent water quality assessment index; Calculate the equipment stability evaluation index based on the optimized effluent water quality evaluation index and the wastewater inflow data, equipment operation parameter deviation data, equipment energy consumption deviation data and their corresponding effluent water quality evaluation index; The equipment stability evaluation index is compared with a preset equipment stability evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, a process optimization reminder is issued.

[0034] It should be noted that by conducting high-load tests on wastewater treatment equipment, under a large water inlet, the deviation of the equipment's operating parameters, equipment energy consumption deviation and effluent water quality evaluation index are observed. If the equipment stability evaluation index does not meet the preset requirements, it means that the process needs to be optimized. For example, the stirring intensity of the regulating tank can be increased to ensure that the wastewater is fully mixed, and the operating parameters of the sludge thickening tank, such as residence time and stirring intensity, can be adjusted to improve the sludge concentration efficiency. The aeration volume can be adjusted to ensure sufficient oxygen supply and maintain the activity of aerobic microorganisms. The operating pressure of the equipment can be reduced by adjusting multiple process links at the same time. The calculation formula of the equipment stability evaluation index is: ; in, is the equipment stability evaluation index, To optimize the effluent water quality assessment index, is the effluent water quality assessment index, is the wastewater inflow data, is the equipment energy consumption deviation data, , and It is the preset characteristic coefficient (which can be obtained by querying the preset wastewater treatment process control platform database).

[0035] The present invention also discloses an artificial intelligence-assisted wastewater treatment process optimization system, comprising a memory and a processor, wherein the memory stores an artificial intelligence-assisted wastewater treatment process optimization method program, and when the artificial intelligence-assisted wastewater treatment process optimization method program is executed by the processor, the following steps are implemented: Obtain wastewater composition data, input the wastewater composition data into the preset wastewater treatment process control platform database for matching and identification, and obtain the adapted process type data; Acquire wastewater recycling demand data, determine effluent water quality index demand data according to the adaptation process type data and the wastewater recycling demand data, and obtain adaptation process parameter data according to the effluent water quality index demand data; Treat wastewater according to the adapted process parameter data, obtain effluent water quality index data of different treatment links, process according to the effluent water quality index data, obtain effluent water quality assessment index, and determine the marking link; The operation deviation data of the marking link is obtained, link deviation level data is obtained according to the operation deviation data processing, optimized process parameter data is obtained according to the link deviation level data processing, and the process of the marking link is optimized according to the optimized process parameter data.

[0036] It should be noted that first, the adaptation process type is obtained according to the wastewater composition data, and the effluent water quality index requirements are determined according to the adaptation process type and the wastewater recycling requirements, and then the adaptation process parameter data is obtained, and then the effluent water quality of different treatment links is evaluated, and then the links with abnormal water quality are marked, and then the link deviation level of the marked link is determined, and then the optimized process parameter data is obtained, and the supply and demand matching evaluation and equipment stability evaluation are carried out. According to the supply and demand matching evaluation results and the equipment stability evaluation results, process optimization reminders are given, thereby achieving the purpose of intelligent optimization of the wastewater treatment process.

[0037] According to an embodiment of the present invention, the wastewater composition data is obtained, and the wastewater composition data is input into a preset wastewater treatment process management and control platform database for matching and identification to obtain the adaptation process type data, including: Obtain wastewater composition data, including organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data; The organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data are input into the preset wastewater treatment process flow control platform database for matching and identification to obtain the adapted process type data.

[0038] It should be noted that different wastewater components are suitable for different process types. For wastewater containing high concentrations of heavy metals, chemical precipitation, ion exchange and other processes may be needed to effectively remove heavy metal ions. For wastewater with high organic content, biological treatment processes such as activated sludge and biofilm methods may be more suitable, using the metabolism of microorganisms to decompose organic matter into harmless substances. For wastewater containing difficult-to-degrade organic matter, special processes such as advanced oxidation technology may be required to destroy the structure of organic matter and improve biodegradability. Therefore, it is necessary to determine the appropriate process type based on the wastewater composition.

[0039] According to an embodiment of the present invention, the step of obtaining wastewater recycling demand data, determining effluent water quality index demand data according to the adaptation process type data and the wastewater recycling demand data, and obtaining adaptation process parameter data according to the effluent water quality index demand data includes: Obtain wastewater recycling demand data, including turbidity demand data, heavy metal content demand data, salt content demand data, nutrient content demand data and microbial content demand data; Input the turbidity demand data, heavy metal content demand data, salt content demand data, nutrient content demand data, microbial content demand data and the adaptation process type data into a preset water quality index demand matching model for processing to obtain effluent water quality index demand data; The effluent water quality index demand data include chemical oxygen demand demand data, biochemical oxygen demand data, suspended solids content demand data, pH demand data, heavy metal content demand data and nitrogen and phosphorus content demand data; The organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data as well as the chemical oxygen demand data, biochemical oxygen demand data, suspended solids content demand data, pH demand data, heavy metal content demand data and nitrogen and phosphorus content demand data are input into a preset wastewater treatment process parameter prediction model for processing to obtain adapted process parameter data.

[0040] It should be noted that the effluent water quality index requirement data is determined according to the adaptation process type and the wastewater recycling demand, and the adapted process parameters are obtained by processing the effluent water quality index requirement data. The preset water quality index requirement matching model is a model obtained by training with turbidity requirement data, heavy metal content requirement data, salt content requirement data, nutrient content requirement data and microbial content requirement data of a large number of historical samples as well as the adapted process type data and effluent water quality index requirement data. The corresponding output effluent water quality index requirement data can be obtained by inputting relevant information for processing. The preset wastewater treatment process parameter prediction model is a model obtained by training with organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data of a large number of historical samples as well as the chemical oxygen demand data, biochemical oxygen demand data, suspended solids content demand data, pH demand data, heavy metal content demand data, nitrogen and phosphorus content demand data and adapted process parameter data. The corresponding output adapted process parameter data can be obtained by inputting relevant information for processing.

[0041] According to an embodiment of the present invention, the wastewater treatment is performed according to the adapted process parameter data, effluent water quality index data of different treatment links are obtained, the effluent water quality index data is processed according to the effluent water quality index data, the effluent water quality evaluation index is obtained, and the marking link is determined, including: Perform wastewater treatment according to the adapted process parameter data to obtain effluent water quality index data of different treatment links, including chemical oxygen demand data, biochemical oxygen demand data, suspended solids content data, pH data, heavy metal content data, and nitrogen and phosphorus content data; The chemical oxygen demand data, biochemical oxygen demand data, suspended solids content data, pH data, heavy metal content data and nitrogen and phosphorus content data of different treatment links are calculated respectively to obtain the effluent water quality assessment index corresponding to different treatment links; The effluent water quality assessment index of different processing links is compared with the preset effluent water quality assessment index threshold of the corresponding link, and the processing link whose threshold comparison result does not meet the requirements of the preset threshold comparison result is marked as a link.

[0042] It should be noted that the chemical oxygen demand data is an indicator used to measure the content of organic matter in the wastewater after treatment, and the biochemical oxygen demand data is an indicator used to measure the content of microorganisms in the wastewater after treatment. The effluent quality of different treatment links is tested to obtain the effluent quality assessment index corresponding to different treatment links, and the abnormal links are determined and marked according to the effluent quality assessment index; The calculation formula of the effluent water quality assessment index is: ; in, is the effluent water quality assessment index, is the chemical oxygen demand data, For biochemical oxygen demand data, is the suspended matter content data, is the pH data, is the heavy metal content data, is the nitrogen and phosphorus content data, , , , , and It is the preset characteristic coefficient (which can be obtained by querying the preset wastewater treatment process control platform database).

[0043] According to an embodiment of the present invention, the step of obtaining the operation deviation data of the marking link, obtaining link deviation level data according to the operation deviation data processing, obtaining optimized process parameter data according to the link deviation level data processing, and performing process optimization on the marking link according to the optimized process parameter data includes: Obtaining the operation deviation data of the marking link, including water inlet speed deviation data, operation time deviation data, energy consumption deviation data and water outlet flow deviation data; Calculate the link deviation evaluation index based on the water inlet speed deviation data, the operating time deviation data, the energy consumption deviation data and the water outlet flow deviation data; The link deviation evaluation index is compared with a preset link deviation evaluation index threshold, and if the threshold comparison result does not meet the preset threshold comparison result requirement, the link deviation level data is determined according to the range level to which the threshold comparison result belongs; The link deviation level data is input into the preset wastewater treatment process control platform database for matching and identification to obtain optimized process parameter data; The marking process is optimized according to the optimized process parameter data.

[0044] It should be noted that by evaluating the link deviation of the marking link, the link deviation level is obtained, and then the optimized process parameters are obtained to achieve process optimization; The calculation formula of the link deviation evaluation index is: ; in, is the link deviation assessment index, is the water inlet speed deviation data, is the running time deviation data, is the energy consumption deviation data, is the water flow deviation data, , , , and It is the preset characteristic coefficient (which can be obtained by querying the preset wastewater treatment process control platform database).

[0045] According to an embodiment of the present invention, it also includes: Obtaining the effluent water quality evaluation index and effluent production data after process optimization, and using the effluent water quality evaluation index after process optimization as the optimized effluent water quality evaluation index; Inputting the optimized effluent water quality assessment index into a preset supply user matching database for matching identification to obtain recommended supply user data; Obtain wastewater usage demand data and effluent water quality index demand index for recommended supply users; Calculate the supply-demand matching evaluation index based on the effluent water quality evaluation index, the effluent water quality index demand index, the effluent production data and the wastewater usage demand data; The supply-demand matching evaluation index is compared with a preset supply-demand matching evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, a process optimization reminder is issued.

[0046] It should be noted that the purified wastewater is suitable for different recycling purposes according to different water quality indicators. If the recycled wastewater is used in a specific industrial process, such as electronic chip manufacturing, the water quality requirements are relatively high. For general industrial water, such as cooling water or washing water, the water quality requirements are relatively low. Therefore, the appropriate supply users are determined based on the optimized effluent water quality evaluation index. The supply-demand matching evaluation index is data used to reflect the matching relationship between the user's demand for purified wastewater and the supply of purified wastewater. If the supply-demand matching evaluation index does not meet the preset requirements, it means that the supply and demand are not matched, and the process parameters need to be optimized; The calculation formula of the supply-demand matching evaluation index is: ; in, is the supply-demand matching evaluation index, is the effluent water quality assessment index, is the effluent water quality index demand index, is the water output data, For wastewater usage demand data, and It is the preset characteristic coefficient (which can be obtained by querying the preset wastewater treatment process control platform database).

[0047] According to an embodiment of the present invention, it also includes: Conduct high-load tests on wastewater treatment equipment to obtain wastewater inflow data, equipment operating parameter deviation data, equipment energy consumption deviation data and their corresponding effluent water quality assessment index; Calculate the equipment stability evaluation index based on the optimized effluent water quality evaluation index and the wastewater inflow data, equipment operation parameter deviation data, equipment energy consumption deviation data and their corresponding effluent water quality evaluation index; The equipment stability evaluation index is compared with a preset equipment stability evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, a process optimization reminder is issued.

[0048] It should be noted that by conducting high-load tests on wastewater treatment equipment, under a large water inlet, the deviation of the equipment's operating parameters, equipment energy consumption deviation and effluent water quality evaluation index are observed. If the equipment stability evaluation index does not meet the preset requirements, it means that the process needs to be optimized. For example, the stirring intensity of the regulating tank can be increased to ensure that the wastewater is fully mixed, and the operating parameters of the sludge thickening tank, such as residence time and stirring intensity, can be adjusted to improve the sludge concentration efficiency. The aeration volume can be adjusted to ensure sufficient oxygen supply and maintain the activity of aerobic microorganisms. The operating pressure of the equipment can be reduced by adjusting multiple process links at the same time. The calculation formula of the equipment stability evaluation index is: ; in, is the equipment stability evaluation index, To optimize the effluent water quality assessment index, is the effluent water quality assessment index, is the wastewater inflow data, is the equipment energy consumption deviation data, , and It is the preset characteristic coefficient (which can be obtained by querying the preset wastewater treatment process control platform database).

[0049] The third aspect of the present invention provides a readable storage medium, which stores an artificial intelligence-assisted wastewater treatment process optimization method program. When the artificial intelligence-assisted wastewater treatment process optimization method program is executed by a processor, the steps of the artificial intelligence-assisted wastewater treatment process optimization method as described in any one of the above items are implemented.

[0050] The present invention discloses an artificial intelligence-assisted wastewater treatment process optimization method, system and medium, which first obtain an adaptation process type according to wastewater composition data, determine the effluent water quality index requirements according to the adaptation process type and wastewater recovery requirements, and then obtain adaptation process parameter data, and then evaluate the effluent water quality of different treatment links, and then mark the links with abnormal water quality, and then determine the link deviation level of the marked links, and then obtain optimized process parameter data, and perform supply and demand matching evaluation and equipment stability evaluation, and perform process optimization reminders according to the supply and demand matching evaluation results and the equipment stability evaluation results, thereby achieving the purpose of intelligent optimization of the wastewater treatment process.

[0051] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0052] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0053] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0054] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0055] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. An artificial intelligence-assisted wastewater treatment process optimization method, characterized in that: The following steps are involved: Obtain wastewater composition data, input the wastewater composition data into the preset wastewater treatment process control platform database for matching and identification, and obtain the adapted process type data; Acquire wastewater recycling demand data, determine effluent water quality index demand data according to the adaptation process type data and the wastewater recycling demand data, and obtain adaptation process parameter data according to the effluent water quality index demand data; Treat wastewater according to the adapted process parameter data, obtain effluent water quality index data of different treatment links, process according to the effluent water quality index data, obtain effluent water quality assessment index, and determine the marking link; The operation deviation data of the marking link is obtained, link deviation level data is obtained according to the operation deviation data processing, optimized process parameter data is obtained according to the link deviation level data processing, and the process of the marking link is optimized according to the optimized process parameter data.

2. The artificial intelligence-assisted wastewater treatment process optimization method according to claim 1 is characterized in that: The wastewater composition data is obtained, and the wastewater composition data is input into a preset wastewater treatment process control platform database for matching and identification to obtain the adapted process type data, including: Obtain wastewater composition data, including organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data; The organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data are input into the preset wastewater treatment process flow control platform database for matching and identification to obtain the adapted process type data.

3. The artificial intelligence-assisted wastewater treatment process optimization method according to claim 2 is characterized in that: The acquiring of wastewater recycling demand data, determining effluent water quality index demand data according to the adaptation process type data and the wastewater recycling demand data, and obtaining adaptation process parameter data according to the effluent water quality index demand data, includes: Obtain wastewater recycling demand data, including turbidity demand data, heavy metal content demand data, salt content demand data, nutrient content demand data and microbial content demand data; Input the turbidity demand data, heavy metal content demand data, salt content demand data, nutrient content demand data, microbial content demand data and the adaptation process type data into a preset water quality index demand matching model for processing to obtain effluent water quality index demand data; The effluent water quality index demand data include chemical oxygen demand demand data, biochemical oxygen demand data, suspended solids content demand data, pH demand data, heavy metal content demand data and nitrogen and phosphorus content demand data; The organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data as well as the chemical oxygen demand data, biochemical oxygen demand data, suspended solids content demand data, pH demand data, heavy metal content demand data and nitrogen and phosphorus content demand data are input into a preset wastewater treatment process parameter prediction model for processing to obtain adapted process parameter data.

4. The artificial intelligence-assisted wastewater treatment process optimization method according to claim 3 is characterized in that: The wastewater treatment is performed according to the adapted process parameter data, the effluent water quality index data of different treatment links are obtained, the effluent water quality index data is processed according to the effluent water quality index data, the effluent water quality evaluation index is obtained, and the marking link is determined, including: Perform wastewater treatment according to the adapted process parameter data to obtain effluent water quality index data of different treatment links, including chemical oxygen demand data, biochemical oxygen demand data, suspended solids content data, pH data, heavy metal content data, and nitrogen and phosphorus content data; The chemical oxygen demand data, biochemical oxygen demand data, suspended solids content data, pH data, heavy metal content data and nitrogen and phosphorus content data of different treatment links are calculated respectively to obtain the effluent water quality assessment index corresponding to different treatment links; The effluent water quality assessment index of different processing links is compared with the preset effluent water quality assessment index threshold of the corresponding link, and the processing link whose threshold comparison result does not meet the requirements of the preset threshold comparison result is marked as a link.

5. The artificial intelligence-assisted wastewater treatment process optimization method according to claim 4 is characterized in that: The step of obtaining the operation deviation data of the marking link, obtaining link deviation level data according to the operation deviation data, obtaining optimized process parameter data according to the link deviation level data, and optimizing the process of the marking link according to the optimized process parameter data includes: Obtaining the operation deviation data of the marking link, including water inlet speed deviation data, operation time deviation data, energy consumption deviation data and water outlet flow deviation data; Calculate the link deviation evaluation index based on the water inlet speed deviation data, the operating time deviation data, the energy consumption deviation data and the water outlet flow deviation data; The link deviation evaluation index is compared with a preset link deviation evaluation index threshold, and if the threshold comparison result does not meet the preset threshold comparison result requirement, the link deviation level data is determined according to the range level to which the threshold comparison result belongs; The link deviation level data is input into the preset wastewater treatment process control platform database for matching and identification to obtain optimized process parameter data; The marking process is optimized according to the optimized process parameter data.

6. The artificial intelligence-assisted wastewater treatment process optimization method according to claim 5, characterized in that: Also includes: Obtaining the effluent water quality evaluation index and effluent production data after process optimization, and using the effluent water quality evaluation index after process optimization as the optimized effluent water quality evaluation index; Inputting the optimized effluent water quality assessment index into a preset supply user matching database for matching identification to obtain recommended supply user data; Obtain wastewater usage demand data and effluent water quality index demand index for recommended supply users; Calculate the supply-demand matching evaluation index based on the effluent water quality evaluation index, the effluent water quality index demand index, the effluent production data and the wastewater usage demand data; The supply-demand matching evaluation index is compared with a preset supply-demand matching evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, a process optimization reminder is issued.

7. The artificial intelligence-assisted wastewater treatment process optimization method according to claim 6, characterized in that: Also includes: Conduct high-load tests on wastewater treatment equipment to obtain wastewater inflow data, equipment operating parameter deviation data, equipment energy consumption deviation data and their corresponding effluent water quality assessment index; Calculate the equipment stability evaluation index based on the optimized effluent water quality evaluation index and the wastewater inflow data, equipment operation parameter deviation data, equipment energy consumption deviation data and their corresponding effluent water quality evaluation index; The equipment stability evaluation index is compared with a preset equipment stability evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, a process optimization reminder is issued.

8. An artificial intelligence-assisted wastewater treatment process optimization system, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a program of an artificial intelligence-assisted wastewater treatment process optimization method, and when the artificial intelligence-assisted wastewater treatment process optimization method program is executed by the processor, the following steps are implemented: Obtain wastewater composition data, input the wastewater composition data into the preset wastewater treatment process control platform database for matching and identification, and obtain the adapted process type data; Acquire wastewater recycling demand data, determine effluent water quality index demand data according to the adaptation process type data and the wastewater recycling demand data, and obtain adaptation process parameter data according to the effluent water quality index demand data; Treat wastewater according to the adapted process parameter data, obtain effluent water quality index data of different treatment links, process according to the effluent water quality index data, obtain effluent water quality assessment index, and determine the marking link; The operation deviation data of the marking link is obtained, link deviation level data is obtained according to the operation deviation data processing, optimized process parameter data is obtained according to the link deviation level data processing, and the process of the marking link is optimized according to the optimized process parameter data.

9. The artificial intelligence-assisted wastewater treatment process optimization system according to claim 8, characterized in that: The wastewater composition data is obtained, and the wastewater composition data is input into a preset wastewater treatment process control platform database for matching and identification to obtain the adapted process type data, including: Obtain wastewater composition data, including organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data; The organic matter concentration data, nitrogen and phosphorus content data, heavy metal content data and microbial content data are input into the preset wastewater treatment process flow control platform database for matching and identification to obtain the adapted process type data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an artificial intelligence-assisted wastewater treatment process optimization program. When the artificial intelligence-assisted wastewater treatment process optimization program is executed by the processor, the steps of the artificial intelligence-assisted wastewater treatment process optimization method as described in any one of claims 1 to 7 are implemented.

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