Wastewater pollutant intelligent classification method and system based on deep learning, and medium

Abstract

The invention provides a wastewater pollutant intelligent classification method and system based on deep learning and a medium. The method comprises the following steps: acquiring physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater, and processing according to the physical characteristic data and the flow characteristic data to obtain wastewater pretreatment difficulty grade data, processing according to the microbial community characteristic data and the microbial activity characteristic data to obtain a biochemical treatment comprehensive category evaluation index, processing according to the chemical characteristic data to obtain a chemical treatment classification evaluation index, and finally matching with a preset wastewater treatment mode database to obtain a treatment scheme; through treatment of wastewater pretreatment difficulty grade data, a biochemical treatment comprehensive category evaluation index and a chemical treatment classification evaluation index, and in combination with preset wastewater treatment mode database matching, an intelligent wastewater pollutant classification technology is realized.

Description

Technical Field

[0001] The present application relates to the field of intelligent classification of wastewater pollutants, and specifically, to a method, system and medium for intelligent classification of wastewater pollutants based on deep learning. Background Art

[0002] With the rapid development of industrialization and urbanization, the types and quantities of wastewater discharge are increasing. Traditional methods for detecting and classifying wastewater pollutants are often inefficient, inaccurate, and rely on manual operations and chemical analysis, which cannot meet the real-time and intelligent requirements of modern wastewater treatment. With the rapid development of intelligent technology, its application potential in the field of wastewater treatment has not yet been fully explored. An intelligent classification method is needed that can accurately and quickly classify wastewater and match treatment results.

[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 a method, system and medium for intelligent classification of wastewater pollutants based on deep learning. The method can obtain physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of the wastewater, obtain wastewater pretreatment difficulty level data according to the physical characteristic data and flow characteristic data processing, obtain biochemical treatment comprehensive category evaluation index according to the microbial community characteristic data and microbial activity characteristic data processing, and then obtain chemical treatment classification evaluation index according to the chemical characteristic data processing, and finally obtain a treatment plan after matching with a preset wastewater treatment method database; thereby, through the processing of wastewater pretreatment difficulty level data, biochemical treatment comprehensive category evaluation index and chemical treatment classification evaluation index, combined with matching with a preset wastewater treatment method database, a technology for intelligent classification of wastewater pollutants is realized.

[0005] This application also provides a wastewater pollutant intelligent classification method based on deep learning, comprising the following steps: Obtaining physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater through preset sensors; Obtaining a pretreatment difficulty index of the wastewater in the pretreatment stage according to the physical characteristic data and the flow characteristic data, comparing the pretreatment difficulty index with a preset treatment difficulty index threshold, and obtaining wastewater pretreatment difficulty level data; Obtaining a comprehensive category evaluation index of biochemical treatment in the wastewater biochemical treatment stage according to the microbial community characteristic data and the microbial activity characteristic data; Obtaining a chemical treatment classification evaluation index for the wastewater deep treatment stage according to the chemical characteristic data processing; According to the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index, the corresponding treatment plans for the pretreatment stage, biochemical treatment stage and deep treatment stage are obtained by matching them with the preset wastewater treatment method database.

[0006] Optionally, in the deep learning-based intelligent classification method for wastewater pollutants described in the present application, the physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of the wastewater obtained by the preset sensor specifically include: Obtaining physical characteristic data and flow characteristic data of the wastewater, the physical characteristic data including turbidity data and density data, and the flow characteristic data including flow data, flow velocity data and pressure data; Obtaining the microbial community characteristic data and microbial activity characteristic data of the wastewater. The microbial community characteristic data include the number of microbial species and microbial unit content data. The microbial activity characteristic data include respiration rate data and biomass growth rate data. Obtain chemical characteristics data of wastewater, including pH data, chemical oxygen demand data, and biochemical oxygen demand data.

[0007] Optionally, in the method for intelligent classification of wastewater pollutants based on deep learning described in the present application, the pretreatment difficulty index of wastewater in the pretreatment stage is obtained according to the physical characteristic data and the flow characteristic data, and the pretreatment difficulty index is compared with a preset treatment difficulty index threshold to obtain wastewater pretreatment difficulty level data, specifically including: Inputting the turbidity data, density data, flow data, flow velocity data and pressure data into a preset wastewater pretreatment difficulty assessment model for processing to obtain a pretreatment difficulty index; Comparing the pretreatment difficulty index with a preset treatment difficulty index threshold to obtain wastewater pretreatment difficulty level data; Extracting a first threshold and a second threshold according to a preset processing difficulty index threshold, wherein the first threshold is greater than the second threshold; comparing the preprocessing difficulty index to the first threshold and a second threshold; If the pretreatment difficulty index is greater than the first threshold, the wastewater pretreatment difficulty level data is high difficulty data; If the pretreatment difficulty index is greater than the second threshold and less than or equal to the first threshold, the wastewater pretreatment difficulty level data is medium difficulty data; If the pretreatment difficulty index is less than or equal to the second threshold, the wastewater pretreatment difficulty level data is basic difficulty data.

[0008] Optionally, in the intelligent classification method for wastewater pollutants based on deep learning described in the present application, the biochemical treatment comprehensive category evaluation index of the wastewater biochemical treatment stage is obtained according to the processing of the microbial community characteristic data and the microbial activity characteristic data, specifically including: According to the microbial species quantity and microbial unit content data combined with the respiration rate data and the biomass growth rate data, a preset wastewater biochemical treatment comprehensive evaluation model is used to obtain a biochemical treatment comprehensive category evaluation index; The calculation formula of the biochemical treatment comprehensive category evaluation index in the wastewater biochemical treatment comprehensive category evaluation model is: ; in, It is the comprehensive category evaluation index of biochemical treatment. , , , They are the number of microbial species, microbial unit content data, respiration rate data, and biomass growth rate data. , , is the preset characteristic coefficient.

[0009] Optionally, in the intelligent classification method for wastewater pollutants based on deep learning described in the present application, the chemical treatment classification evaluation index of the wastewater deep treatment stage is obtained according to the chemical characteristic data processing, specifically including: Extracting a preset number of sampled chemical characteristic data of wastewater at different locations according to the chemical characteristic data, the sampled chemical characteristic data including sampled pH data, sampled chemical oxygen demand data and sampled biochemical oxygen demand data; Calculating the average value of the preset number of sampled pH data, sampled chemical oxygen demand data and sampled biochemical oxygen demand data to obtain effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data; The effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data are input into a preset chemical treatment classification evaluation model for processing to obtain a chemical treatment classification evaluation index.

[0010] Optionally, the deep learning-based intelligent classification method for wastewater pollutants described in the present application further includes: The wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index are calculated and input into a preset wastewater treatment comprehensive difficulty evaluation model for processing to obtain a wastewater treatment comprehensive difficulty index; The calculation formula of the comprehensive difficulty index of wastewater treatment in the comprehensive difficulty assessment model of wastewater treatment is: ; in, is the comprehensive difficulty index of wastewater treatment, It is the comprehensive category evaluation index of biochemical treatment. The difficulty level data of wastewater pretreatment is For chemical treatment classification assessment index, , , is the preset characteristic coefficient.

[0011] Optionally, in the wastewater pollutant intelligent classification method based on deep learning described in the present application, after obtaining the comprehensive difficulty index of wastewater treatment, it also includes: Comparing the wastewater treatment comprehensive difficulty index with a preset wastewater treatment comprehensive difficulty index threshold to obtain a wastewater treatment complexity level; Extracting a first difficulty threshold and a second difficulty threshold according to the wastewater treatment comprehensive difficulty index threshold, and the first difficulty threshold is greater than the second difficulty threshold; Comparing the wastewater treatment comprehensive difficulty index with the first difficulty threshold and the second difficulty threshold; If the comprehensive difficulty index of wastewater treatment is greater than the first difficulty threshold, the wastewater treatment complexity level is a complex level; If the comprehensive difficulty index of wastewater treatment is greater than the second difficulty threshold and less than or equal to the first difficulty threshold, the wastewater treatment complexity level is a general level; If the comprehensive difficulty index of wastewater treatment is less than or equal to the second difficulty threshold, the wastewater treatment complexity level is a simple level; The wastewater treatment complexity level is matched with a preset wastewater treatment estimated cost database to obtain a wastewater treatment estimated cost level, including low cost, medium cost or high cost.

[0012] In a second aspect, the present application provides a wastewater pollutant intelligent classification system based on deep learning, the system comprising: a memory and a processor, the memory storing a program of a wastewater pollutant intelligent classification method based on deep learning, and the program of the wastewater pollutant intelligent classification method based on deep learning is executed by the processor to implement the following steps: Obtaining physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater through preset sensors; Obtaining a pretreatment difficulty index of the wastewater in the pretreatment stage according to the physical characteristic data and the flow characteristic data, comparing the pretreatment difficulty index with a preset treatment difficulty index threshold, and obtaining wastewater pretreatment difficulty level data; Obtaining a comprehensive category evaluation index of biochemical treatment in the wastewater biochemical treatment stage according to the microbial community characteristic data and the microbial activity characteristic data; Obtaining a chemical treatment classification evaluation index for the wastewater deep treatment stage according to the chemical characteristic data processing; According to the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index, the corresponding treatment plans for the pretreatment stage, biochemical treatment stage and deep treatment stage are obtained by matching them with the preset wastewater treatment method database.

[0013] Optionally, in the wastewater pollutant intelligent classification system based on deep learning described in the present application, the physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of the wastewater obtained by the preset sensor specifically include: Obtaining physical characteristic data and flow characteristic data of the wastewater, the physical characteristic data including turbidity data and density data, and the flow characteristic data including flow data, flow velocity data and pressure data; Obtaining the microbial community characteristic data and microbial activity characteristic data of the wastewater. The microbial community characteristic data include the number of microbial species and microbial unit content data. The microbial activity characteristic data include respiration rate data and biomass growth rate data. Obtain chemical characteristics data of wastewater, including pH data, chemical oxygen demand data, and biochemical oxygen demand data.

[0014] In the third aspect, the present application also provides a readable storage medium, which stores a program for an intelligent classification method for wastewater pollutants based on deep learning. When the program for an intelligent classification method for wastewater pollutants based on deep learning is executed by a processor, the steps of the intelligent classification method for wastewater pollutants based on deep learning as described in any one of the above items are implemented.

[0015] As can be seen from the above, the method, system and medium for intelligent classification of wastewater pollutants based on deep learning provided by the present application. The method obtains the physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of the wastewater, obtains the wastewater pretreatment difficulty level data according to the physical characteristic data and flow characteristic data processing, obtains the biochemical treatment comprehensive category evaluation index according to the microbial community characteristic data and microbial activity characteristic data processing, and then obtains the chemical treatment classification evaluation index according to the chemical characteristic data processing, and finally obtains the treatment plan after matching with the preset wastewater treatment method database; thus, the technology of intelligent classification of wastewater pollutants is realized by processing the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index, combined with the preset wastewater treatment method database matching.

[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 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 1 A flowchart of a method for intelligent classification of wastewater pollutants based on deep learning provided in an embodiment of the present application; Figure 2 A flow chart of obtaining physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater in the intelligent classification method of wastewater pollutants based on deep learning provided in an embodiment of the present application; Figure 3 A flowchart of obtaining wastewater pretreatment difficulty level data for the deep learning-based intelligent classification method for wastewater pollutants 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 1The flowchart of the wastewater pollutant intelligent classification method based on deep learning in some embodiments of the present application. The wastewater pollutant intelligent classification method based on deep learning is used in terminal devices, such as computers, mobile phones, etc. The wastewater pollutant intelligent classification method based on deep learning includes the following steps: S11, obtaining physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater through preset sensors; S12, obtaining a pretreatment difficulty index of the wastewater in the pretreatment stage according to the physical characteristic data and the flow characteristic data, comparing the pretreatment difficulty index with a preset treatment difficulty index threshold, and obtaining wastewater pretreatment difficulty level data; S13, obtaining a comprehensive category evaluation index of biochemical treatment in the wastewater biochemical treatment stage according to the microbial community characteristic data and the microbial activity characteristic data; S14, obtaining a chemical treatment classification evaluation index for the wastewater deep treatment stage according to the chemical characteristic data processing; S15. According to the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index, the wastewater pretreatment difficulty level data are matched with the preset wastewater treatment method database to obtain the corresponding treatment solutions for the pretreatment stage, biochemical treatment stage and deep treatment stage.

[0022] It should be noted that wastewater treatment mainly includes three stages, including pretreatment stage, biochemical treatment stage and deep treatment stage. In the above three stages, pollutants are classified respectively, so that wastewater can be treated more specifically. In the pretreatment stage, the pretreatment difficulty index is calculated and then the threshold is compared to obtain wastewater pretreatment difficulty level data; in the biochemical treatment stage, the biochemical treatment comprehensive category evaluation index is calculated; in the deep treatment stage, the chemical treatment classification evaluation index is calculated; the wastewater pretreatment difficulty level data, biochemical treatment comprehensive category evaluation index, and chemical treatment classification evaluation index corresponding to the three treatment stages are matched with the preset wastewater treatment method database to obtain the treatment plans corresponding to the three stages; the preset wastewater treatment method database is a database obtained by training a large amount of physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data, and chemical characteristic data and treatment plan. The database contains the treatment plans corresponding to the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index, and the chemical treatment classification evaluation index. The treatment plan also includes the corresponding treatment time, added reaction substances, and treatment mode. For example, in the pretreatment stage, The basic difficulty data corresponds to a short processing time and a fine screen is selected; the wastewater treatment method database can be updated and adjusted according to the actual situation of the wastewater treatment station.

[0023] Please refer to Figure 2 , Figure 2 The present invention provides a flowchart of obtaining physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of the intelligent classification method of wastewater pollutants based on deep learning. According to an embodiment of the present invention, the physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater obtained by preset sensors specifically include: S21. Obtaining physical characteristic data and flow characteristic data of the wastewater, wherein the physical characteristic data includes turbidity data and density data, and the flow characteristic data includes flow data, flow velocity data and pressure data; S22. Obtaining microbial community characteristic data and microbial activity characteristic data of the wastewater, wherein the microbial community characteristic data includes the number of microbial species and microbial unit content data, and the microbial activity characteristic data includes respiration rate data and biomass growth rate data; S23. Obtain chemical characteristic data of wastewater, including pH data, chemical oxygen demand data and biochemical oxygen demand data.

[0024] It should be noted that flow data and flow velocity data refer to the data of wastewater from different sources flowing into the wastewater treatment station, and pressure data refers to the outlet pressure data of the pretreatment stage of the wastewater treatment station. Different microorganisms have different degradation abilities for different pollutants. The more microbial species there are, the more positive correlation there will be in the wastewater's own purification ability. Similarly, microbial unit content data refers to the data on the total content of microorganisms per unit volume. Respiration rate data refers to the respiration rate of microorganisms, which reflects the metabolic intensity of microorganisms to a certain extent. A fast respiration rate means that the wastewater is rich in easily degradable organic matter and the microorganisms are growing actively. The biomass growth rate data reflects the increase in the number of microorganisms within a preset time.

[0025] Please refer to Figure 3 , Figure 3 It is a flowchart of obtaining wastewater pretreatment difficulty level data of the wastewater pollutant intelligent classification method based on deep learning provided by the embodiment of the present application. According to the embodiment of the present invention, the pretreatment difficulty index of the wastewater in the pretreatment stage is obtained according to the physical characteristic data and the flow characteristic data, and the pretreatment difficulty index is compared with the preset treatment difficulty index threshold to obtain the wastewater pretreatment difficulty level data, specifically including: S31, inputting the turbidity data, density data, flow data, flow velocity data and pressure data into a preset wastewater pretreatment difficulty assessment model for processing to obtain a pretreatment difficulty index; S32, comparing the pretreatment difficulty index with a preset treatment difficulty index threshold to obtain wastewater pretreatment difficulty level data; S33, extracting a first threshold and a second threshold according to a preset processing difficulty index threshold, and the first threshold is greater than the second threshold; S34, comparing the preprocessing difficulty index with the first threshold and the second threshold; S35. If the pretreatment difficulty index is greater than the first threshold, the wastewater pretreatment difficulty level data is high difficulty data; S36: If the pretreatment difficulty index is greater than the second threshold and less than or equal to the first threshold, the wastewater pretreatment difficulty level data is medium difficulty data; S37. If the pretreatment difficulty index is less than or equal to the second threshold, the wastewater pretreatment difficulty level data is the basic difficulty data.

[0026] It should be noted that in the pretreatment stage, the pretreatment difficulty data is calculated based on the turbidity data, density data, flow data, flow velocity data and pressure data. The pretreatment difficulty data refers to data that can reflect the difficulty of treating wastewater in the pretreatment stage to a certain extent. The larger the data, the more difficult it is to treat. The calculation formula of the pretreatment difficulty index in the wastewater pretreatment difficulty assessment model is: ; in, is the preprocessing difficulty index, , , , , They are turbidity data, density data, flow data, flow velocity data, and pressure data. , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset wastewater pollutant intelligent classification platform based on deep learning); In this embodiment, the processing difficulty index threshold is set to: (0, 0.45], which is basic difficulty data; (0.45, 0.75], which is medium difficulty data; (0.75, 1], which is high difficulty data; when the pretreatment difficulty index is 0.9, the wastewater pretreatment difficulty level data is high difficulty data.

[0027] According to an embodiment of the present invention, the processing of the microbial community characteristic data and the microbial activity characteristic data to obtain a comprehensive category evaluation index of the biochemical treatment stage of the wastewater includes: According to the microbial species quantity and microbial unit content data combined with the respiration rate data and the biomass growth rate data, a preset wastewater biochemical treatment comprehensive evaluation model is used to obtain a biochemical treatment comprehensive category evaluation index; The calculation formula of the biochemical treatment comprehensive category evaluation index in the wastewater biochemical treatment comprehensive category evaluation model is: ; in, It is the comprehensive category evaluation index of biochemical treatment. , , , They are the number of microbial species, microbial unit content data, respiration rate data, and biomass growth rate data. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset wastewater pollutant intelligent classification platform based on deep learning).

[0028] It should be noted that the growth and production of microorganisms in wastewater are closely related to pollutants. The more types of microorganisms in wastewater, the more helpful it is to improve the stability of the sewage treatment system; the greater the unit content of microorganisms, the stronger the ability of microorganisms to treat pollutants in wastewater; the biochemical treatment comprehensive category evaluation index is obtained through processing based on the number of microbial species, microbial unit content data, respiration rate data and biomass growth rate data. The biochemical treatment comprehensive category evaluation index refers to the data basis for classifying the difficulty of wastewater pollutant treatment according to the pollutant situation in the biochemical treatment stage. Different biochemical treatment comprehensive category evaluation indexes represent different wastewater quality, which corresponds to different pollutant classifications and different treatment methods.

[0029] According to an embodiment of the present invention, obtaining a chemical treatment classification evaluation index for the wastewater deep treatment stage according to the chemical characteristic data processing specifically includes: Extracting a preset number of sampled chemical characteristic data of wastewater at different locations according to the chemical characteristic data, the sampled chemical characteristic data including sampled pH data, sampled chemical oxygen demand data and sampled biochemical oxygen demand data; Calculating the average value of the preset number of sampled pH data, sampled chemical oxygen demand data and sampled biochemical oxygen demand data to obtain effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data; The effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data are input into a preset chemical treatment classification evaluation model for processing to obtain a chemical treatment classification evaluation index.

[0030] It should be noted that, due to the fluidity of wastewater, wastewater at different locations may have different chemical characteristics. Therefore, in order to better obtain the chemical characteristic data of wastewater, a preset amount of chemical characteristic data of wastewater is collected at different locations to obtain sampled chemical characteristic data. The effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data are obtained by taking the average value of the sampled pH data, sampled chemical oxygen demand data and sampled biochemical oxygen demand data, and then the chemical treatment classification evaluation index is obtained according to the effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data. The calculation formula of the chemical treatment classification evaluation index in the chemical treatment classification evaluation model is: ; in, For chemical treatment classification assessment index, , , They are effective pH data, effective chemical oxygen demand data, and effective biochemical oxygen demand data. , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset wastewater pollutant intelligent classification platform based on deep learning); The chemical treatment classification evaluation index reflects the chemical pollution situation of wastewater to a certain extent, and classification treatment is carried out according to the pollution situation; the preset chemical treatment classification evaluation model is a model obtained by training a large amount of historical data, through which the chemical treatment classification evaluation index can be calculated.

[0031] According to an embodiment of the present invention, it also includes: The wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index are calculated and input into a preset wastewater treatment comprehensive difficulty evaluation model for processing to obtain a wastewater treatment comprehensive difficulty index; The calculation formula of the comprehensive difficulty index of wastewater treatment in the comprehensive difficulty assessment model of wastewater treatment is: ; in, is the comprehensive difficulty index of wastewater treatment, It is the comprehensive category evaluation index of biochemical treatment. The difficulty level data of wastewater pretreatment is For chemical treatment classification assessment index, , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset wastewater pollutant intelligent classification platform based on deep learning).

[0032] It should be noted that after obtaining the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category assessment index and the chemical treatment classification assessment index, in order to have a more comprehensive assessment of the wastewater, the comprehensive wastewater treatment difficulty index is obtained. The comprehensive wastewater treatment difficulty index reflects the difficulty of treating the wastewater to a certain extent. The larger the index, the more complicated the treatment. The comprehensive wastewater treatment difficulty assessment model is obtained through training with a large amount of historical data, and the comprehensive wastewater treatment difficulty index can be calculated through this model.

[0033] According to an embodiment of the present invention, after obtaining the comprehensive difficulty index of wastewater treatment, the method further includes: Comparing the wastewater treatment comprehensive difficulty index with a preset wastewater treatment comprehensive difficulty index threshold to obtain a wastewater treatment complexity level; Extracting a first difficulty threshold and a second difficulty threshold according to the wastewater treatment comprehensive difficulty index threshold, and the first difficulty threshold is greater than the second difficulty threshold; Comparing the wastewater treatment comprehensive difficulty index with the first difficulty threshold and the second difficulty threshold; If the comprehensive difficulty index of wastewater treatment is greater than the first difficulty threshold, the wastewater treatment complexity level is a complex level; If the comprehensive difficulty index of wastewater treatment is greater than the second difficulty threshold and less than or equal to the first difficulty threshold, the wastewater treatment complexity level is a general level; If the comprehensive difficulty index of wastewater treatment is less than or equal to the second difficulty threshold, the wastewater treatment complexity level is a simple level; The wastewater treatment complexity level is matched with a preset wastewater treatment estimated cost database to obtain a wastewater treatment estimated cost level, including low cost, medium cost or high cost.

[0034] It should be noted that different treatment measures will be taken according to the degree of wastewater pollutants during the treatment process, and different measures will have different treatment costs, which is manifested as the higher the degree of wastewater pollution, the higher the treatment cost; in order to better estimate the treatment cost of wastewater and have a more intuitive understanding, the comprehensive difficulty index of wastewater treatment is now compared with the threshold to obtain the complexity level of wastewater treatment, and then the corresponding estimated cost level of wastewater treatment is matched according to the complexity level of wastewater treatment; in this embodiment, low cost means that the wastewater treatment cost is less than or equal to 10% of the average cost of wastewater treatment, medium cost means that it fluctuates within the range of 10% above and below the average cost of wastewater treatment, and high cost means that it is greater than or equal to 10% of the average cost of wastewater treatment; in this embodiment, the threshold of the comprehensive difficulty index of wastewater treatment is set as: (0, 0.55], the complexity level of wastewater treatment is simple; (0.55, 0.75], the complexity level of wastewater treatment is general; (0.75, 1], the complexity level of wastewater treatment is complex; when the comprehensive difficulty index of wastewater treatment is 0.6, the complexity level of wastewater treatment is general.

[0035] The present invention also discloses a wastewater pollutant intelligent classification system based on deep learning, comprising a memory and a processor, wherein the memory stores a wastewater pollutant intelligent classification method program based on deep learning, and when the wastewater pollutant intelligent classification method program based on deep learning is executed by the processor, the following steps are implemented: Obtaining physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater through preset sensors; Obtaining a pretreatment difficulty index of the wastewater in the pretreatment stage according to the physical characteristic data and the flow characteristic data, comparing the pretreatment difficulty index with a preset treatment difficulty index threshold, and obtaining wastewater pretreatment difficulty level data; Obtaining a comprehensive category evaluation index of biochemical treatment in the wastewater biochemical treatment stage according to the microbial community characteristic data and the microbial activity characteristic data; Obtaining a chemical treatment classification evaluation index for the wastewater deep treatment stage according to the chemical characteristic data processing; According to the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index, the corresponding treatment plans for the pretreatment stage, biochemical treatment stage and deep treatment stage are obtained by matching them with the preset wastewater treatment method database.

[0036] It should be noted that wastewater treatment mainly includes three stages, including pretreatment stage, biochemical treatment stage and deep treatment stage. In the above three stages, pollutants are classified respectively, so that wastewater can be treated more specifically. In the pretreatment stage, the pretreatment difficulty index is calculated and then the threshold is compared to obtain wastewater pretreatment difficulty level data; in the biochemical treatment stage, the biochemical treatment comprehensive category evaluation index is calculated; in the deep treatment stage, the chemical treatment classification evaluation index is calculated; the wastewater pretreatment difficulty level data, biochemical treatment comprehensive category evaluation index, and chemical treatment classification evaluation index corresponding to the three treatment stages are matched with the preset wastewater treatment method database to obtain the treatment plans corresponding to the three stages; the preset wastewater treatment method database is a database obtained by training a large amount of physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data, and chemical characteristic data and treatment plan. The database contains the treatment plans corresponding to the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index, and the chemical treatment classification evaluation index. The treatment plan also includes the corresponding treatment time, added reaction substances, and treatment mode. For example, in the pretreatment stage, The basic difficulty data corresponds to a short processing time and a fine screen is selected; the wastewater treatment method database can be updated and adjusted according to the actual situation of the wastewater treatment station.

[0037] According to an embodiment of the present invention, the physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater obtained by preset sensors specifically include: Obtaining physical characteristic data and flow characteristic data of the wastewater, the physical characteristic data including turbidity data and density data, and the flow characteristic data including flow data, flow velocity data and pressure data; Obtaining the microbial community characteristic data and microbial activity characteristic data of the wastewater. The microbial community characteristic data include the number of microbial species and microbial unit content data. The microbial activity characteristic data include respiration rate data and biomass growth rate data. Obtain chemical characteristics data of wastewater, including pH data, chemical oxygen demand data, and biochemical oxygen demand data.

[0038] It should be noted that flow data and flow velocity data refer to the data of wastewater from different sources flowing into the wastewater treatment station, and pressure data refers to the outlet pressure data of the pretreatment stage of the wastewater treatment station. Different microorganisms have different degradation abilities for different pollutants. The more microbial species there are, the more positive correlation there will be in the wastewater's own purification ability. Similarly, microbial unit content data refers to the data on the total content of microorganisms per unit volume. Respiration rate data refers to the respiration rate of microorganisms, which reflects the metabolic intensity of microorganisms to a certain extent. A fast respiration rate means that the wastewater is rich in easily degradable organic matter and the microorganisms are growing actively. The biomass growth rate data reflects the increase in the number of microorganisms within a preset time.

[0039] According to an embodiment of the present invention, the pretreatment difficulty index of wastewater in the pretreatment stage is obtained according to the physical characteristic data and the flow characteristic data, and the pretreatment difficulty index is compared with a preset treatment difficulty index threshold to obtain wastewater pretreatment difficulty level data, specifically including: Inputting the turbidity data, density data, flow data, flow velocity data and pressure data into a preset wastewater pretreatment difficulty assessment model for processing to obtain a pretreatment difficulty index; Comparing the pretreatment difficulty index with a preset treatment difficulty index threshold to obtain wastewater pretreatment difficulty level data; Extracting a first threshold and a second threshold according to a preset processing difficulty index threshold, wherein the first threshold is greater than the second threshold; comparing the preprocessing difficulty index to the first threshold and a second threshold; If the pretreatment difficulty index is greater than the first threshold, the wastewater pretreatment difficulty level data is high difficulty data; If the pretreatment difficulty index is greater than the second threshold and less than or equal to the first threshold, the wastewater pretreatment difficulty level data is medium difficulty data; If the pretreatment difficulty index is less than or equal to the second threshold, the wastewater pretreatment difficulty level data is basic difficulty data.

[0040] It should be noted that in the pretreatment stage, the pretreatment difficulty data is calculated based on the turbidity data, density data, flow data, flow velocity data and pressure data. The pretreatment difficulty data refers to data that can reflect the difficulty of treating wastewater in the pretreatment stage to a certain extent. The larger the data, the more difficult it is to treat. The calculation formula of the pretreatment difficulty index in the wastewater pretreatment difficulty assessment model is: ; in, is the preprocessing difficulty index, , , , , They are turbidity data, density data, flow data, flow velocity data, and pressure data. , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset wastewater pollutant intelligent classification platform based on deep learning); In this embodiment, the processing difficulty index threshold is set to: (0, 0.45], which is basic difficulty data; (0.45, 0.75], which is medium difficulty data; (0.75, 1], which is high difficulty data; when the pretreatment difficulty index is 0.9, the wastewater pretreatment difficulty level data is high difficulty data.

[0041] According to an embodiment of the present invention, the processing of the microbial community characteristic data and the microbial activity characteristic data to obtain a comprehensive category evaluation index of the biochemical treatment stage of the wastewater includes: According to the microbial species quantity and microbial unit content data combined with the respiration rate data and the biomass growth rate data, a preset wastewater biochemical treatment comprehensive evaluation model is used to obtain a biochemical treatment comprehensive category evaluation index; The calculation formula of the biochemical treatment comprehensive category evaluation index in the wastewater biochemical treatment comprehensive category evaluation model is: ; in, It is the comprehensive category evaluation index of biochemical treatment. , , , They are the number of microbial species, microbial unit content data, respiration rate data, and biomass growth rate data. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset wastewater pollutant intelligent classification platform based on deep learning).

[0042] It should be noted that the growth and production of microorganisms in wastewater are closely related to pollutants. The more types of microorganisms in wastewater, the more helpful it is to improve the stability of the sewage treatment system; the greater the unit content of microorganisms, the stronger the ability of microorganisms to treat pollutants in wastewater; the biochemical treatment comprehensive category evaluation index is obtained through processing based on the number of microbial species, microbial unit content data, respiration rate data and biomass growth rate data. The biochemical treatment comprehensive category evaluation index refers to the data basis for classifying the difficulty of wastewater pollutant treatment according to the pollutant situation in the biochemical treatment stage. Different biochemical treatment comprehensive category evaluation indexes represent different wastewater quality, which corresponds to different pollutant classifications and different treatment methods.

[0043] According to an embodiment of the present invention, obtaining a chemical treatment classification evaluation index for the wastewater deep treatment stage according to the chemical characteristic data processing specifically includes: Extracting a preset number of sampled chemical characteristic data of wastewater at different locations according to the chemical characteristic data, the sampled chemical characteristic data including sampled pH data, sampled chemical oxygen demand data and sampled biochemical oxygen demand data; Calculating the average value of the preset number of sampled pH data, sampled chemical oxygen demand data and sampled biochemical oxygen demand data to obtain effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data; The effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data are input into a preset chemical treatment classification evaluation model for processing to obtain a chemical treatment classification evaluation index.

[0044] It should be noted that, due to the fluidity of wastewater, wastewater at different locations may have different chemical characteristics. Therefore, in order to better obtain the chemical characteristic data of wastewater, a preset amount of chemical characteristic data of wastewater is collected at different locations to obtain sampled chemical characteristic data. The effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data are obtained by taking the average value of the sampled pH data, sampled chemical oxygen demand data and sampled biochemical oxygen demand data, and then the chemical treatment classification evaluation index is obtained according to the effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data. The calculation formula of the chemical treatment classification evaluation index in the chemical treatment classification evaluation model is: ; in, For chemical treatment classification assessment index, , , They are effective pH data, effective chemical oxygen demand data, and effective biochemical oxygen demand data. , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset wastewater pollutant intelligent classification platform based on deep learning); The chemical treatment classification evaluation index reflects the chemical pollution situation of wastewater to a certain extent, and classification treatment is carried out according to the pollution situation; the preset chemical treatment classification evaluation model is a model obtained by training a large amount of historical data, through which the chemical treatment classification evaluation index can be calculated.

[0045] According to an embodiment of the present invention, it also includes: The wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index are calculated and input into a preset wastewater treatment comprehensive difficulty evaluation model for processing to obtain a wastewater treatment comprehensive difficulty index; The calculation formula of the comprehensive difficulty index of wastewater treatment in the comprehensive difficulty assessment model of wastewater treatment is: ; in, is the comprehensive difficulty index of wastewater treatment, It is the comprehensive category evaluation index of biochemical treatment. The difficulty level data of wastewater pretreatment is For chemical treatment classification assessment index, , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset wastewater pollutant intelligent classification platform based on deep learning).

[0046] It should be noted that after obtaining the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category assessment index and the chemical treatment classification assessment index, in order to have a more comprehensive assessment of the wastewater, the comprehensive wastewater treatment difficulty index is obtained. The comprehensive wastewater treatment difficulty index reflects the difficulty of treating the wastewater to a certain extent. The larger the index, the more complicated the treatment. The comprehensive wastewater treatment difficulty assessment model is obtained through training with a large amount of historical data, and the comprehensive wastewater treatment difficulty index can be calculated through this model.

[0047] According to an embodiment of the present invention, after obtaining the comprehensive difficulty index of wastewater treatment, the method further includes: Comparing the wastewater treatment comprehensive difficulty index with a preset wastewater treatment comprehensive difficulty index threshold to obtain a wastewater treatment complexity level; Extracting a first difficulty threshold and a second difficulty threshold according to the wastewater treatment comprehensive difficulty index threshold, and the first difficulty threshold is greater than the second difficulty threshold; Comparing the wastewater treatment comprehensive difficulty index with the first difficulty threshold and the second difficulty threshold; If the comprehensive difficulty index of wastewater treatment is greater than the first difficulty threshold, the wastewater treatment complexity level is a complex level; If the comprehensive difficulty index of wastewater treatment is greater than the second difficulty threshold and less than or equal to the first difficulty threshold, the wastewater treatment complexity level is a general level; If the comprehensive difficulty index of wastewater treatment is less than or equal to the second difficulty threshold, the wastewater treatment complexity level is a simple level; The wastewater treatment complexity level is matched with a preset wastewater treatment estimated cost database to obtain a wastewater treatment estimated cost level, including low cost, medium cost or high cost.

[0048] It should be noted that different treatment measures will be taken according to the degree of wastewater pollutants during the treatment process, and different measures will have different treatment costs, which is manifested as the higher the degree of wastewater pollution, the higher the treatment cost; in order to better estimate the treatment cost of wastewater and have a more intuitive understanding, the comprehensive difficulty index of wastewater treatment is now compared with the threshold to obtain the complexity level of wastewater treatment, and then the corresponding estimated cost level of wastewater treatment is matched according to the complexity level of wastewater treatment; in this embodiment, low cost means that the wastewater treatment cost is less than or equal to 10% of the average cost of wastewater treatment, medium cost means that it fluctuates within the range of 10% above and below the average cost of wastewater treatment, and high cost means that it is greater than or equal to 10% of the average cost of wastewater treatment; in this embodiment, the threshold of the comprehensive difficulty index of wastewater treatment is set as: (0, 0.55], the complexity level of wastewater treatment is simple; (0.55, 0.75], the complexity level of wastewater treatment is general; (0.75, 1], the complexity level of wastewater treatment is complex; when the comprehensive difficulty index of wastewater treatment is 0.6, the complexity level of wastewater treatment is general.

[0049] The third aspect of the present invention provides a readable storage medium, which includes a program for an intelligent classification method for wastewater pollutants based on deep learning. When the program for an intelligent classification method for wastewater pollutants based on deep learning is executed by a processor, the steps of the intelligent classification method for wastewater pollutants based on deep learning as described in any one of the above items are implemented.

[0050] The deep learning-based intelligent classification method, system and medium for wastewater pollutants disclosed in the present invention obtain physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater, obtain wastewater pretreatment difficulty level data according to the physical characteristic data and flow characteristic data processing, obtain biochemical treatment comprehensive category evaluation index according to the microbial community characteristic data and microbial activity characteristic data processing, and then obtain chemical treatment classification evaluation index according to the chemical characteristic data processing, and finally obtain the treatment plan after matching with the preset wastewater treatment method database; thereby, the technology of intelligent classification of wastewater pollutants is realized by processing the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index, combined with matching with the preset wastewater treatment method database.

[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. Intelligent classification method for wastewater pollutants based on deep learning, characterized by: include: Obtaining physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater through preset sensors; Obtaining a pretreatment difficulty index of the wastewater in the pretreatment stage according to the physical characteristic data and the flow characteristic data, comparing the pretreatment difficulty index with a preset treatment difficulty index threshold, and obtaining wastewater pretreatment difficulty level data; Obtaining a comprehensive category evaluation index of biochemical treatment in the wastewater biochemical treatment stage according to the microbial community characteristic data and the microbial activity characteristic data; Obtaining a chemical treatment classification evaluation index for the wastewater deep treatment stage according to the chemical characteristic data processing; According to the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index, the corresponding treatment plans for the pretreatment stage, biochemical treatment stage and deep treatment stage are obtained by matching them with the preset wastewater treatment method database.

2. The method for intelligent classification of wastewater pollutants based on deep learning according to claim 1 is characterized in that: The physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of the wastewater obtained by the preset sensors specifically include: Obtaining physical characteristic data and flow characteristic data of the wastewater, the physical characteristic data including turbidity data and density data, and the flow characteristic data including flow data, flow velocity data and pressure data; Obtaining the microbial community characteristic data and microbial activity characteristic data of the wastewater, the microbial community characteristic data includes the number of microbial species and microbial unit content data, and the microbial activity characteristic data includes the respiration rate data and biomass growth rate data; Obtain chemical characteristics data of wastewater, including pH data, chemical oxygen demand data, and biochemical oxygen demand data.

3. The method for intelligent classification of wastewater pollutants based on deep learning according to claim 2 is characterized in that: The processing of obtaining the pretreatment difficulty index of the wastewater in the pretreatment stage according to the physical characteristic data and the flow characteristic data, comparing the pretreatment difficulty index with a preset treatment difficulty index threshold, and obtaining the wastewater pretreatment difficulty level data specifically includes: Inputting the turbidity data, density data, flow data, flow velocity data and pressure data into a preset wastewater pretreatment difficulty assessment model for processing to obtain a pretreatment difficulty index; Comparing the pretreatment difficulty index with a preset treatment difficulty index threshold to obtain wastewater pretreatment difficulty level data; Extracting a first threshold and a second threshold according to a preset processing difficulty index threshold, wherein the first threshold is greater than the second threshold; comparing the preprocessing difficulty index to the first threshold and a second threshold; If the pretreatment difficulty index is greater than the first threshold, the wastewater pretreatment difficulty level data is high difficulty data; If the pretreatment difficulty index is greater than the second threshold and less than or equal to the first threshold, the wastewater pretreatment difficulty level data is medium difficulty data; If the pretreatment difficulty index is less than or equal to the second threshold, the wastewater pretreatment difficulty level data is basic difficulty data.

4. The method for intelligent classification of wastewater pollutants based on deep learning according to claim 3 is characterized in that: The biochemical treatment comprehensive category evaluation index of the wastewater biochemical treatment stage is obtained according to the microbial community characteristic data and the microbial activity characteristic data, specifically including: According to the microbial species quantity and microbial unit content data combined with the respiration rate data and the biomass growth rate data, a preset wastewater biochemical treatment comprehensive evaluation model is used to obtain a biochemical treatment comprehensive category evaluation index; The calculation formula of the biochemical treatment comprehensive category evaluation index in the wastewater biochemical treatment comprehensive category evaluation model is: ； in, It is the comprehensive category evaluation index of biochemical treatment. , , , They are the number of microbial species, microbial unit content data, respiration rate data, and biomass growth rate data. , , is the preset characteristic coefficient.

5. The method for intelligent classification of wastewater pollutants based on deep learning according to claim 4 is characterized in that: The step of obtaining the chemical treatment classification evaluation index of the wastewater deep treatment stage according to the chemical characteristic data processing specifically includes: Extracting a preset number of sampled chemical characteristic data of wastewater at different locations according to the chemical characteristic data, the sampled chemical characteristic data including sampled pH data, sampled chemical oxygen demand data and sampled biochemical oxygen demand data; Calculating the average value of the preset number of sampled pH data, sampled chemical oxygen demand data, and sampled biochemical oxygen demand data to obtain effective pH data, effective chemical oxygen demand data, and effective biochemical oxygen demand data; The effective pH data, effective chemical oxygen demand data and effective biochemical oxygen demand data are input into a preset chemical treatment classification evaluation model for processing to obtain a chemical treatment classification evaluation index.

6. The method for intelligent classification of wastewater pollutants based on deep learning according to claim 5 is characterized in that: Also includes: The wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index are calculated and input into a preset wastewater treatment comprehensive difficulty evaluation model for processing to obtain a wastewater treatment comprehensive difficulty index; The calculation formula of the comprehensive difficulty index of wastewater treatment in the comprehensive difficulty assessment model of wastewater treatment is: ； in, is the comprehensive difficulty index of wastewater treatment, It is the comprehensive category evaluation index of biochemical treatment. The difficulty level data of wastewater pretreatment is The chemical treatment classification evaluation index is , , is the preset characteristic coefficient.

7. The method for intelligent classification of wastewater pollutants based on deep learning according to claim 6 is characterized in that: After obtaining the comprehensive difficulty index of wastewater treatment, it also includes: Comparing the wastewater treatment comprehensive difficulty index with a preset wastewater treatment comprehensive difficulty index threshold to obtain a wastewater treatment complexity level; Extracting a first difficulty threshold and a second difficulty threshold according to the wastewater treatment comprehensive difficulty index threshold, and the first difficulty threshold is greater than the second difficulty threshold; Comparing the wastewater treatment comprehensive difficulty index with the first difficulty threshold and the second difficulty threshold; If the comprehensive difficulty index of wastewater treatment is greater than the first difficulty threshold, the wastewater treatment complexity level is a complex level; If the comprehensive difficulty index of wastewater treatment is greater than the second difficulty threshold and less than or equal to the first difficulty threshold, the wastewater treatment complexity level is a general level; If the comprehensive difficulty index of wastewater treatment is less than or equal to the second difficulty threshold, the wastewater treatment complexity level is a simple level; The wastewater treatment complexity level is matched with a preset wastewater treatment estimated cost database to obtain a wastewater treatment estimated cost level, including low cost, medium cost or high cost.

8. A wastewater pollutant intelligent classification system based on deep learning, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a wastewater pollutant intelligent classification method program based on deep learning, and the wastewater pollutant intelligent classification method program based on deep learning is executed by the processor to implement the following steps: Obtaining physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of wastewater through preset sensors; Obtaining a pretreatment difficulty index of the wastewater in the pretreatment stage according to the physical characteristic data and the flow characteristic data, comparing the pretreatment difficulty index with a preset treatment difficulty index threshold, and obtaining wastewater pretreatment difficulty level data; Obtaining a comprehensive category evaluation index of biochemical treatment in the wastewater biochemical treatment stage according to the microbial community characteristic data and the microbial activity characteristic data; Obtaining a chemical treatment classification evaluation index for the wastewater deep treatment stage according to the chemical characteristic data processing; According to the wastewater pretreatment difficulty level data, the biochemical treatment comprehensive category evaluation index and the chemical treatment classification evaluation index, the corresponding treatment plans for the pretreatment stage, biochemical treatment stage and deep treatment stage are obtained by matching them with the preset wastewater treatment method database.

9. The wastewater pollutant intelligent classification system based on deep learning according to claim 8 is characterized in that: The physical characteristic data, flow characteristic data, microbial community characteristic data, microbial activity characteristic data and chemical characteristic data of the wastewater obtained by the preset sensors specifically include: Obtaining physical characteristic data and flow characteristic data of the wastewater, the physical characteristic data including turbidity data and density data, and the flow characteristic data including flow data, flow velocity data and pressure data; Obtaining the microbial community characteristic data and microbial activity characteristic data of the wastewater. The microbial community characteristic data include the number of microbial species and microbial unit content data. The microbial activity characteristic data include respiration rate data and biomass growth rate data. Obtain chemical characteristics data of wastewater, including pH data, chemical oxygen demand data, and biochemical oxygen demand data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a program for an intelligent classification method for wastewater pollutants based on deep learning. When the program for an intelligent classification method for wastewater pollutants based on deep learning is executed by a processor, the steps of the intelligent classification method for wastewater pollutants based on deep learning as described in any one of claims 1 to 7 are implemented.

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