Sewage treatment agent adding optimization method and system based on AI algorithm and medium

Abstract

The invention provides a sewage treatment agent adding optimization method and system based on an AI algorithm and a medium. The method comprises the following steps: acquiring sewage water quality characteristic information and purified water reuse information, respectively extracting sewage water quality characteristic data and discharge standard data, processing according to the sewage water quality characteristic data to obtain a pollution degree identification grade, processing in combination with the discharge standard data to obtain sewage treatment process information, and then performing water treatment. The method comprises the following steps: acquiring a treatment agent concentration mean value of a treatment agent adding point, comparing the treatment agent concentration mean value with a preset standard treatment agent concentration mean value to obtain a concentration deviation ratio, performing treatment according to a threshold comparison result to obtain corresponding optimized process parameter information, performing water treatment, and acquiring purified water quality parameter data corresponding to a process node; processing with preset water quality parameter standard data to obtain a sewage treatment actual effect index, and finally optimizing the dosage of the treating agent according to a threshold comparison result; therefore, optimization of addition of the sewage treatment agent is realized, and intelligence of sewage treatment is improved.

Description

Technical Field

[0001] The present application relates to the field of sewage treatment technology, and more specifically, to a sewage treatment agent dosing optimization method, system, and medium based on AI algorithm. Background Art

[0002] With the acceleration of industrialization, population growth and rapid urbanization, water resource pollution is becoming increasingly serious. Sewage treatment has become a key link in environmental protection and sustainable development. However, traditional sewage treatment methods have gradually revealed many limitations in the face of increasingly complex sewage composition and ever-increasing treatment requirements. Traditional sewage treatment processes often rely on fixed empirical parameters and manual operations, and are difficult to adapt to sewage quality and different reuse needs in real time, resulting in unstable sewage treatment effects. Sometimes, emission standards cannot be met, causing environmental pollution, or excessive addition of sewage treatment agents to ensure compliance increases treatment costs and also brings about secondary pollution problems. Therefore, there is an urgent need to develop an intelligent method for optimizing the addition of sewage treatment agents.

[0003] In response to the above problems, effective technical solutions are urgently needed. Summary of the Invention

[0004] The purpose of this application is to provide a sewage treatment agent dosage optimization method, system and medium based on AI algorithm, which can obtain sewage treatment process information through pollution identification level combined with emission standard data, optimize process parameter information based on the calculation of concentration deviation rate of treatment agent addition point, and finally calculate sewage treatment effectiveness index and perform threshold comparison, thereby optimizing treatment agent dosage and realizing intelligent optimization of sewage treatment agent dosage.

[0005] This application also provides a sewage treatment agent dosage optimization method based on an AI algorithm, comprising the following steps: Obtain sewage water quality characteristic information and purified water reuse information within a preset time period, extract sewage water quality characteristic data based on the sewage water quality characteristic information, and obtain emission standard data based on the purified water reuse information; Processing the sewage water quality characteristic data to obtain a pollution degree identification grade; Obtaining sewage treatment process information through a preset sewage treatment process information database according to the pollution degree identification level and emission standard data; Performing water treatment according to the sewage treatment process information, obtaining an average treatment agent concentration at a treatment agent addition point within a preset time period, comparing the average treatment agent concentration with an average preset standard treatment agent concentration, and obtaining a concentration deviation rate corresponding to the treatment agent addition point; Comparing the concentration deviation rate with a preset concentration deviation detection threshold, and processing the comparison result to obtain the optimized process parameter information corresponding to each dosing point; Perform water treatment according to the optimized process parameter information, obtain purified water quality parameter data corresponding to the process node, process the purified water quality parameter data with preset water quality parameter standard data, and obtain a sewage treatment effectiveness index; The sewage treatment effectiveness index is compared with a preset water treatment quality inspection threshold, and the treatment agent dosage is optimized according to the threshold comparison result.

[0006] Optionally, in the AI ​​algorithm-based sewage treatment agent dosage optimization method described in the present application, obtaining sewage water quality characteristic information and purified water reuse information within a preset time period, extracting sewage water quality characteristic data based on the sewage water quality characteristic information, and obtaining emission standard data based on the purified water reuse information include: Obtain sewage water quality characteristics information and purified water reuse information within a preset time period; Extracting sewage water quality characteristic data based on sewage water quality characteristic information, including pollutant category characteristic data and corresponding pollutant concentration data; According to the purified water reuse information, the preset purified water and emission standard relationship table is queried to obtain the emission standard data.

[0007] Optionally, in the AI ​​algorithm-based sewage treatment agent dosage optimization method described in the present application, the processing according to the sewage water quality characteristic data to obtain the pollution degree identification level includes: According to the pollutant category characteristic data, a preset pollutant category weight list is searched to obtain the weight value corresponding to the pollutant category; Multiplying the pollutant concentration data by the corresponding weight value and summing them up to obtain a sewage pollution assessment index; Comparing the sewage pollution degree assessment index with a preset sewage standard pollution degree assessment index to obtain a relative pollution degree value; The relative value of the pollution degree is compared with a preset sewage identification threshold value, and the pollution degree identification level is obtained according to the range to which the threshold comparison result belongs.

[0008] Optionally, in the sewage treatment agent dosage optimization method based on the AI ​​algorithm described in the present application, the water treatment is performed according to the sewage treatment process information, the average treatment agent concentration at the treatment agent dosage point within a preset time period is obtained, the average treatment agent concentration is compared with the average treatment agent concentration of a preset standard, and the concentration deviation rate corresponding to the treatment agent dosage point is obtained, including: Perform water treatment according to the sewage treatment process information, and obtain the real-time concentration of the treatment agent corresponding to multiple points within a preset range of the treatment agent addition point within a preset time period; Obtain the distance data from the location point to the treatment agent addition point; Query a preset weight value list based on the distance data to obtain a weight value corresponding to the location point; Processing is performed based on the real-time concentration of the treatment agent and the corresponding weight value to obtain the average treatment agent concentration at the treatment agent addition point within a preset time period; The mean concentration of the treatment agent is compared with the mean concentration of the preset standard treatment agent to obtain the concentration deviation rate corresponding to the treatment agent addition point.

[0009] Optionally, in the AI ​​algorithm-based sewage treatment agent dosing optimization method described in the present application, the concentration deviation rate is compared with a preset concentration deviation detection threshold, and the optimized process parameter information corresponding to each dosing point is obtained according to the comparison result, including: Comparing the concentration deviation rate with a preset concentration deviation detection threshold; If it is less than or equal to the preset concentration deviation test threshold, no action will be taken; If it is greater than the preset concentration deviation detection threshold, the preset treatment agent addition flow optimization method is used according to the concentration deviation rate to obtain the optimized amount of treatment agent addition flow corresponding to the addition point.

[0010] Optionally, in the sewage treatment agent dosage optimization method based on the AI ​​algorithm described in the present application, performing water treatment according to the optimized process parameter information, obtaining purified water quality parameter data corresponding to the process node, processing the purified water quality parameter data with preset water quality parameter standard data, and obtaining the sewage treatment effectiveness index, include: Perform water treatment according to the optimized process parameter information to obtain purified water quality parameter data corresponding to the process nodes, including pH value, COD data, BOD data, pathogen count, ammonia nitrogen content, and phosphorus content; The pH value, COD data, BOD data, pathogen quantity, ammonia nitrogen content and phosphorus content are respectively compared with the preset water quality parameter standard data to obtain relative values ​​of purified water quality parameters, including relative pH value, relative COD value, relative BOD value, relative pathogen quantity, relative ammonia nitrogen content and relative phosphorus content; The relative pH value, COD relative value, BOD relative value, pathogen quantity relative value, ammonia nitrogen content relative value and phosphorus content relative value are input into a preset sewage treatment effectiveness evaluation model for processing to obtain a sewage treatment effectiveness index.

[0011] Optionally, in the AI ​​algorithm-based sewage treatment agent dosage optimization method described in the present application, the sewage treatment effectiveness index is compared with a preset water treatment quality inspection threshold, and the treatment agent dosage is optimized according to the threshold comparison result, including: Comparing the sewage treatment effectiveness index with a preset water treatment quality inspection threshold; If it is less than or equal to the first preset water treatment quality inspection threshold, then activate the response of increasing the amount of sewage treatment agent added; If it is greater than the first preset water treatment quality inspection threshold and less than or equal to the second preset water treatment quality inspection threshold, no action will be taken; If it is greater than the second preset water treatment quality inspection threshold, the response of reducing the amount of sewage treatment agent is activated.

[0012] In a second aspect, the present application provides a sewage treatment agent dosing optimization system based on an AI algorithm, the system comprising: a memory and a processor, the memory including a program for a sewage treatment agent dosing optimization method based on an AI algorithm, and the program for the sewage treatment agent dosing optimization method based on an AI algorithm, when executed by the processor, implements the following steps: Obtain sewage water quality characteristic information and purified water reuse information within a preset time period, extract sewage water quality characteristic data based on the sewage water quality characteristic information, and obtain emission standard data based on the purified water reuse information; Processing the sewage water quality characteristic data to obtain a pollution degree identification grade; Obtaining sewage treatment process information through a preset sewage treatment process information database according to the pollution degree identification level and emission standard data; Performing water treatment according to the sewage treatment process information, obtaining an average treatment agent concentration at a treatment agent addition point within a preset time period, comparing the average treatment agent concentration with an average preset standard treatment agent concentration, and obtaining a concentration deviation rate corresponding to the treatment agent addition point; Comparing the concentration deviation rate with a preset concentration deviation detection threshold, and processing the comparison result to obtain the optimized process parameter information corresponding to each dosing point; Perform water treatment according to the optimized process parameter information, obtain purified water quality parameter data corresponding to the process node, process the purified water quality parameter data with preset water quality parameter standard data, and obtain a sewage treatment effectiveness index; The sewage treatment effectiveness index is compared with a preset water treatment quality inspection threshold, and the treatment agent dosage is optimized according to the threshold comparison result.

[0013] Optionally, in the sewage treatment agent dosage optimization system based on the AI ​​algorithm described in the present application, the obtaining of sewage water quality characteristic information and purified water reuse information within a preset time period, extracting sewage water quality characteristic data based on the sewage water quality characteristic information, and obtaining emission standard data based on the purified water reuse information include: Obtain sewage water quality characteristics information and purified water reuse information within a preset time period; Extracting sewage water quality characteristic data based on sewage water quality characteristic information, including pollutant category characteristic data and corresponding pollutant concentration data; According to the purified water reuse information, the preset purified water and emission standard relationship table is queried to obtain the emission standard data.

[0014] In a third aspect, the present application also provides a computer-readable storage medium, which stores a sewage treatment agent dosing optimization method program based on an AI algorithm. When the sewage treatment agent dosing optimization method program based on an AI algorithm is executed by a processor, the steps of the sewage treatment agent dosing optimization method based on an AI algorithm as described in any one of the above items are implemented.

[0015] From the above, it can be seen that the sewage treatment agent dosage optimization method, system and medium based on AI algorithm provided in this application obtain sewage treatment process information through the pollution identification level combined with emission standard data, optimize the process parameter information based on the calculation of the concentration deviation rate of the treatment agent addition point, and finally calculate the sewage treatment effectiveness index and perform threshold comparison, thereby optimizing the treatment agent dosage and realizing intelligent optimization of sewage treatment agent dosage.

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

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. 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 relevant drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flowchart of a sewage treatment agent dosage optimization method based on an AI algorithm provided in an embodiment of the present application; Figure 2 A flowchart of obtaining a pollution level assessment level for a sewage treatment agent dosage optimization method based on an AI algorithm provided in an embodiment of the present application; Figure 3 A flow chart of obtaining the sewage treatment effectiveness index of the sewage treatment agent addition optimization method based on the AI ​​algorithm provided in the 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 generally 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 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 fall within 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. Therefore, once an item is defined in one drawing, it does not need to be further defined or 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 should not be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 This is a flow chart of a sewage treatment agent dosing optimization method based on an AI algorithm in some embodiments of the present application. This sewage treatment agent dosing optimization method based on an AI algorithm is used in terminal devices, such as computers, mobile phones, etc. This sewage treatment agent dosing optimization method based on an AI algorithm includes the following steps: S11. Obtain sewage water quality characteristic information and purified water reuse information within a preset time period, extract sewage water quality characteristic data based on the sewage water quality characteristic information, and obtain emission standard data based on the purified water reuse information; S12. Processing the sewage water quality characteristic data to obtain a pollution level identification grade; S13, obtaining sewage treatment process information through a preset sewage treatment process information database according to the pollution degree identification level and emission standard data; S14. Performing water treatment according to the sewage treatment process information, obtaining an average treatment agent concentration at the treatment agent addition point within a preset time period, comparing the average treatment agent concentration with an average preset standard treatment agent concentration, and obtaining a concentration deviation rate corresponding to the treatment agent addition point; S15, comparing the concentration deviation rate with a preset concentration deviation detection threshold, and obtaining optimized process parameter information corresponding to each dosing point according to the comparison result; S16. Perform water treatment according to the optimized process parameter information, obtain purified water quality parameter data corresponding to the process node, and process the purified water quality parameter data with preset water quality parameter standard data to obtain a sewage treatment effectiveness index; S17. Compare the sewage treatment effectiveness index with a preset water treatment quality inspection threshold, and optimize the treatment agent dosage according to the threshold comparison result.

[0022] It should be noted that in order to achieve intelligent optimization of sewage treatment agent addition, the sewage water quality characteristic data extracted according to the sewage water quality characteristic information is first processed to obtain the pollution degree identification level, and the emission standard data is obtained according to the clean water reuse information. The sewage treatment process information is obtained through the preset sewage treatment process information database based on the pollution degree identification level and the emission standard data. Among them, the sewage treatment process information database is constructed by the pollution degree identification level and emission standard data of a large number of historical samples and the corresponding sewage treatment processes. Then, water treatment is implemented according to the obtained sewage treatment process information, and the average treatment agent concentration at the treatment agent addition point within the preset time period is obtained. In this embodiment, the preset The time period is set to 10 minutes, and the mean concentration of the treatment agent is compared with the preset standard treatment agent concentration mean to obtain the concentration deviation rate corresponding to the treatment agent addition point, and then compare it with the preset concentration deviation detection threshold. According to the comparison results, the optimized process parameter information corresponding to each addition point is obtained, including increasing the addition flow rate, reducing the addition flow rate or not treating. After the optimization of the sewage treatment process information is completed, water treatment is continued, and the purified water quality parameter data corresponding to the process node is obtained. It is processed with the preset water quality parameter standard data to obtain the sewage treatment effectiveness index, and finally compared with the threshold to test the water treatment effectiveness and evaluate whether the treatment agent dosage is reasonable.

[0023] According to an embodiment of the present invention, the steps of obtaining sewage water quality characteristic information and purified water reuse information within a preset time period, extracting sewage water quality characteristic data based on the sewage water quality characteristic information, and obtaining emission standard data based on the purified water reuse information include: Obtain sewage water quality characteristics information and purified water reuse information within a preset time period; Extracting sewage water quality characteristic data based on sewage water quality characteristic information, including pollutant category characteristic data and corresponding pollutant concentration data; According to the purified water reuse information, the preset purified water and emission standard relationship table is queried to obtain the emission standard data.

[0024] It should be noted that in order to accurately assess the pollution information of sewage, the sewage water quality characteristic information is first obtained, and the sewage water quality characteristic data including pollutant category characteristic data and corresponding pollutant concentration data are extracted; the emission standards for different uses of sewage after treatment are also different, and the clean water reuse information is first obtained, such as equipment cooling circulating water, greening water, etc. According to the clean water reuse information, the preset clean water and emission standard relationship table is queried to obtain the emission standard data, where the clean water and emission standard relationship table is provided by the sewage treatment agent addition optimization platform.

[0025] Please refer to Figure 2 , Figure 2 This is a flow chart of obtaining a pollution level assessment level for a sewage treatment agent dosage optimization method based on an AI algorithm in some embodiments of the present application. According to an embodiment of the present invention, the processing of the sewage water quality characteristic data to obtain the pollution level assessment level includes: S21, querying a preset pollutant category weight list according to the pollutant category characteristic data to obtain a weight value corresponding to the pollutant category; S22, multiplying the pollutant concentration data by the corresponding weight value and summing the results to obtain a sewage pollution assessment index; S23, comparing the sewage pollution degree assessment index with a preset sewage standard pollution degree assessment index to obtain a relative pollution degree value; S24. Compare the relative value of the pollution level with a preset sewage identification threshold, and obtain a pollution level identification grade according to the range to which the threshold comparison result belongs.

[0026] It should be noted that different pollutants in sewage have different importance in the assessment of pollution degree. Therefore, first, the preset pollutant category weight list is queried according to the pollutant category characteristic data to obtain the weight value corresponding to the pollutant category. Among them, the pollutant category weight list is provided by the sewage treatment agent addition optimization platform. The pollutant concentration data is multiplied by the corresponding weight value and summed up to obtain the sewage pollution degree assessment index. For example, the concentration and weight value corresponding to suspended solids are 100 mg / L and 1 respectively, and the concentration and weight value corresponding to total phosphorus are 7 respectively. mg / L, 5, then 100*1+7*5=135 is the sewage pollution degree assessment index. The obtained sewage pollution degree assessment index is compared with the preset sewage standard pollution degree assessment index to obtain a relative pollution degree value. For example, the obtained sewage pollution degree assessment index is 135, and the sewage standard pollution degree assessment index is 500, then 135 / 500=0.27 is the relative pollution degree value. The obtained relative pollution degree value is compared with the preset sewage identification threshold value. In this embodiment, the sewage identification threshold value is set to (0, 0.5], (0.5, 0.7], and (0.7, 1], which correspond to low pollution, moderate pollution, and high pollution, respectively. For example, if the obtained relative pollution degree value is 0.27, the pollution degree identification level is low pollution.

[0027] According to an embodiment of the present invention, performing water treatment according to the sewage treatment process information, obtaining the average treatment agent concentration at the treatment agent addition point within a preset time period, comparing the average treatment agent concentration with the average treatment agent concentration of a preset standard, and obtaining the concentration deviation rate corresponding to the treatment agent addition point include: Perform water treatment according to the sewage treatment process information, and obtain the real-time concentration of the treatment agent corresponding to multiple points within a preset range of the treatment agent addition point within a preset time period; Obtain the distance data from the location point to the treatment agent addition point; Query a preset weight value list based on the distance data to obtain a weight value corresponding to the location point; Processing is performed based on the real-time concentration of the treatment agent and the corresponding weight value to obtain the average treatment agent concentration at the treatment agent addition point within a preset time period; The mean concentration of the treatment agent is compared with the mean concentration of the preset standard treatment agent to obtain the concentration deviation rate corresponding to the treatment agent addition point.

[0028] It should be noted that after determining the sewage treatment process information, sewage treatment is implemented to obtain the real-time concentration of the treatment agent corresponding to multiple location points within the preset range of the treatment agent addition point within the preset time period. In this embodiment, the preset time period is set to 10 minutes. For example, there are 3 sampling location points, and the real-time concentrations of the treatment agent are 10 mg / L, 8 mg / L, and 9 mg / L, respectively. The distance data from the sampling location point to the treatment agent addition point are obtained, for example, 1 m, 1.2 m, and 1.3 m, respectively. The preset weight value list is queried according to the distance data to obtain the weight values ​​corresponding to the location points, which are 0.5, 0.3, and 0.2, respectively. The weight value list is provided by the sewage treatment agent addition optimization platform, and 10*0.5+8*0.3+9*0.2=9.2 mg / L is the average treatment agent concentration of the treatment agent addition point within the preset time period. The obtained average treatment agent concentration is compared with the preset standard treatment agent concentration average to obtain the concentration deviation rate corresponding to the treatment agent addition point. The concentration deviation rate calculation formula is: ; in, is the concentration deviation rate, 、 are the mean concentrations of treatment agent and standard treatment agent, respectively.

[0029] According to an embodiment of the present invention, the concentration deviation rate is compared with a preset concentration deviation detection threshold, and the optimized process parameter information corresponding to each dosing point is obtained according to the comparison result, including: Comparing the concentration deviation rate with a preset concentration deviation detection threshold; If it is less than or equal to the preset concentration deviation test threshold, no action will be taken; If it is greater than the preset concentration deviation detection threshold, the preset treatment agent addition flow optimization method is used according to the concentration deviation rate to obtain the optimized amount of treatment agent addition flow corresponding to the addition point.

[0030] It should be noted that when the concentration of the sewage treatment agent stock solution is constant, the amount of sewage treatment agent addition flow rate has a greater impact on the concentration. The obtained concentration deviation rate is compared with the preset concentration deviation detection threshold. In this embodiment, the concentration deviation detection threshold is set to (0, 0.4] and (0.4, 1], corresponding to no treatment and optimized addition flow rate, respectively. For example, if the obtained concentration deviation rate is 0.3, it means that the concentration at the addition point is normal, and no treatment is performed. If the obtained concentration deviation rate is 0.5, it means that the addition flow rate is insufficient or excessive. Then, according to the concentration deviation rate, the preset treatment agent addition flow rate optimization method is used to obtain the optimized amount of treatment agent addition flow rate corresponding to the addition point.

[0031] Please refer to Figure 3 , Figure 3 This is a flow chart of obtaining a sewage treatment effectiveness index for a sewage treatment agent dosage optimization method based on an AI algorithm in some embodiments of the present application. According to an embodiment of the present invention, performing water treatment according to the optimized process parameter information, obtaining purified water quality parameter data corresponding to a process node, processing the purified water quality parameter data with preset water quality parameter standard data, and obtaining a sewage treatment effectiveness index include: S31. Perform water treatment according to the optimized process parameter information to obtain purified water quality parameter data corresponding to the process node, including pH value, COD data, BOD data, pathogen count, ammonia nitrogen content, and phosphorus content; S32, comparing the pH value, COD data, BOD data, pathogen count, ammonia nitrogen content, and phosphorus content with preset water quality parameter standard data, respectively, to obtain relative values ​​of purified water quality parameters, including relative pH value, relative COD value, relative BOD value, relative pathogen count, relative ammonia nitrogen content, and relative phosphorus content; S33. Input the relative pH value, COD relative value, BOD relative value, pathogen quantity relative value, ammonia nitrogen content relative value and phosphorus content relative value into a preset sewage treatment effectiveness evaluation model for processing to obtain a sewage treatment effectiveness index.

[0032] It should be noted that after optimizing the process parameter information, water treatment is continued, and purified water quality parameter data corresponding to each process node, including pH value, COD data, BOD data, pathogen quantity, ammonia nitrogen content, and phosphorus content, are obtained. Among them, COD data is chemical oxygen demand data, and BOD data is biochemical oxygen demand data. They are compared with the preset water quality parameter standard data to obtain relative values ​​of purified water quality parameters. For example, the obtained pH value is 6.5 and the pH standard value is 7, then 6.5 / 7=0.93 is the pH relative value; the obtained pH relative value, COD relative value, BOD relative value, pathogen quantity relative value, ammonia nitrogen content relative value, and phosphorus content relative value are input into the preset sewage treatment effectiveness evaluation model for processing to obtain the sewage treatment effectiveness index; The calculation formula of the sewage treatment effectiveness index in the sewage treatment effectiveness evaluation model is: ; in, is the sewage treatment effectiveness index, 、 、 、 、 、 They are the relative values ​​of pH, COD, BOD, pathogen quantity, ammonia nitrogen content and phosphorus content. 、 It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the sewage treatment agent dosing optimization platform).

[0033] According to an embodiment of the present invention, the sewage treatment effectiveness index is compared with a preset water treatment quality inspection threshold, and the treatment agent dosage is optimized according to the threshold comparison result, including: Comparing the sewage treatment effectiveness index with a preset water treatment quality inspection threshold; If it is less than or equal to the first preset water treatment quality inspection threshold, then activate the response of increasing the amount of sewage treatment agent added; If it is greater than the first preset water treatment quality inspection threshold and less than or equal to the second preset water treatment quality inspection threshold, no action will be taken; If it is greater than the second preset water treatment quality inspection threshold, the response of reducing the amount of sewage treatment agent is activated.

[0034] It should be noted that the obtained sewage treatment effectiveness index is compared with the preset water treatment quality inspection threshold. In this embodiment, the water treatment quality inspection threshold is set to (0, 0.3], (0.3, 0.7], and (0.7, 1], respectively corresponding to activating an increase in the sewage treatment agent dosage response, no treatment, and activating a decrease in the sewage treatment agent dosage response. For example, if the obtained sewage treatment effectiveness index is 0.2, it means that the currently added sewage treatment agent is insufficient, then the increase in the sewage treatment agent dosage response is activated. If the obtained sewage treatment effectiveness index is 0.5, it means that the currently added sewage treatment agent is normal, then no treatment is performed. If the obtained sewage treatment effectiveness index is 0.8, it means that the currently added sewage treatment agent is excessive, then the decrease in the sewage treatment agent dosage response is activated.

[0035] It is worth mentioning that according to an embodiment of the present invention, if the concentration deviation is greater than the preset concentration deviation detection threshold, the optimized amount of treatment agent addition flow corresponding to the addition point is obtained by using a preset treatment agent addition flow optimization method according to the concentration deviation rate, including: Obtain real-time flow data and treatment agent concentration data corresponding to the sewage treatment agent addition point; Calculate the optimized flow rate of the treatment agent corresponding to the addition point based on the real-time flow data and the treatment agent concentration data in combination with the concentration deviation rate and the mean value of the standard treatment agent concentration; If the mean concentration of the treatment agent is less than the mean concentration of the preset standard treatment agent, the optimized amount of treatment agent addition flow is the increase in the treatment agent addition flow; If the mean treatment agent concentration is greater than the preset standard treatment agent concentration mean, the treatment agent addition flow rate optimization amount is the treatment agent addition flow rate reduction amount.

[0036] It should be noted that when the concentration deviation rate is greater than the preset concentration deviation test threshold, it means that the concentration at the sewage treatment agent addition point is too low or too high. In order to improve the sewage treatment effect, the sewage treatment agent addition flow rate needs to be adjusted. First, the real-time flow data and treatment agent concentration data corresponding to the sewage treatment agent addition point are obtained, and then the concentration deviation rate and the mean standard treatment agent concentration are combined for calculation to obtain the optimized treatment agent addition flow rate corresponding to the addition point. The calculation formula for the optimized amount of treatment agent addition flow is: ; in, To optimize the flow rate of treatment agent, 、 、 、 They are real-time flow data, treatment agent concentration data, concentration deviation rate and standard treatment agent concentration mean, 、 It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the sewage treatment agent dosing optimization platform).

[0037] The present invention also discloses a sewage treatment agent dosing optimization system based on an AI algorithm, comprising a memory and a processor. The memory includes a sewage treatment agent dosing optimization method program based on the AI ​​algorithm. When the sewage treatment agent dosing optimization method program based on the AI ​​algorithm is executed by the processor, the following steps are implemented: Obtain sewage water quality characteristic information and purified water reuse information within a preset time period, extract sewage water quality characteristic data based on the sewage water quality characteristic information, and obtain emission standard data based on the purified water reuse information; Processing the sewage water quality characteristic data to obtain a pollution degree identification grade; Obtaining sewage treatment process information through a preset sewage treatment process information database according to the pollution degree identification level and emission standard data; Performing water treatment according to the sewage treatment process information, obtaining an average treatment agent concentration at a treatment agent addition point within a preset time period, comparing the average treatment agent concentration with an average preset standard treatment agent concentration, and obtaining a concentration deviation rate corresponding to the treatment agent addition point; Comparing the concentration deviation rate with a preset concentration deviation detection threshold, and processing the comparison result to obtain the optimized process parameter information corresponding to each dosing point; Perform water treatment according to the optimized process parameter information, obtain purified water quality parameter data corresponding to the process node, process the purified water quality parameter data with the preset water quality parameter standard data, and obtain the sewage treatment effectiveness index; The sewage treatment effectiveness index is compared with a preset water treatment quality inspection threshold, and the treatment agent dosage is optimized according to the threshold comparison result.

[0038] It should be noted that in order to achieve intelligent optimization of sewage treatment agent addition, the sewage water quality characteristic data extracted according to the sewage water quality characteristic information is first processed to obtain the pollution degree identification level, and the emission standard data is obtained according to the clean water reuse information. The sewage treatment process information is obtained through the preset sewage treatment process information database based on the pollution degree identification level and the emission standard data. Among them, the sewage treatment process information database is constructed by the pollution degree identification level and emission standard data of a large number of historical samples and the corresponding sewage treatment processes. Then, water treatment is implemented according to the obtained sewage treatment process information, and the average treatment agent concentration at the treatment agent addition point within the preset time period is obtained. In this embodiment, the preset The time period is set to 10 minutes, and the mean concentration of the treatment agent is compared with the preset standard treatment agent concentration mean to obtain the concentration deviation rate corresponding to the treatment agent addition point, and then compare it with the preset concentration deviation detection threshold. According to the comparison results, the optimized process parameter information corresponding to each addition point is obtained, including increasing the addition flow rate, reducing the addition flow rate or not treating. After the optimization of the sewage treatment process information is completed, water treatment is continued, and the purified water quality parameter data corresponding to the process node is obtained. It is processed with the preset water quality parameter standard data to obtain the sewage treatment effectiveness index, and finally compared with the threshold to test the water treatment effectiveness and evaluate whether the treatment agent dosage is reasonable.

[0039] According to an embodiment of the present invention, the steps of obtaining sewage water quality characteristic information and purified water reuse information within a preset time period, extracting sewage water quality characteristic data based on the sewage water quality characteristic information, and obtaining emission standard data based on the purified water reuse information include: Obtain sewage water quality characteristics information and purified water reuse information within a preset time period; Extracting sewage water quality characteristic data based on sewage water quality characteristic information, including pollutant category characteristic data and corresponding pollutant concentration data; According to the purified water reuse information, the preset purified water and emission standard relationship table is queried to obtain the emission standard data.

[0040] It should be noted that in order to accurately assess the pollution information of sewage, the sewage water quality characteristic information is first obtained, and the sewage water quality characteristic data including pollutant category characteristic data and corresponding pollutant concentration data are extracted; the emission standards for different uses of sewage after treatment are also different, and the clean water reuse information is first obtained, such as equipment cooling circulating water, greening water, etc. According to the clean water reuse information, the preset clean water and emission standard relationship table is queried to obtain the emission standard data, where the clean water and emission standard relationship table is provided by the sewage treatment agent addition optimization platform.

[0041] According to an embodiment of the present invention, processing the sewage water quality characteristic data to obtain a pollution degree identification level includes: According to the pollutant category characteristic data, a preset pollutant category weight list is searched to obtain the weight value corresponding to the pollutant category; Multiplying the pollutant concentration data by the corresponding weight value and summing them up to obtain a sewage pollution assessment index; Comparing the sewage pollution degree assessment index with a preset sewage standard pollution degree assessment index to obtain a relative pollution degree value; The relative value of the pollution degree is compared with a preset sewage identification threshold value, and the pollution degree identification level is obtained according to the range to which the threshold comparison result belongs.

[0042] It should be noted that different pollutants in sewage have different importance in the assessment of pollution degree. Therefore, first, the preset pollutant category weight list is queried according to the pollutant category characteristic data to obtain the weight value corresponding to the pollutant category. Among them, the pollutant category weight list is provided by the sewage treatment agent addition optimization platform. The pollutant concentration data is multiplied by the corresponding weight value and summed up to obtain the sewage pollution degree assessment index. For example, the concentration and weight value corresponding to suspended solids are 100 mg / L and 1 respectively, and the concentration and weight value corresponding to total phosphorus are 7 respectively. mg / L, 5, then 100*1+7*5=135 is the sewage pollution degree assessment index. The obtained sewage pollution degree assessment index is compared with the preset sewage standard pollution degree assessment index to obtain a relative pollution degree value. For example, the obtained sewage pollution degree assessment index is 135, and the sewage standard pollution degree assessment index is 500, then 135 / 500=0.27 is the relative pollution degree value. The obtained relative pollution degree value is compared with the preset sewage identification threshold value. In this embodiment, the sewage identification threshold value is set to (0, 0.5], (0.5, 0.7], and (0.7, 1], which correspond to low pollution, moderate pollution, and high pollution, respectively. For example, if the obtained relative pollution degree value is 0.27, the pollution degree identification level is low pollution.

[0043] According to an embodiment of the present invention, performing water treatment according to the sewage treatment process information, obtaining the average treatment agent concentration at the treatment agent addition point within a preset time period, comparing the average treatment agent concentration with the average treatment agent concentration of a preset standard, and obtaining the concentration deviation rate corresponding to the treatment agent addition point include: Perform water treatment according to the sewage treatment process information, and obtain the real-time concentration of the treatment agent corresponding to multiple points within a preset range of the treatment agent addition point within a preset time period; Obtain the distance data from the location point to the treatment agent addition point; Query a preset weight value list based on the distance data to obtain a weight value corresponding to the location point; Processing is performed based on the real-time concentration of the treatment agent and the corresponding weight value to obtain the average treatment agent concentration at the treatment agent addition point within a preset time period; The mean concentration of the treatment agent is compared with the mean concentration of the preset standard treatment agent to obtain the concentration deviation rate corresponding to the treatment agent addition point.

[0044] It should be noted that after determining the sewage treatment process information, sewage treatment is implemented to obtain the real-time concentration of the treatment agent corresponding to multiple location points within the preset range of the treatment agent addition point within the preset time period. In this embodiment, the preset time period is set to 10 minutes. For example, there are 3 sampling location points, and the real-time concentrations of the treatment agent are 10 mg / L, 8 mg / L, and 9 mg / L, respectively. The distance data from the sampling location point to the treatment agent addition point are obtained, for example, 1 m, 1.2 m, and 1.3 m, respectively. The preset weight value list is queried according to the distance data to obtain the weight values ​​corresponding to the location points, which are 0.5, 0.3, and 0.2, respectively. The weight value list is provided by the sewage treatment agent addition optimization platform, and 10*0.5+8*0.3+9*0.2=9.2 mg / L is the average treatment agent concentration of the treatment agent addition point within the preset time period. The obtained average treatment agent concentration is compared with the preset standard treatment agent concentration average to obtain the concentration deviation rate corresponding to the treatment agent addition point. The concentration deviation rate calculation formula is: ; in, is the concentration deviation rate, 、 are the mean concentrations of treatment agent and standard treatment agent, respectively.

[0045] According to an embodiment of the present invention, the concentration deviation rate is compared with a preset concentration deviation detection threshold, and the optimized process parameter information corresponding to each dosing point is obtained according to the comparison result, including: Comparing the concentration deviation rate with a preset concentration deviation detection threshold; If it is less than or equal to the preset concentration deviation test threshold, no action will be taken; If it is greater than the preset concentration deviation detection threshold, the preset treatment agent addition flow optimization method is used according to the concentration deviation rate to obtain the optimized amount of treatment agent addition flow corresponding to the addition point.

[0046] It should be noted that when the concentration of the sewage treatment agent stock solution is constant, the amount of sewage treatment agent addition flow rate has a greater impact on the concentration. The obtained concentration deviation rate is compared with the preset concentration deviation detection threshold. In this embodiment, the concentration deviation detection threshold is set to (0, 0.4] and (0.4, 1], corresponding to no treatment and optimized addition flow rate, respectively. For example, if the obtained concentration deviation rate is 0.3, it means that the concentration at the addition point is normal, and no treatment is performed. If the obtained concentration deviation rate is 0.5, it means that the addition flow rate is insufficient or excessive. Then, according to the concentration deviation rate, the preset treatment agent addition flow rate optimization method is used to obtain the optimized amount of treatment agent addition flow rate corresponding to the addition point.

[0047] According to an embodiment of the present invention, performing water treatment according to the optimized process parameter information, obtaining purified water quality parameter data corresponding to the process node, processing the purified water quality parameter data with preset water quality parameter standard data, and obtaining a sewage treatment effectiveness index includes: Perform water treatment according to the optimized process parameter information to obtain purified water quality parameter data corresponding to the process nodes, including pH value, COD data, BOD data, pathogen count, ammonia nitrogen content, and phosphorus content; The pH value, COD data, BOD data, pathogen quantity, ammonia nitrogen content and phosphorus content are respectively compared with the preset water quality parameter standard data to obtain relative values ​​of purified water quality parameters, including relative pH value, relative COD value, relative BOD value, relative pathogen quantity, relative ammonia nitrogen content and relative phosphorus content; The relative pH value, COD relative value, BOD relative value, pathogen quantity relative value, ammonia nitrogen content relative value and phosphorus content relative value are input into a preset sewage treatment effectiveness evaluation model for processing to obtain a sewage treatment effectiveness index.

[0048] It should be noted that after optimizing the process parameter information, water treatment is continued, and purified water quality parameter data corresponding to each process node, including pH value, COD data, BOD data, pathogen quantity, ammonia nitrogen content, and phosphorus content, are obtained. Among them, COD data is chemical oxygen demand data, and BOD data is biochemical oxygen demand data. They are compared with the preset water quality parameter standard data to obtain relative values ​​of purified water quality parameters. For example, the obtained pH value is 6.5 and the pH standard value is 7, then 6.5 / 7=0.93 is the pH relative value; the obtained pH relative value, COD relative value, BOD relative value, pathogen quantity relative value, ammonia nitrogen content relative value, and phosphorus content relative value are input into the preset sewage treatment effectiveness evaluation model for processing to obtain the sewage treatment effectiveness index; The calculation formula of the sewage treatment effectiveness index in the sewage treatment effectiveness evaluation model is: ; in, is the sewage treatment effectiveness index, 、 、 、 、 、 They are the relative values ​​of pH, COD, BOD, pathogen quantity, ammonia nitrogen content and phosphorus content. 、 It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the sewage treatment agent dosing optimization platform).

[0049] According to an embodiment of the present invention, the sewage treatment effectiveness index is compared with a preset water treatment quality inspection threshold, and the treatment agent dosage is optimized according to the threshold comparison result, including: Comparing the sewage treatment effectiveness index with a preset water treatment quality inspection threshold; If it is less than or equal to the first preset water treatment quality inspection threshold, then activate the response of increasing the amount of sewage treatment agent added; If it is greater than the first preset water treatment quality inspection threshold and less than or equal to the second preset water treatment quality inspection threshold, no action will be taken; If it is greater than the second preset water treatment quality inspection threshold, the response of reducing the amount of sewage treatment agent is activated.

[0050] It should be noted that the obtained sewage treatment effectiveness index is compared with the preset water treatment quality inspection threshold. In this embodiment, the water treatment quality inspection threshold is set to (0, 0.3], (0.3, 0.7], and (0.7, 1], respectively corresponding to activating an increase in the sewage treatment agent dosage response, no treatment, and activating a decrease in the sewage treatment agent dosage response. For example, if the obtained sewage treatment effectiveness index is 0.2, it means that the currently added sewage treatment agent is insufficient, then the increase in the sewage treatment agent dosage response is activated. If the obtained sewage treatment effectiveness index is 0.5, it means that the currently added sewage treatment agent is normal, then no treatment is performed. If the obtained sewage treatment effectiveness index is 0.8, it means that the currently added sewage treatment agent is excessive, then the decrease in the sewage treatment agent dosage response is activated.

[0051] It is worth mentioning that according to an embodiment of the present invention, if the concentration deviation is greater than the preset concentration deviation detection threshold, the optimized amount of treatment agent addition flow corresponding to the addition point is obtained by using a preset treatment agent addition flow optimization method according to the concentration deviation rate, including: Obtain real-time flow data and treatment agent concentration data corresponding to the sewage treatment agent addition point; Calculate the optimized flow rate of the treatment agent corresponding to the addition point based on the real-time flow data and the treatment agent concentration data in combination with the concentration deviation rate and the mean value of the standard treatment agent concentration; If the mean concentration of the treatment agent is less than the mean concentration of the preset standard treatment agent, the optimized amount of treatment agent addition flow is the increase in the treatment agent addition flow; If the mean treatment agent concentration is greater than the preset standard treatment agent concentration mean, the treatment agent addition flow rate optimization amount is the treatment agent addition flow rate reduction amount.

[0052] It should be noted that when the concentration deviation rate is greater than the preset concentration deviation test threshold, it means that the concentration at the sewage treatment agent addition point is too low or too high. In order to improve the sewage treatment effect, the sewage treatment agent addition flow rate needs to be adjusted. First, the real-time flow data and treatment agent concentration data corresponding to the sewage treatment agent addition point are obtained, and then the concentration deviation rate and the mean standard treatment agent concentration are combined for calculation to obtain the optimized treatment agent addition flow rate corresponding to the addition point. The calculation formula for the optimized amount of treatment agent addition flow is: ; in, To optimize the flow rate of treatment agent, 、 、 、 They are real-time flow data, treatment agent concentration data, concentration deviation rate and standard treatment agent concentration mean, 、 It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the sewage treatment agent dosing optimization platform).

[0053] The third aspect of the present invention provides a readable storage medium, which stores a sewage treatment agent dosing optimization method program based on an AI algorithm. When the sewage treatment agent dosing optimization method program based on an AI algorithm is executed by a processor, the steps of the sewage treatment agent dosing optimization method based on an AI algorithm as described in any one of the above items are implemented.

[0054] The AI-based sewage treatment agent dosage optimization method, system, and medium disclosed in the present invention obtain sewage treatment process information by combining pollution identification levels with emission standard data, optimize process parameter information based on the calculation of the concentration deviation rate of the treatment agent addition point, and finally calculate the sewage treatment effectiveness index and perform threshold comparison to optimize the treatment agent dosage, thereby realizing intelligent optimization of sewage treatment agent dosage.

[0055] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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.

[0056] 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 across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0057] 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.

[0058] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware related to program instructions, and the aforementioned program may be stored in a readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0059] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as standalone products, they can also be stored on a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A sewage treatment agent dosing optimization method based on AI algorithm, characterized in that: The following steps are involved: Obtain sewage water quality characteristic information and purified water reuse information within a preset time period, extract sewage water quality characteristic data based on the sewage water quality characteristic information, and obtain emission standard data based on the purified water reuse information; Processing the sewage water quality characteristic data to obtain a pollution degree identification grade; Obtaining sewage treatment process information through a preset sewage treatment process information database according to the pollution degree identification level and emission standard data; Performing water treatment according to the sewage treatment process information, obtaining an average treatment agent concentration at a treatment agent addition point within a preset time period, comparing the average treatment agent concentration with an average preset standard treatment agent concentration, and obtaining a concentration deviation rate corresponding to the treatment agent addition point; Comparing the concentration deviation rate with a preset concentration deviation detection threshold, and processing the comparison result to obtain the optimized process parameter information corresponding to each dosing point; Perform water treatment according to the optimized process parameter information, obtain purified water quality parameter data corresponding to the process node, process the purified water quality parameter data with preset water quality parameter standard data, and obtain a sewage treatment effectiveness index; The sewage treatment effectiveness index is compared with a preset water treatment quality inspection threshold, and the treatment agent dosage is optimized according to the threshold comparison result.

2. The sewage treatment agent dosage optimization method based on AI algorithm according to claim 1 is characterized in that: The obtaining of sewage water quality characteristic information and purified water reuse information within a preset time period, extracting sewage water quality characteristic data based on the sewage water quality characteristic information, and obtaining emission standard data based on the purified water reuse information includes: Obtain sewage water quality characteristics information and purified water reuse information within a preset time period; Extracting sewage water quality characteristic data based on sewage water quality characteristic information, including pollutant category characteristic data and corresponding pollutant concentration data; According to the purified water reuse information, the preset purified water and emission standard relationship table is queried to obtain the emission standard data.

3. The sewage treatment agent dosage optimization method based on AI algorithm according to claim 2 is characterized in that: The processing according to the sewage water quality characteristic data to obtain the pollution degree identification level includes: According to the pollutant category characteristic data, a preset pollutant category weight list is searched to obtain the weight value corresponding to the pollutant category; Multiplying the pollutant concentration data by the corresponding weight value and summing them up to obtain a sewage pollution assessment index; Comparing the sewage pollution degree assessment index with a preset sewage standard pollution degree assessment index to obtain a relative pollution degree value; The relative value of the pollution degree is compared with a preset sewage identification threshold value, and the pollution degree identification level is obtained according to the range to which the threshold comparison result belongs.

4. The sewage treatment agent dosing optimization method based on AI algorithm according to claim 3 is characterized in that: The water treatment is performed according to the sewage treatment process information, an average treatment agent concentration at the treatment agent addition point within a preset time period is obtained, the average treatment agent concentration is compared with the average treatment agent concentration of a preset standard, and a concentration deviation rate corresponding to the treatment agent addition point is obtained, including: Perform water treatment according to the sewage treatment process information, and obtain the real-time concentration of the treatment agent corresponding to multiple points within a preset range of the treatment agent addition point within a preset time period; Obtain the distance data from the location point to the treatment agent addition point; Query a preset weight value list based on the distance data to obtain a weight value corresponding to the location point; Processing is performed based on the real-time concentration of the treatment agent and the corresponding weight value to obtain the average treatment agent concentration at the treatment agent addition point within a preset time period; The mean concentration of the treatment agent is compared with the mean concentration of the preset standard treatment agent to obtain the concentration deviation rate corresponding to the treatment agent addition point.

5. The sewage treatment agent dosing optimization method based on AI algorithm according to claim 4 is characterized in that: The concentration deviation rate is compared with a preset concentration deviation detection threshold, and the optimized process parameter information corresponding to each dosing point is obtained according to the comparison result, including: Comparing the concentration deviation rate with a preset concentration deviation detection threshold; If it is less than or equal to the preset concentration deviation test threshold, no action will be taken; If it is greater than the preset concentration deviation detection threshold, the preset treatment agent addition flow optimization method is used according to the concentration deviation rate to obtain the optimized amount of treatment agent addition flow corresponding to the addition point.

6. The sewage treatment agent dosage optimization method based on AI algorithm according to claim 5 is characterized in that: The water treatment is performed according to the optimized process parameter information, the purified water quality parameter data corresponding to the process node is obtained, the purified water quality parameter data is processed with the preset water quality parameter standard data, and the sewage treatment effectiveness index is obtained. include:, Perform water treatment according to the optimized process parameter information to obtain purified water quality parameter data corresponding to the process nodes, including pH value, COD data, BOD data, pathogen count, ammonia nitrogen content, and phosphorus content; The pH value, COD data, BOD data, pathogen quantity, ammonia nitrogen content and phosphorus content are respectively compared with the preset water quality parameter standard data to obtain relative values ​​of purified water quality parameters, including relative pH value, relative COD value, relative BOD value, relative pathogen quantity, relative ammonia nitrogen content and relative phosphorus content; The relative pH value, COD relative value, BOD relative value, pathogen quantity relative value, ammonia nitrogen content relative value and phosphorus content relative value are input into a preset sewage treatment effectiveness evaluation model for processing to obtain a sewage treatment effectiveness index.

7. The sewage treatment agent dosage optimization method based on AI algorithm according to claim 6 is characterized in that: The sewage treatment effectiveness index is compared with a preset water treatment quality inspection threshold, and the treatment agent dosage is optimized according to the threshold comparison result, including: Comparing the sewage treatment effectiveness index with a preset water treatment quality inspection threshold; If it is less than or equal to the first preset water treatment quality inspection threshold, then activate the response of increasing the amount of sewage treatment agent added; If it is greater than the first preset water treatment quality inspection threshold and less than or equal to the second preset water treatment quality inspection threshold, no action will be taken; If it is greater than the second preset water treatment quality inspection threshold, the response of reducing the amount of sewage treatment agent is activated.

8. The sewage treatment agent dosing optimization system based on AI algorithm is characterized by: The system comprises a memory and a processor, wherein the memory comprises a program of a sewage treatment agent dosing optimization method based on an AI algorithm, and when the program of the sewage treatment agent dosing optimization method based on an AI algorithm is executed by the processor, the following steps are implemented: Obtain sewage water quality characteristic information and purified water reuse information within a preset time period, extract sewage water quality characteristic data based on the sewage water quality characteristic information, and obtain emission standard data based on the purified water reuse information; Processing the sewage water quality characteristic data to obtain a pollution degree identification grade; Obtaining sewage treatment process information through a preset sewage treatment process information database according to the pollution degree identification level and emission standard data; Performing water treatment according to the sewage treatment process information, obtaining an average treatment agent concentration at a treatment agent addition point within a preset time period, comparing the average treatment agent concentration with an average preset standard treatment agent concentration, and obtaining a concentration deviation rate corresponding to the treatment agent addition point; Comparing the concentration deviation rate with a preset concentration deviation detection threshold, and processing the comparison result to obtain the optimized process parameter information corresponding to each dosing point; Perform water treatment according to the optimized process parameter information, obtain purified water quality parameter data corresponding to the process node, process the purified water quality parameter data with preset water quality parameter standard data, and obtain a sewage treatment effectiveness index; The sewage treatment effectiveness index is compared with a preset water treatment quality inspection threshold, and the treatment agent dosage is optimized according to the threshold comparison result.

9. The sewage treatment agent dosing optimization system based on AI algorithm according to claim 8 is characterized in that: The obtaining of sewage water quality characteristic information and purified water reuse information within a preset time period, extracting sewage water quality characteristic data based on the sewage water quality characteristic information, and obtaining emission standard data based on the purified water reuse information includes: Obtain sewage water quality characteristics information and purified water reuse information within a preset time period; Extracting sewage water quality characteristic data based on sewage water quality characteristic information, including pollutant category characteristic data and corresponding pollutant concentration data; According to the purified water reuse information, the preset purified water and emission standard relationship table is queried to obtain the emission standard data.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a sewage treatment agent dosing optimization method program based on an AI algorithm. When the sewage treatment agent dosing optimization method program based on an AI algorithm is executed by a processor, the steps of the sewage treatment agent dosing optimization method based on an AI algorithm as described in any one of claims 1 to 7 are implemented.

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