Intelligent decision optimization system and method oriented to artificial intelligence

Abstract

The invention discloses an artificial intelligence-oriented intelligent decision optimization system and method, and the system comprises a data collection module, a control module and an output module, and the data collection module is used for obtaining the related information of a wastewater treatment pool and product production; the control module is used for controlling basic requirements of wastewater treatment according to the parameter information of the data acquisition module and making a real-time decision on a specific treatment mode of the wastewater treatment pool; according to the invention, the treatment process of the wastewater treatment tank is controlled in real time, the actual production condition of a factory is considered when the treatment process is selected, and an efficient and energy-saving treatment scheme is designed according to the characteristics of wastewater, so that the treatment efficiency is improved. The method gives consideration to the treatment quality and the treatment speed while solving the problem of wastewater treatment through wastewater reutilization for enterprises, effectively treats environmental pollution, and has the characteristics of high planning and decision-making capability and high water quality control precision.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental management of industrial products, and in particular to an intelligent decision-making optimization system and method oriented to artificial intelligence. Background Art

[0002] The production and manufacturing of industrial products requires a large amount of water resources. As long as water is used, wastewater will be generated. Wastewater needs to be treated in a timely manner, otherwise it is easy to pollute other water bodies and cause harm to water quality.

[0003] In the existing technology, enterprises monitor the treatment process of industrial wastewater in real time, and at the same time, the industrial wastewater is treated according to the process through the industrial wastewater treatment and reuse system, so that the wastewater meets the use standards again, and the wastewater can be fully recycled and continued to be used, which not only saves a lot of precious water resource costs, but also achieves zero discharge of wastewater, which is beneficial to environmental protection. However, for enterprises that treat and reuse wastewater, especially during the peak period of tight production rhythm, there is often a situation where the wastewater treatment pool is in short supply. The treatment pool often has not finished treating the previous batch of wastewater, and the next batch of wastewater that needs to be treated comes one after another; at the same time, while reusing wastewater, the quality of wastewater treatment must also be strictly controlled. Therefore, it is very necessary to design an intelligent decision-making optimization system and method for artificial intelligence with strong planning and decision-making capabilities and high water quality control accuracy. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent decision-making optimization system and method for artificial intelligence to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent decision-making optimization system for artificial intelligence, comprising a data acquisition module, a control module and an output module, wherein the data acquisition module is used to obtain relevant information of the wastewater treatment pool and product production; the control module is used to control the basic requirements of wastewater treatment through the parameter information of the data acquisition module, and make real-time decisions on the specific treatment method of the wastewater treatment pool; the output module is used to implement the treatment method content of the control module.

[0006] According to the above technical solution, the data acquisition module includes a treatment pool information acquisition module and a production information acquisition module. The treatment pool information acquisition module is used to obtain relevant information of the wastewater treatment pool; the production information acquisition module is used to obtain relevant parameters of the product production line and the quality inspection results of the product.

[0007] According to the above technical solution, the control module includes a product impact analysis module and a processing flow decision module. The product impact analysis module is used to analyze and control the product quality inspection results caused by different water qualities; the processing flow decision module is used to make real-time decisions on the processing flow of the treatment pool based on the analysis results of the product impact analysis module.

[0008] According to the above technical solution, the product impact analysis module further includes a product qualification rate analysis submodule and a defective product utilization rate analysis submodule. The product qualification rate analysis submodule is used to control the qualification rate and defective rate requirements of products using treated wastewater; the defective product utilization rate analysis submodule is used to control the availability requirements of defective products using treated wastewater.

[0009] According to the above technical scheme, the treatment process decision module includes a product quality adaptation submodule and a wastewater treatment process selection submodule. The product quality adaptation submodule is used to adapt different product quality requirements to different volume conditions of the treatment pool; the wastewater treatment process selection submodule is used to select a suitable treatment process according to the quality requirements of the product quality adaptation submodule.

[0010] According to the above technical scheme, the specific information of the recyclable wastewater treatment pool obtained by the treatment pool information acquisition module includes: deep treatment pool, process control treatment pool and ordinary treatment pool. The deep treatment pool, process control treatment pool and ordinary treatment pool are respectively located in the three diversions of the industrial drainage channel. The drainage priority of industrial wastewater is deep treatment pool>process control treatment pool>ordinary treatment pool. Among them, the process control treatment pool is equipped with multiple diversion valves by the process gate control module to control the incoming wastewater to undergo additional wastewater treatment processes on the basic treatment process of the ordinary treatment pool. The wastewater treated in the ordinary treatment pool and the wastewater that does not meet the treatment standards in the process control treatment pool do not participate in secondary utilization.

[0011] According to the above technical solution, the intelligent decision-making optimization method mainly includes the following steps:

[0012] Step S1: After the production line starts running, the product qualification rate analysis submodule determines the standard level that the qualified rate and defective rate of the treated wastewater used by the product need to reach based on the quality inspection results of the first batch of products;

[0013] Step S2: The product qualification rate analysis submodule further formulates the standard level that the utilization rate of the wastewater after treatment of defective products needs to reach based on the quality inspection results of the first batch of products;

[0014] Step S3: The processing flow decision module adapts different product quality requirements to different volumetric quantities of the processing pool, and selects a suitable processing flow according to the quality requirements of the product quality adaptation submodule.

[0015] According to the above technical solution, in step S1, the system extracts the quality inspection result information of the first batch of products of the day, and obtains the qualified rate of the first batch of products as η%, then the defective rate of the first batch of products is (1-η%), if the qualified rate and the defective rate of the product are within the standard line, the information is included; continue to monitor the quality inspection results of subsequent batches of products, obtain the qualified rate of the product as μ%, and the ratio of defective products as (1-μ%), the product parameters must ensure μ% ≥ γ1*η% and μ% ≥ λ%, (1-μ%) ≤ γ2*(1-η%) and (1-μ%) ≥ (1-λ%), wherein γ1 is the maximum influence coefficient of processing water on the product qualified rate, γ2 is the maximum influence coefficient of processing water on the product defective rate, λ% is the target product qualified rate standard stored in the database, and (1-λ%) is the target product defective rate standard stored in the database.

[0016] According to the above technical solution, in step S2, the system continues to analyze the defective products in the first batch of products obtained in step S1, and obtains the ratio of defective products that can be utilized in the defective products as β1%. The quality inspection results of subsequent batches of products are continued to be monitored, and the utilization rate of defective products in the products is β2%. The product parameters must ensure that β2% ≥ 0.7β1%.

[0017] According to the above technical solution, step S3 further includes:

[0018] Step S31: The treatment pool information acquisition module remotely monitors the status of the factory wastewater treatment pool online and obtains the total volume of each wastewater treatment pool in real time;

[0019] Step S32: When the volume of the factory wastewater treatment pool is lower than 80% of the maximum volume of the factory wastewater treatment pool, the system predicts the total wastewater treatment time by extracting the product production time and production water consumption information, and then obtains the ratio of qualified products to defective products in industrial production through the data acquisition module. After the adaptation changes to meet the requirements of the defective product utilization analysis submodule, the product quality adaptation submodule controls the process control treatment pool to adapt and increase the processing flow and then outputs the processing time. If the processing time is less than the predicted total processing time, the adaptation changes to meet the requirements of the defective product utilization analysis submodule, adapt to increase the number of process steps of the process control treatment pool and re-match, and adjust the processing flow of the process control treatment pool in real time to meet the current estimated total processing time error of less than 5% for all wastewater volume and analysis prediction;

[0020] Step S33: When the volume of the factory wastewater treatment pool is higher than 80% of the maximum volume of the factory wastewater treatment pool, the system predicts the total wastewater treatment time by extracting the rate of product production, and then obtains the historical average daily wastewater treatment workload and wastewater treatment cleaning efficiency after the wastewater is treated through the data acquisition module; the estimated time for industrial wastewater treatment is calculated based on the historical average daily wastewater treatment workload and wastewater treatment cleaning efficiency, and the process time of the process control treatment pool is adjusted in real time to meet the treatment requirements in combination with the minimum treatment requirement information of the product qualification rate analysis submodule for wastewater treatment. The error between the estimated wastewater treatment completion degree to the minimum wastewater cleaning degree requirement and the total treatment time predicted by the analysis is less than 5%. If the wastewater treatment situation under the treatment process of the current process control treatment pool meets the minimum treatment requirement of the product qualification rate analysis submodule, the deep treatment pool is entered; until the total load of the process control treatment pool is monitored to be lower than 70% of the maximum load of the process control treatment pool during the wastewater treatment, the execution of step S43 is terminated and the execution of step S42 is jumped;

[0021] Step S34: When multiple production lines with different processes are running at the same time and produce wastewater with different impurities, the wastewater treatment process selection submodule extracts the water quality requirements of different production lines through the production information collection module, and ranks the production lines from high to low according to the water quality requirements. After the wastewater is treated through steps S32-S33, the wastewater is ranked from high to low according to the deep water tank wastewater treatment> process control treatment pool wastewater treatment process from high to low to the product qualification rate analysis submodule to obtain the wastewater with the least process, and the wastewater treatment quality is ranked from high to low and paired with the production line in turn to obtain the matching degree of the selected production line.

[0022] X=(1-W%)|α1%-α2%|*100%, where α1% is the ratio of the water requirement of the matching target production line in the ranking, α2% is the ratio of the wastewater treatment quality of the matching target treatment pool in the ranking, and W% is the ratio of the water volume that has been received and stored by the matching target production line. The water in the treatment pool will flow into the production line with the highest matching degree.

[0023] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention controls the treatment process of the wastewater treatment pool in real time, takes into account the actual production situation of the factory itself when selecting the treatment process, and designs efficient and energy-saving treatment plans based on the characteristics of the wastewater, so that the enterprise itself can solve the wastewater treatment problem through wastewater reuse while taking into account the treatment quality and treatment speed, thereby effectively controlling environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0025] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1 The present invention provides a technical solution: an intelligent decision-making optimization system and method for artificial intelligence, comprising:

[0028] Data acquisition module, control module and output module. The data acquisition module is used to obtain relevant information about the wastewater treatment pool and product production; the control module is used to control the basic requirements of wastewater treatment through the parameter information of the data acquisition module, and make real-time decisions on the specific treatment method of the wastewater treatment pool; the output module is used to implement the treatment method content of the control module.

[0029] The present invention controls the treatment process of the wastewater treatment pool in real time, takes into account the actual production situation of the factory itself when selecting the treatment process, and designs an efficient and energy-saving treatment plan based on the characteristics of the wastewater. This allows the enterprise to solve the problem of wastewater treatment through wastewater reuse while taking into account the treatment quality and speed, thereby effectively controlling environmental pollution.

[0030] The data acquisition module includes a treatment pool information acquisition module and a production information acquisition module. The treatment pool information acquisition module is used to obtain relevant information of the wastewater treatment pool; the production information acquisition module is used to obtain relevant parameters of the product production line and product quality inspection results.

[0031] The control module includes a product impact analysis module and a processing flow decision module. The product impact analysis module is used to analyze and control the product quality inspection results caused by different water qualities; the processing flow decision module is used to make real-time decisions on the processing flow of the treatment pool based on the analysis results of the product impact analysis module.

[0032] The product impact analysis module further includes a product qualification rate analysis submodule and a defective product utilization rate analysis submodule. The product qualification rate analysis submodule is used to control the qualification rate and defective rate requirements of products using treated wastewater; the defective product utilization rate analysis submodule is used to control the availability requirements of defective products using treated wastewater.

[0033] The treatment process decision module includes a product quality adaptation submodule and a wastewater treatment process selection submodule. The product quality adaptation submodule is used to adapt different product quality requirements to different treatment pool volumes; the wastewater treatment process selection submodule is used to select a suitable treatment process based on the quality requirements of the product quality adaptation submodule.

[0034] The specific information of the recyclable wastewater treatment pool obtained by the treatment pool information acquisition module includes: deep treatment pool, process control treatment pool and ordinary treatment pool. The deep treatment pool, process control treatment pool and ordinary treatment pool are respectively located in the three diversions of the industrial drainage channel. The drainage priority of industrial wastewater is deep treatment pool>process control treatment pool>ordinary treatment pool. Among them, the process control treatment pool is equipped with multiple diversion valves by the process gate control module to control the incoming wastewater to undergo additional wastewater treatment processes on the basic treatment process of the ordinary treatment pool. The wastewater treated in the ordinary treatment pool and the wastewater that does not meet the treatment standards in the process control treatment pool will not be used for secondary utilization.

[0035] In a preferred embodiment, the intelligent decision-making optimization method mainly includes the following steps:

[0036] Step S1: After the production line starts running, the product qualification rate analysis submodule determines the standard level that the qualified rate and defective rate of the treated wastewater used by the product need to reach based on the quality inspection results of the first batch of products;

[0037] Step S2: The product qualification rate analysis submodule further formulates the standard level that the utilization rate of the wastewater after treatment of defective products needs to reach based on the quality inspection results of the first batch of products;

[0038] Step S3: The processing flow decision module adapts different product quality requirements to different volumetric quantities of the processing pool, and selects a suitable processing flow according to the quality requirements of the product quality adaptation submodule.

[0039] In step S1 of the present embodiment, the system extracts the quality inspection result information of the first batch of products of the day, and obtains the qualified rate of the first batch of products as η%, then the defective rate of the first batch of products is (1-η%), if the qualified rate and the defective rate of the product are within the standard line, the information is included; continue to monitor the quality inspection results of subsequent batches of products, obtain the qualified rate of the product as μ%, and the ratio of defective products as (1-μ%), because the quality of industrial water will affect the quality of the product, the product parameters must ensure μ% ≥ γ1*η% and μ% ≥ λ%, (1-μ%) ≤ γ2*(1-η%) and (1-μ%) ≥ (1-λ%), wherein γ1 is the maximum influence coefficient of processing water on the product qualified rate, γ2 is the maximum influence coefficient of processing water on the product defective rate, λ% is the target product qualified rate standard stored in the database, and (1-λ%) is the target product defective rate standard stored in the database.

[0040] Although the difference in the quality of industrial water will affect the qualified rate and defective rate of products, the quality of products is affected by many factors. At the same time, the use of industrial water also requires basic treatment. Therefore, the change in the quality of industrial water is not enough to directly cause a huge change in the qualified rate of products.

[0041] In step S2 of this embodiment, the system continues to analyze the defective products in the first batch of products obtained in step S1, and obtains the ratio of defective products that can be utilized in the defective products as β1%. The system continues to monitor the quality inspection results of subsequent batches of products, and obtains the utilization rate of defective products in the products as β2%. The product parameters must ensure that β2% ≥ 0.7β1%.

[0042] Although the quality of industrial water will have little impact on the quality of the product, the protective layer of defective products is damaged, and the probability of undesirable impurities in incompletely treated wastewater contaminating the product is relatively high. Therefore, monitoring the ratio of recyclable defective products in defective products can intuitively reflect the quality of wastewater treatment.

[0043] In this embodiment, step S3 further includes:

[0044] Step S31: The treatment pool information acquisition module remotely monitors the status of the factory wastewater treatment pool online and obtains the total volume of each wastewater treatment pool in real time;

[0045] Step S32: When the volume of the factory wastewater treatment pool is lower than 80% of the maximum volume of the factory wastewater treatment pool, the system predicts the total wastewater treatment time by extracting the product production time and production water consumption information, and then obtains the ratio of qualified products to defective products in industrial production through the data acquisition module. After the adaptation changes to meet the requirements of the defective product utilization analysis submodule, the product quality adaptation submodule controls the process control treatment pool to adapt and increase the processing flow and then outputs the processing time. If the processing time is less than the predicted total processing time, the adaptation changes to meet the requirements of the defective product utilization analysis submodule, adapt to increase the number of process steps of the process control treatment pool and re-match, and adjust the processing flow of the process control treatment pool in real time to meet the current estimated total processing time error of less than 5% for all wastewater volume and analysis prediction;

[0046] When the volume of the factory wastewater treatment pool is lower than 80% of the maximum volume of the factory wastewater treatment pool, the factory's wastewater treatment volume is in a normal cycle.

[0047] Furthermore, when the pressure in the wastewater treatment pool is relatively low or in a non-production stage, the treatment time can be kept within a controllable range while the quality of wastewater treatment can be improved by meeting the requirements of the defective product utilization analysis submodule, thereby improving the product quality of the factory.

[0048] At the same time, since some production processes have lower quality requirements for wastewater than other production processes with strict requirements, and the wastewater after different treatment processes must be evenly distributed to each production line, when the treatment quality of a part of the wastewater meets the requirements of the defective product utilization analysis sub-module, further improving the steps of the wastewater treatment process will have little effect on the improvement of product quality in the production line with relatively low current water quality requirements. By adjusting the treatment process of the treatment pool in real-time, the treatment cost of the treatment pool is saved to a certain extent, and the company's profits are maximized while ensuring product quality.

[0049] Step S33: When the volume of the factory wastewater treatment pool is higher than 80% of the maximum volume of the factory wastewater treatment pool, the system predicts the total wastewater treatment time by extracting the rate of product production, and then obtains the historical average daily wastewater treatment workload and wastewater treatment cleaning efficiency after the wastewater is treated through the data acquisition module; the estimated time for industrial wastewater treatment is calculated based on the historical average daily wastewater treatment workload and wastewater treatment cleaning efficiency, and the process time of the process control treatment pool is adjusted in real time to meet the treatment requirements in combination with the minimum treatment requirement information of the product qualification rate analysis submodule for wastewater treatment. The error between the estimated wastewater treatment completion degree to the minimum wastewater cleaning degree requirement and the total treatment time predicted by the analysis is less than 5%. If the wastewater treatment situation under the treatment process of the current process control treatment pool meets the minimum treatment requirement of the product qualification rate analysis submodule, the deep treatment pool is entered; until the total load of the process control treatment pool is monitored to be lower than 70% of the maximum load of the process control treatment pool during the wastewater treatment, the execution of step S43 is terminated and the execution of step S42 is jumped;

[0050] When the wastewater treatment pressure in the wastewater treatment pool is high, the processing flow decision module gives priority to taking measures to ensure the basic needs of wastewater treatment to ensure that the wastewater treatment pressure is in a normal state, adjusts the processing burden of the wastewater treatment pool in real time, improves the qualified rate of wastewater treatment quality, and makes timely adjustments after the peak period of wastewater treatment to improve the wastewater treatment process, improve the effect of wastewater treatment quality, and improve the qualified rate and reuse rate of products.

[0051] Step S34: When multiple production lines with different processes are running at the same time and produce wastewater with different impurities, the wastewater treatment process selection submodule extracts the water quality requirements of different production lines through the production information collection module, and ranks the production lines from high to low according to the water quality requirements. After the wastewater is treated through steps S32-S33, the wastewater is ranked from high to low according to the deep water tank wastewater treatment> process control treatment pool wastewater treatment process from high to low to the product qualification rate analysis submodule to obtain the wastewater with the least process, and the wastewater treatment quality is ranked from high to low and paired with the production line in turn to obtain the matching degree of the selected production line.

[0052] X=(1-W%)|α1%-α2%|*100%, where α1% is the ratio of the water requirement of the matching target production line in the ranking, α2% is the ratio of the wastewater treatment quality of the matching target treatment pool in the ranking, and W% is the ratio of the water volume that has been received and stored by the matching target production line. The water in the treatment pool will flow into the production line with the highest matching degree.

[0053] When multiple different process production lines are running, due to the different water quality produced by different production lines, the system has different ways of treating wastewater, resulting in differences in the treatment quality of wastewater after treatment. The wastewater treatment process selection submodule adapts the wastewater under different wastewater treatments to the most suitable production line, ensuring that the water after wastewater process treatment can be matched with the requirements of the product impact analysis module, thereby increasing the reliability of wastewater treatment and improving the controllable qualified rate of products.

[0054] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or pool including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or pool.

[0055] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent decision-making optimization system for artificial intelligence, characterized by: It includes a data acquisition module, a control module and an output module. The data acquisition module is used to obtain relevant information about the wastewater treatment pool and product production; the control module is used to control the basic requirements of wastewater treatment through the parameter information of the data acquisition module, and make real-time decisions on the specific treatment method of the wastewater treatment pool; the output module is used to implement the treatment method content of the control module.

2. The intelligent decision-making optimization system for artificial intelligence according to claim 1, characterized in that: The data acquisition module includes a treatment pool information acquisition module and a production information acquisition module. The treatment pool information acquisition module is used to obtain relevant information of the wastewater treatment pool; the production information acquisition module is used to obtain relevant parameters of the product production line and product quality inspection results.

3. The intelligent decision-making optimization system for artificial intelligence according to claim 2, characterized in that: The control module includes a product impact analysis module and a processing flow decision module. The product impact analysis module is used to analyze and control the product quality inspection results caused by different water qualities; the processing flow decision module is used to make real-time decisions on the processing flow of the treatment pool based on the analysis results of the product impact analysis module.

4. The intelligent decision-making optimization system for artificial intelligence according to claim 3, characterized in that: The product impact analysis module further includes a product qualification rate analysis submodule and a defective product utilization rate analysis submodule. The product qualification rate analysis submodule is used to control the qualification rate and defective rate requirements of products using treated wastewater; the defective product utilization rate analysis submodule is used to control the availability requirements of defective products using treated wastewater.

5. The intelligent decision-making optimization system for artificial intelligence according to claim 4, characterized in that: The treatment process decision module includes a product quality adaptation submodule and a wastewater treatment process selection submodule. The product quality adaptation submodule is used to adapt different product quality requirements to different volume conditions in the treatment pool; the wastewater treatment process selection submodule is used to select a suitable treatment process according to the quality requirements of the product quality adaptation submodule.

6. The intelligent decision-making optimization system for artificial intelligence according to claim 5, characterized in that: The specific information of the recyclable wastewater treatment pool obtained by the treatment pool information acquisition module includes: a deep treatment pool, a process control treatment pool and a general treatment pool. The deep treatment pool, the process control treatment pool and the general treatment pool are respectively located in three diversions of the industrial drainage channel. The drainage priority of industrial wastewater is deep treatment pool>process control treatment pool>general treatment pool. Among them, the process control treatment pool is provided with multiple diversion valves by the process gate control module to control the incoming wastewater to undergo additional wastewater treatment processes on the basic treatment process of the general treatment pool. The wastewater treated in the general treatment pool and the wastewater that does not meet the treatment standards in the process control treatment pool are not involved in secondary utilization.

7. An intelligent decision-making optimization method for artificial intelligence, characterized in that: The intelligent decision-making optimization method mainly includes the following steps: Step S1: After the production line starts running, the product qualification rate analysis submodule determines the standard level that the qualified rate and defective rate of the treated wastewater used by the product need to reach based on the quality inspection results of the first batch of products; Step S2: The product qualification rate analysis submodule further formulates the standard level that the utilization rate of the wastewater after treatment of defective products needs to reach based on the quality inspection results of the first batch of products; Step S3: The processing flow decision module adapts different product quality requirements to different volumetric quantities of the processing pool, and selects a suitable processing flow according to the quality requirements of the product quality adaptation submodule.

8. The intelligent decision-making optimization method for artificial intelligence according to claim 7, characterized in that: In the step S1, the system extracts the quality inspection result information of the first batch of products of the day, and obtains the qualified rate of the first batch of products as η%, then the defective rate of the first batch of products is (1-η%), if the qualified rate and the defective rate of the product are within the standard line, the information is included; continue to monitor the quality inspection results of subsequent batches of products, obtain the qualified rate of the product as μ%, and the ratio of defective products as (1-μ%), the product parameters must ensure μ% ≥ γ1*η% and μ% ≥ λ%, (1-μ%) ≤ γ2*(1-η%) and (1-μ%) ≥ (1-λ%), wherein γ1 is the maximum influence coefficient of processing water on the product qualified rate, γ2 is the maximum influence coefficient of processing water on the product defective rate, λ% is the target product qualified rate standard stored in the database, and (1-λ%) is the target product defective rate standard stored in the database.

9. The intelligent decision-making optimization method for artificial intelligence according to claim 8, characterized in that: In step S2, the system continues to analyze the defective products in the first batch of products obtained in step S1, and obtains the ratio of defective products that can be utilized in the defective products as β1%. The quality inspection results of subsequent batches of products are continued to be monitored, and the utilization rate of defective products in the products is obtained as β2%. The product parameters must ensure that β2% ≥ 0.7β1%.

10. The intelligent decision-making optimization method for artificial intelligence according to claim 9, characterized in that: The step S3 further comprises: Step S31: The treatment pool information acquisition module remotely monitors the status of the factory wastewater treatment pool online and obtains the total volume of each wastewater treatment pool in real time; Step S32: When the volume of the factory wastewater treatment pool is lower than 80% of the maximum volume of the factory wastewater treatment pool, the system predicts the total wastewater treatment time by extracting the product production time and production water consumption information, and then obtains the ratio of qualified products to defective products in industrial production through the data acquisition module. After the adaptation changes to meet the requirements of the defective product utilization analysis submodule, the product quality adaptation submodule controls the process control treatment pool to adapt and increase the processing flow and then outputs the processing time. If the processing time is less than the predicted total processing time, the adaptation changes to meet the requirements of the defective product utilization analysis submodule, adapt to increase the number of process steps of the process control treatment pool and re-match, and adjust the processing flow of the process control treatment pool in real time to meet the current estimated total processing time error of less than 5% for all wastewater volume and analysis prediction; Step S33: When the volume of the factory wastewater treatment pool is higher than 80% of the maximum volume of the factory wastewater treatment pool, the system predicts the total wastewater treatment time by extracting the rate of product production, and then obtains the historical average daily wastewater treatment workload and wastewater treatment cleaning efficiency after the wastewater is treated through the data acquisition module; the estimated time for industrial wastewater treatment is calculated based on the historical average daily wastewater treatment workload and wastewater treatment cleaning efficiency, and the process time of the process control treatment pool is adjusted in real time to meet the treatment requirements in combination with the minimum treatment requirement information of the product qualification rate analysis submodule for wastewater treatment. The error between the estimated wastewater treatment completion degree to the minimum wastewater cleaning degree requirement and the total treatment time predicted by the analysis is less than 5%. If the wastewater treatment situation under the treatment process of the current process control treatment pool meets the minimum treatment requirement of the product qualification rate analysis submodule, the deep treatment pool is entered; until the total load of the process control treatment pool is monitored to be lower than 70% of the maximum load of the process control treatment pool during the wastewater treatment, the execution of step S43 is terminated and the execution of step S42 is jumped; Step S34: When multiple production lines with different processes are running at the same time and produce wastewater with different impurities, the wastewater treatment process selection submodule extracts the water quality requirements of different production lines through the production information collection module, and ranks the production lines from high to low according to the water quality requirements. After the wastewater is treated through steps S32-S33, the wastewater is ranked from high to low according to the deep water tank wastewater treatment> process control treatment pool wastewater treatment process to the product qualification rate analysis submodule to obtain the least wastewater treatment process, and the wastewater treatment quality is ranked from high to low and paired with the production lines in turn to obtain the matching degree of the selected production line X=(1-W%)|α1%-α2%|*100%, where α1% is the ratio of the water requirement of the matching target production line in the ranking, α2% is the ratio of the wastewater treatment quality of the matching target treatment pool in the ranking, and W% is the ratio of the water volume that has been received and stored by the matching target production line, and the water in the treatment pool flows into the production line with the highest matching degree.

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