An intelligent decision-making optimization system and method for artificial intelligence

Abstract

The present invention discloses an intelligent decision-making optimization system and method for artificial intelligence, including a data acquisition module, a control module and an output module. The data acquisition module is used to obtain relevant information on wastewater treatment ponds 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 methods of the wastewater treatment ponds; the output module is used to implement the content of the treatment methods of the control module. By grasping the treatment process of the wastewater treatment ponds in real time, the present invention takes into account the actual production situation of its own factory when selecting the treatment process, designs an energy-efficient treatment plan according to the characteristics of the wastewater, enables the enterprise to solve the wastewater treatment problem through wastewater reuse while taking into account the treatment quality and treatment speed, effectively controls environmental pollution, and has the characteristics of strong planning and decision-making ability and high accuracy of water quality control.

Description

Technical Field

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

[0002] The production and manufacturing of industrial products require a large amount of water resources, and wastewater will be generated whenever water is used. The wastewater needs to be treated in a timely manner; otherwise, it is very easy to pollute other water bodies, causing harm to the reduction of water quality.

[0003] In the prior art, enterprises monitor the treatment process of industrial wastewater in real time. At the same time, through the industrial wastewater treatment and reuse system, the industrial wastewater is processed according to the process, so that the wastewater reaches the use standard again, and the wastewater can be fully recycled and reused. This not only saves a large amount of precious water resource costs, but also realizes zero discharge of wastewater, which is beneficial to environmental protection. However, for enterprises that carry out wastewater treatment and reuse, especially during the peak period with a tight production rhythm, the wastewater treatment pool often falls short of demand. Often, the previous batch of wastewater has not been completely treated in the treatment pool before the next batch of wastewater to be treated arrives; at the same time, while reusing the wastewater, the quality of wastewater treatment also needs to be strictly controlled. Therefore, it is very necessary to design an intelligent decision-making optimization system and method for artificial intelligence with strong design and planning 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 art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: An intelligent decision-making optimization system for artificial intelligence includes a data acquisition module, a control module, and an output module. 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 methods of the wastewater treatment pool; the output module is used to implement the content of the treatment method 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 products.

[0007] According to the above technical solution, the control module includes a product impact analysis module and a processing flow decision-making 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-making module is used to make real-time decisions on the processing flow of the treatment pool according to 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 sub-module and a defective product utilization rate analysis sub-module. The product qualification rate analysis sub-module is used to control the product qualification rate and defective rate requirements of products using the treated wastewater; the defective product utilization rate analysis sub-module is used to control the requirements for the available rate of defective products using the treated wastewater.

[0009] According to the above technical solution, the processing flow decision-making module includes a product quality adaptation sub-module and a wastewater treatment process selection sub-module. The product quality adaptation sub-module is used to adapt different product quality requirements under different volumes of the treatment pool; the wastewater treatment process selection sub-module is used to select a suitable treatment process according to the quality requirements of the product quality adaptation sub-module.

[0010] According to the above technical solution, the specific information of the recyclable wastewater treatment pool obtained by the treatment pool information collection 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 in three shunts of the industrial drainage channel. The industrial wastewater drainage priority 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 perform additional wastewater treatment processes on the basis of the basic treatment process of the general treatment pool. The treated wastewater in the general 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 optimization method mainly includes the following steps:

[0012] Step S1: After the production line starts running, the product qualification rate analysis sub-module formulates the standard levels that the product qualification rate and defective rate of products using the treated wastewater need to reach through the quality inspection results of the first batch of products;

[0013] Step S2: The product qualification rate analysis sub-module further formulates the standard levels that the available rate of defective products using the treated wastewater needs to reach through the quality inspection results of the first batch of products;

[0014] Step S3: The processing flow decision-making module adapts different product quality requirements under different volumes of the treatment pool and selects a suitable processing flow according to the quality requirements of the product quality adaptation sub-module.

[0015] According to the above technical solution, in step S1, the system extracts the quality inspection result information of the first batch of products on the same day. If the qualified rate of the first batch of products is η%, then the defective rate of the first batch of products is (1 - η%). If both the qualified rate and the defective rate of the products are within the standard line, the information is collected. Then, continue to monitor the quality inspection results of subsequent batches of products. If the qualified rate of the products is μ%, and the ratio of defective products is (1 - μ%), the product parameters need to ensure that μ% ≥ γ1 * η% and μ% ≥ λ%, (1 - μ%) ≤ γ2 * (1 - η%) and (1 - μ%) ≥ (1 - λ%), where 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. If the ratio of available defective products in the defective products is β1%, then continue to monitor the quality inspection results of subsequent batches of products. If the utilization rate of defective products in the products is β2%, the product parameters need to ensure that β2% ≥ 0.7β1%.

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

[0018] Step S31: The processing pool information collection 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 duration by extracting the product production time and water consumption information. Then, through the data collection module, it obtains the ratio of qualified and defective products in industrial production. After adapting to meet the requirements of the defective product utilization rate analysis sub-module, the product quality adaptation sub-module controls the process to adapt and increase the treatment process of the treatment pool and then outputs the treatment time. If the treatment time is less than the predicted total treatment time, adapt to meet the requirements of the defective product utilization rate analysis sub-module, adapt to increase the number of process steps of the treatment pool with process control and re-match, and adjust the treatment process of the treatment pool with process control in real time to ensure that the error between the current predicted total wastewater volume to be treated and the analysis prediction is less than 5%.

[0020] Step S33: When the accumulation volume in the factory wastewater treatment pool is higher than 80% of the maximum accumulation volume of the factory wastewater treatment pool, the system predicts the total wastewater treatment duration by extracting the production rate of product production, and then obtains the historical daily average wastewater treatment workload and the wastewater treatment cleaning efficiency after the wastewater is treated through the data acquisition module; calculates the estimated time for industrial wastewater treatment based on the historical daily average wastewater treatment workload and the wastewater treatment cleaning efficiency, combines the minimum treatment requirement information for wastewater treatment from the product qualification rate analysis sub-module, and adjusts the process time of the process control treatment pool in real time to meet the treatment requirements, with the predicted wastewater treatment completion degree reaching the minimum wastewater cleaning degree requirement and the error between the analyzed and predicted total treatment time being less than 5%. If the wastewater treatment situation under the current treatment process of the process control treatment pool meets the minimum treatment requirements of the product qualification rate analysis sub-module, then it enters the advanced treatment pool; until it is monitored during wastewater treatment that the total load of the process control treatment pool is lower than 70% of the maximum load of the process control treatment pool, step S43 is aborted and step S42 is jumped to for execution;

[0021] Step S34: When multiple production lines with different processes are running simultaneously and generating wastewater with different impurities, the wastewater treatment process selection sub-module extracts the water quality requirements of different production lines through the production information acquisition module, ranks the production lines from high to low according to the water quality requirements, and after treating the wastewater through steps S32 - S33, ranks the wastewater treatment quality from high to low according to the advanced pool wastewater treatment > the wastewater in the process control treatment pool is treated from high to low to the least process received by the product qualification rate analysis sub-module, and pairs the wastewater with the production lines in sequence 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 use requirements 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 already received and stored by the matching target production line. The water in the treatment pool is flowed 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, considers the actual production situation of its own factory when selecting the treatment process, designs an energy-efficient and environmentally friendly treatment plan based on the characteristics of the wastewater, takes into account both the treatment quality and treatment speed while solving the wastewater treatment problem through wastewater reuse for the enterprise itself, and effectively controls environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The 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 to the present invention. In the drawings:

[0025] Figure 1 It is a schematic diagram of the system modules of the present invention. Specific implementation manners

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] Please refer to Figure 1 , the present invention provides a technical solution: an intelligent decision-making optimization system and method for artificial intelligence, including:

[0028] A data acquisition module, a control module, and an output module. The data acquisition module is used to obtain relevant information on wastewater treatment ponds 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 methods of the wastewater treatment ponds; the output module is used to implement the content of the treatment methods of the control module.

[0029] The present invention controls the treatment process of the wastewater treatment pond in real time, considers the actual production situation of its own factory when selecting the treatment process, designs an energy-efficient and environmentally friendly treatment plan based on the characteristics of the wastewater, and takes into account both the treatment quality and speed while solving the wastewater treatment problem through wastewater reuse for the enterprise itself, effectively controlling environmental pollution.

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

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

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

[0033] The processing flow decision module includes a product quality adaptation sub-module and a wastewater treatment process selection sub-module. The product quality adaptation sub-module is used to adapt different product quality requirements under different volumes of the treatment pool; the wastewater treatment process selection sub-module is used to select an appropriate treatment process according to the quality requirements of the product quality adaptation sub-module.

[0034] The specific information of the recyclable wastewater treatment pool obtained by the treatment pool information collection module includes: the advanced treatment pool, the process control treatment pool, and the general treatment pool. The advanced treatment pool, the process control treatment pool, and the general treatment pool are respectively at three divergences of the industrial drainage channel. The industrial wastewater drainage priority is advanced treatment pool > process control treatment pool > general treatment pool. Among them, the process control treatment pool is provided with a plurality of diversion valves by the process gate control module to control the incoming wastewater to carry out additional wastewater treatment processes on the basis treatment process of the general treatment pool. The treated wastewater in the general treatment pool and the wastewater that does not meet the treatment standard in the process control treatment pool do not participate in secondary utilization.

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

[0036] Step S1: After the production line starts to run, the product qualification rate analysis sub-module formulates the standard levels that the qualification rate and the defective rate of the wastewater used after treatment of the product need to reach through the quality inspection results of the first batch of products;

[0037] Step S2: The product qualification rate analysis sub-module further formulates the standard levels that the utilization rate of the defective products using the treated wastewater needs to reach through the quality inspection results of the first batch of products;

[0038] Step S3: The processing flow decision module adapts different product quality requirements under different volumes of the treatment pool, and selects an appropriate treatment process according to the quality requirements of the product quality adaptation sub-module.

[0039] In step S1 of this embodiment, the system extracts the quality inspection result information of the first batch of products on the same day, and obtains that the qualification rate of the first batch of products is η%. Then the defective rate of the first batch of products is (1 - η%). If both the qualification rate and the defective rate of the products are within the standard line, the information is recorded; continue to monitor the quality inspection results of subsequent batches of products, and obtain that the qualification rate of the products is μ%, and the ratio of defective products of the products is (1 - μ%). Since the quality of industrial water will affect the quality of the products, the product parameters need to ensure that μ% ≥ γ1 * η% and μ% ≥ λ%, (1 - μ%) ≤ γ2 * (1 - η%) and (1 - μ%) ≥ (1 - λ%), where γ1 is the maximum influence coefficient of the processed water on the product qualification rate, γ2 is the maximum influence coefficient of the processed water on the product defective rate, λ% is the target product qualification rate standard stored in the database, and (1 - λ%) is the target product defective rate standard stored in the database.

[0040] Although the differences in the quality of industrial water can affect the qualified rate and defective rate of products, the quality of products is jointly affected by multiple 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 sufficient 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, obtains the ratio of available defective products in the defective products as β1%, continues to monitor the quality inspection results of subsequent batches of products, obtains the availability rate of defective products in the products as β2%, and the product parameters need to ensure that β2% ≥ 0.7β1%.

[0042] Although the quality of industrial water has little impact on the quality of products, due to the damaged protective layer of defective products, the probability of incomplete treatment of waste water contaminating products with bad impurities is relatively high. Therefore, monitoring the ratio of recyclable defective products in defective products can directly reflect the quality of waste water treatment.

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

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

[0045] Step S32: When the volume of the factory waste water treatment pool is lower than 80% of the maximum volume of the factory waste water treatment pool, the system predicts the total waste water treatment duration 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 collection module. After adapting to the requirements of the defective product utilization rate analysis sub-module, the product quality adaptation sub-module controls the process control processing pool to adapt and increase the processing process and then outputs the processing time. If the processing time is less than the predicted total processing time, adapt to the requirements of the defective product utilization rate analysis sub-module, adapt to increase the number of process steps of the process control processing pool and re-match, and adjust the processing process of the process control processing pool in real time to ensure that the error between the current estimated total waste water volume to be processed and the analyzed and predicted total processing time is less than 5%;

[0046] When the volume of the factory waste water treatment pool is lower than 80% of the maximum volume of the factory waste water treatment pool, the waste water treatment volume of the factory is in normal circulation.

[0047] Furthermore, it can be realized that when the pressure of the waste water treatment pool is relatively small or in the non-production stage, while ensuring that the processing time is within a controllable range, the quality of waste water treatment can also meet the requirements of the defective product utilization rate analysis sub-module, thereby improving the product quality of the factory.

[0048] At the same time, since the quality requirements for wastewater in some production processes are relatively lower than those in other production processes with stricter requirements, and the wastewater after different treatment processes needs to 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 rate analysis sub-module, further improving the steps of the wastewater treatment process has little effect on improving the product quality in the production line with relatively lower current water quality requirements. By adjusting the treatment process of the process control treatment tank in real time, the treatment cost of the treatment tank is saved to a certain extent, and the company's benefits are maximized on the premise of ensuring product quality.

[0049] Step S33: When the accumulated volume of the factory wastewater treatment tank is higher than 80% of the maximum accumulated volume of the factory wastewater treatment tank, the system predicts the total wastewater treatment duration by extracting the production rate of the product, and then obtains the historical daily average wastewater treatment workload and the wastewater treatment cleaning efficiency after the wastewater is treated through the data acquisition module; calculates the estimated time for industrial wastewater treatment based on the historical daily average wastewater treatment workload and the wastewater treatment cleaning efficiency, combines the minimum treatment requirement information of the wastewater treatment by the product qualification rate analysis sub-module, and adjusts the process time of the process control treatment tank in real time to meet the treatment requirements. The estimated wastewater treatment completion degree reaches the lowest wastewater cleaning degree requirement, and the error between the analyzed and predicted total treatment time is less than 5%. If the wastewater treatment situation meets the minimum treatment requirements of the product qualification rate analysis sub-module under the current treatment process of the process control treatment tank, it enters the deep treatment tank; until it is monitored during the wastewater treatment that the total load of the process control treatment tank is lower than 70% of the maximum load of the process control treatment tank, step S43 is aborted and step S42 is executed by jumping;

[0050] When the wastewater treatment pressure in the wastewater treatment tank is relatively high, the treatment process decision module preferentially takes measures to ensure the basic requirements of wastewater treatment to keep the wastewater treatment pressure in a normal state, adjusts the treatment burden of the wastewater treatment tank in real time, improves the qualification rate of wastewater treatment quality, and timely adjusts to the effect of improving the wastewater treatment process and improving the wastewater treatment quality after passing the peak period of wastewater treatment, so as to improve the qualification rate and reusability of products.

[0051] Step S34: When multiple production lines with different processes are running simultaneously and generating wastewater with different impurities, the wastewater treatment process selection sub-module extracts the water quality requirements of different production lines through the production information collection module, ranks the production lines from high to low according to the level of water quality requirements. After treating the wastewater through steps S32 - S33, the wastewater is ranked from high to low in terms of wastewater treatment quality according to the order of deep pool wastewater treatment > wastewater in the process control treatment tank being treated by the treatment process from high to low to the least-processed wastewater obtained by the product qualification rate analysis sub-module, and is paired with the production lines in turn to obtain the matching degree of the selected production lines.

[0052] X = (1 - W%)|α1% - α2%| * 100%, where α1% is the ranking ratio of meeting the water requirement of the target production line, α2% is the ranking ratio of meeting the wastewater treatment quality of the target treatment tank, and W% is the ratio of the water volume already received and stored in the target production line. The water in the treatment tank is flowed into the production line with the highest matching degree.

[0053] Under the operation of multiple different process production lines, due to the different water qualities generated by different production lines and the different ways of wastewater treatment by the system, the treatment qualities of the wastewater after different wastewater treatments will also be different. The wastewater treatment process selection sub-module ensures that the water after the wastewater treatment process can be matched with the wastewater according to the requirements of the product impact analysis module by adapting the wastewater under different wastewater treatments to the most suitable production line, increasing the reliability of the wastewater treatment and improving the controllable qualification rate of the product.

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

[0055] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. 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, set the standard levels that the qualified rate and defective rate of products using the treated wastewater need to reach based on the quality inspection results of the first batch of products; Step S2: Further set the standard levels that the available rate of defective products using the treated wastewater needs to reach based on the quality inspection results of the first batch of products; Step S3: Adapt different product quality requirements under different volumes of the treatment pool, and select appropriate treatment processes according to the product quality adaptation requirements; Among them, obtaining the specific information of the recyclable wastewater treatment pool includes: the advanced treatment pool, the process control treatment pool, and the general treatment pool. The advanced treatment pool, the process control treatment pool, and the general treatment pool are respectively at the three shunts of the industrial drainage channel. The industrial wastewater drainage priority is advanced treatment pool > process control treatment pool > general treatment pool. Among them, the process control treatment pool is provided with multiple shunt valves to control the incoming wastewater to perform additional wastewater treatment processes on the basis of the basic treatment process in the general treatment pool. The treated wastewater in the general treatment pool and the wastewater that does not meet the treatment standards in the process control treatment pool do not participate in secondary utilization; When the wastewater treatment volume of the factory is in normal circulation, when the wastewater treatment pool is in the non-production stage, ensure that the treatment time is within a controllable range, and make the quality of the wastewater treatment meet the requirements. Since the quality requirements of the production process for wastewater are relatively low compared to other production processes, and the wastewater after different treatment processes needs to be evenly distributed to each production line. When the treatment quality of a part of the wastewater meets the requirements, further improving the wastewater treatment process again has a relatively small impact on the product quality in the production line with relatively low current water quality requirements. Adjust the treatment process of the process control treatment pool in real time.

2. The intelligent decision-making optimization method for artificial intelligence according to claim 1, characterized in that: In the above-mentioned step S1, the system extracts the quality inspection result information of the first batch of products on the same day. If the qualified rate of the first batch of products is η%, then the defective rate of the first batch of products is ((1 - η%). If both the qualified rate and defective rate of the products are within the standard line, the information is recorded; continue to monitor the quality inspection results of subsequent batches of products. If the qualified rate of the products is μ%, and the ratio of defective products is ((1 - μ%), the product parameters need to ensure that μ% ≥ γ1 * η% and μ% ≥ λ%, (1 - μ%) ≤ γ2 * (1 - η%) and (1 - μ%) ≥ (1 - λ%). Among them, γ1 is the maximum influence coefficient of the processed water on the product qualified rate, γ2 is the maximum influence coefficient of the processed 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.

3. The intelligent decision-making optimization method for artificial intelligence according to claim 2, characterized in that: In the above-mentioned step S2, the system continues to analyze the defective products in the first batch of products obtained in step S1. If the available defective product ratio in the defective products is β1%, continue to monitor the quality inspection results of subsequent batches of products. If the available rate of defective products in the products is β2%, the product parameters need to ensure that β2% ≥ 0.7β1%.

4. An intelligent decision-making optimization method for artificial intelligence according to claim 3, characterized in that: The above-mentioned step S3 further includes: Step S31: Remotely and online monitor the status of the factory wastewater treatment pool, and obtain the total volume of each wastewater treatment pool in real time; Step S32: When the accumulated volume in the factory wastewater treatment tank is less than 80% of the maximum accumulated volume of the factory wastewater treatment tank, the system predicts the total wastewater treatment duration by extracting the product production time and water consumption information, then obtains the ratio of qualified products to defective products in industrial production. After the adaptation change meets the requirements, the control process controls the treatment tank to adapt and increase the treatment process and then outputs the treatment time. If the treatment time is less than the predicted total treatment time, and the adaptation change meets the requirements, the number of process steps of the process control treatment tank is adapted and increased and rematched, and the treatment process of the process control treatment tank is adjusted in real time to ensure that the error between the currently estimated total wastewater volume to be treated and the analyzed and predicted total treatment time is less than 5%; Step S33: When the accumulated volume in the factory wastewater treatment tank is higher than 80% of the maximum accumulated volume of the factory wastewater treatment tank, the system predicts the total wastewater treatment duration by extracting the production rate of the product, and then obtains the historical daily average wastewater treatment workload and wastewater treatment cleaning efficiency after the wastewater is treated; calculates the estimated time for industrial wastewater treatment based on the historical daily average wastewater treatment workload and wastewater treatment cleaning efficiency, and combines the minimum treatment requirement information for wastewater treatment to adjust the process time of the process control treatment tank in real time to meet the treatment requirements, ensuring that the error between the predicted wastewater treatment completion degree to the lowest wastewater cleaning degree requirement and the analyzed and predicted total treatment time is less than 5%. If the wastewater treatment situation meets the minimum treatment requirements under the current treatment process of the process control treatment tank, it will enter the advanced treatment tank; until it is monitored during wastewater treatment that the total load of the process control treatment tank is less than 70% of the maximum load of the process control treatment tank, step S43 is aborted and step S42 is executed by jumping; Step S34: When multiple production lines with different processes are running simultaneously and generating wastewater with different impurities, extract the water quality requirements of different production lines, rank the production lines from high to low according to the water quality requirements. After treating the wastewater through steps S32 - S33, rank the wastewater treatment quality from high to low according to the wastewater treatment in the deep pool > the wastewater in the process control treatment tank is treated by the process from high to low to the wastewater with the least process, and pair the wastewater with the production lines in order. Obtain the matching degree X of the selected production line = (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 tank in the ranking, and W% is the ratio of the water volume already received and stored by the matching target production line, and flow the water in the treatment tank into the production line with the highest matching degree.

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