An intelligent decision optimization method for artificial intelligence

Abstract

The application discloses an intelligent decision optimization method for artificial intelligence, which comprises a data acquisition module, a control module and an output module, the data acquisition module is used for acquiring relevant information of wastewater treatment ponds and product production; the control module is used for controlling basic requirements of wastewater treatment through parameter information of the data acquisition module, and making real-time decisions on specific treatment modes of the wastewater treatment ponds; and the output module is used for implementing the treatment mode content of the control module. The application realizes real-time control on the treatment process of the wastewater treatment ponds, considers actual production conditions of the factory when selecting the treatment process, designs an efficient and energy-saving treatment scheme according to characteristics of the wastewater, solves the wastewater treatment problem through wastewater reuse for the enterprise, and considers treatment quality and treatment speed, so that environmental pollution is effectively controlled. The application has the characteristics of strong planning and decision-making ability and high precision of water quality control.

Description

Technical Field

[0001] This invention relates to the field of environmental management technology for industrial products, specifically to an intelligent decision-making optimization method oriented towards artificial intelligence. Background Technology

[0002] The production of industrial products requires a large amount of water resources, and wherever water is used, wastewater is generated. Wastewater needs to be treated in a timely manner, otherwise it can easily pollute other water bodies and cause water quality degradation.

[0003] In existing technologies, enterprises monitor the industrial wastewater treatment process in real time. Simultaneously, through an industrial wastewater treatment and reuse system, the wastewater is treated according to the process, ensuring it meets reuse standards again. This allows for complete recycling and reuse, saving significant amounts of valuable water resources and achieving zero discharge, thus protecting the environment. However, for enterprises engaged in wastewater treatment and reuse, especially during peak production periods, wastewater treatment ponds often experience supply shortages. The ponds frequently receive new batches of wastewater before the previous batch has been fully treated. Furthermore, strict quality control is crucial during wastewater reuse. Therefore, it is essential to design an intelligent decision-making optimization system and method based on artificial intelligence, characterized by strong planning and decision-making capabilities and high precision in water quality control. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent decision optimization method for artificial intelligence, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: an intelligent decision-making optimization system for artificial intelligence, comprising a data acquisition module, a control module, and an output module. The data acquisition module is used to acquire relevant information about wastewater treatment ponds and product production. The control module is used to control the basic requirements of wastewater treatment through the parameter information from the data acquisition module and to make real-time decisions on the specific treatment methods for the wastewater treatment ponds. The output module is used to implement the treatment methods specified by 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 acquire relevant information about the wastewater treatment pool; the production information acquisition module is used to acquire 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 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 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 product qualification rate and defective rate requirements of products using treated wastewater; the defective product utilization rate analysis submodule is used to control the utilization rate requirements of defective products using treated wastewater.

[0009] According to the above technical solution, the processing flow 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 for different volumes of the treatment tank. 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 solution, the specific information of the recyclable wastewater treatment tank obtained by the treatment tank information acquisition module includes: a deep treatment tank, a process control treatment tank, and a general treatment tank. The deep treatment tank, the process control treatment tank, and the general treatment tank are respectively located in three branches of the industrial drainage channel. The priority of industrial wastewater discharge is deep treatment tank > process control treatment tank > general treatment tank. The process control treatment tank is equipped with multiple branch valves by the process gate control module to control the wastewater entering the tank to undergo an additional wastewater treatment process on top of the basic treatment process of the general treatment tank. The wastewater treated in the general treatment tank and the wastewater in the process control treatment tank that does not meet the treatment standards are not used for secondary reuse.

[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 submodule uses the quality inspection results of the first batch of products to determine the standard level that the qualification rate and defect rate of the wastewater after product treatment need to reach.

[0013] Step S2: The product qualification rate analysis submodule further determines the standard level that the utilization rate of wastewater after the use and 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 to different product quality requirements under different processing pool volumes, and selects the appropriate processing flow based on 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 results of the first batch of products on that day, obtains the pass rate of the first batch of products as η%, and then the defect rate of the first batch of products is (1-η%). If both the pass rate and the defect rate are within the standard range, the information is recorded. The system continues to monitor the quality inspection results of subsequent batches of products, obtaining the pass rate as μ% and the defect rate as (1-μ%). Product parameters must be guaranteed. and , and , where γ1 is the maximum influence coefficient of processing water on product qualification rate, γ2 is the maximum influence coefficient of processing water on product defect rate, λ% is the target product qualification rate standard stored in the database, and (1-λ%) is the target product defect rate standard stored in the database.

[0016] According to the above technical solution, in step S2, the system further analyzes the defective products in the first batch of products obtained in step S1, and obtains the usable defective product ratio as β1%. The system then continues to monitor the quality inspection results of subsequent batches of products, obtaining the usable defective product rate as β2%. Product parameters must be guaranteed. .

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

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

[0019] Step S32: When the volume of the factory wastewater treatment tank is less than 80% of the maximum volume, the system predicts the total wastewater treatment time by extracting product production time and production water consumption information. Then, it obtains the ratio of qualified products to defective products in industrial production through the data acquisition module. After adapting and changing to meet the requirements of the defective product utilization rate analysis submodule, the product quality adaptation submodule controls the process control treatment tank to adapt and add processing processes and outputs the processing time. If the processing time is less than the predicted total processing time, it adapts and changes to meet the requirements of the defective product utilization rate analysis submodule, adapts and increases the number of process steps in the process control treatment tank and rematches them. The process control treatment tank's processing process is adjusted in real time to ensure that the error between the current estimated amount of wastewater to be treated and the predicted total processing time is less than 5%.

[0020] Step S33: When the wastewater treatment tank volume exceeds 80% of the maximum volume, the system predicts the total wastewater treatment time by extracting the product production rate. It then obtains the historical daily average wastewater treatment workload and wastewater treatment efficiency through the data acquisition module. Based on the historical daily average wastewater treatment workload and wastewater treatment efficiency, the system calculates the estimated time for industrial wastewater treatment. Combining this with the minimum treatment requirements from the product qualification rate analysis submodule, the system adjusts the process time of the process control tank in real time to meet the treatment requirements. The error between the estimated wastewater treatment completion rate to the minimum wastewater cleanliness requirement and the predicted total treatment time is less than 5%. If the wastewater treatment status under the current process control tank meets the minimum treatment requirements of the product qualification rate analysis submodule, the system proceeds to the deep treatment tank. Step S33 is terminated and the system jumps to step S32 when the total load of the process control tank is detected to be below 70% of the maximum load during wastewater treatment.

[0021] Step S34: When multiple production lines with different processes operate simultaneously and generate wastewater with different impurities, the wastewater treatment process selection submodule extracts the water quality requirements of different production lines through the production information acquisition module. Based on the water quality requirements, the production lines are ranked from highest to lowest. After wastewater treatment in steps S32-S33, the wastewater is ranked according to the wastewater treatment process in the deep water tank > process control treatment tank (from highest to lowest) to the process with the least wastewater treatment obtained by the product qualification rate analysis submodule. The wastewater treatment quality is then matched with the production lines in descending order of quality to obtain the matching degree of the selected production line. Where α1% is the ratio of the target production line's water requirements in the ranking, α2% is the ratio of the target treatment pool's wastewater treatment quality in the ranking, and W% is the ratio of the water volume already received and stored by the target production line. The water in the treatment pool will flow into the production line with the highest matching degree.

[0022] 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 pond in real time, takes into account the actual production situation of the factory when selecting the treatment process, and designs an efficient and energy-saving treatment solution based on the characteristics of the wastewater. This allows enterprises to solve the wastewater treatment problem through wastewater reuse while taking into account the treatment quality and speed, and effectively control environmental pollution. Attached Figure Description

[0023] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0024] Figure 1 This is a schematic diagram of the system module composition of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see Figure 1 This invention provides a technical solution: an intelligent decision-making optimization system and method for artificial intelligence, comprising:

[0027] The system comprises 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 pond and product production. The control module is used to control the basic requirements of wastewater treatment based on the parameter information from the data acquisition module and to make real-time decisions on the specific treatment methods of the wastewater treatment pond. The output module is used to implement the treatment methods specified by the control module.

[0028] This invention enables real-time monitoring of the wastewater treatment process, taking into account the actual production conditions of the factory when selecting the treatment process. Based on the characteristics of the wastewater, it designs a highly efficient and energy-saving treatment solution, allowing enterprises to solve wastewater treatment problems through wastewater reuse while ensuring treatment quality and speed, and effectively controlling environmental pollution.

[0029] 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 about 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.

[0030] 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 pool based on the analysis results of the product impact analysis module.

[0031] 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 utilization rate requirements of defective products using treated wastewater.

[0032] The 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 for different volumes of the treatment tank; 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.

[0033] The specific information of the recyclable wastewater treatment tanks acquired by the treatment tank information acquisition module includes: deep treatment tank, process control treatment tank, and ordinary treatment tank. The deep treatment tank, process control treatment tank, and ordinary treatment tank are located at three branches of the industrial drainage channel. The priority of industrial wastewater discharge is deep treatment tank > process control treatment tank > ordinary treatment tank. The process control treatment tank is equipped with multiple diversion valves by the process gate control module to control the wastewater entering the tank to undergo additional wastewater treatment on top of the basic treatment process of the ordinary treatment tank. The wastewater treated in the ordinary treatment tank and the wastewater in the process control treatment tank that does not meet the treatment standards are not used for secondary reuse.

[0034] In a preferred embodiment, the intelligent decision optimization method mainly includes the following steps:

[0035] Step S1: After the production line starts running, the product qualification rate analysis submodule uses the quality inspection results of the first batch of products to determine the standard level that the qualification rate and defect rate of the wastewater after product treatment need to reach.

[0036] Step S2: The product qualification rate analysis submodule further determines the standard level that the utilization rate of wastewater after the use and treatment of defective products needs to reach based on the quality inspection results of the first batch of products.

[0037] Step S3: The processing flow decision module adapts to different product quality requirements under different processing pool volumes, and selects the appropriate processing flow based on the quality requirements of the product quality adaptation sub-module.

[0038] In step S1 of this embodiment, the system extracts the quality inspection results of the first batch of products on that day, obtains the pass rate of the first batch of products as η%, and the defect rate of the first batch of products is (1-η%). If both the pass rate and the defect rate are within the standard range, the information is recorded. The system continues to monitor the quality inspection results of subsequent batches of products, obtaining the pass rate as μ% and the defect rate as (1-μ%). Since the quality of industrial water affects the quality of the products, the product parameters must be guaranteed. and , and , where γ1 is the maximum influence coefficient of processing water on product qualification rate, γ2 is the maximum influence coefficient of processing water on product defect rate, λ% is the target product qualification rate standard stored in the database, and (1-λ%) is the target product defect rate standard stored in the database.

[0039] Although differences in industrial water quality can affect product pass rate and defect rate, product quality is influenced by a variety of factors. In addition, industrial water requires basic treatment. Therefore, changes in industrial water quality are not enough to directly cause a significant change in product pass rate.

[0040] 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 usable defective product ratio as β1%. The system continues to monitor the quality inspection results of subsequent batches of products, and obtains the usable defective product ratio as β2%. Product parameters must be guaranteed. .

[0041] Although the quality of industrial water has little impact on product quality, the probability of contamination from untreated wastewater is relatively high due to the damaged protective layer of defective products. Therefore, monitoring the percentage of recyclable defective products in the wastewater treatment process can directly reflect the quality of wastewater treatment.

[0042] Step S3 in this embodiment further includes:

[0043] Step S31: The wastewater treatment tank information acquisition module remotely monitors the status of the factory wastewater treatment tanks online and obtains the total volume of each wastewater treatment tank in real time;

[0044] Step S32: When the volume of the factory wastewater treatment tank is less than 80% of the maximum volume, the system predicts the total wastewater treatment time by extracting product production time and production water consumption information. Then, it obtains the ratio of qualified products to defective products in industrial production through the data acquisition module. After adapting and changing to meet the requirements of the defective product utilization rate analysis submodule, the product quality adaptation submodule controls the process control treatment tank to adapt and add processing processes and outputs the processing time. If the processing time is less than the predicted total processing time, it adapts and changes to meet the requirements of the defective product utilization rate analysis submodule, adapts and increases the number of process steps in the process control treatment tank and rematches them. The process control treatment tank's processing process is adjusted in real time to ensure that the error between the current estimated amount of wastewater to be treated and the predicted total processing time is less than 5%.

[0045] When the volume of the factory's wastewater treatment pond is less than 80% of the maximum volume, the factory's wastewater treatment volume is in normal circulation.

[0046] This allows the factory to improve product quality while ensuring that the treatment time is within a controllable range, even when the pressure in the wastewater treatment tank is relatively low or during non-production periods, by achieving the required wastewater treatment quality that meets the requirements of the defective product utilization analysis submodule.

[0047] Meanwhile, since some production processes have lower requirements for wastewater quality compared to other processes with stricter requirements, and wastewater from different treatment processes needs to be evenly distributed to various production lines, once the treatment quality of a portion of the wastewater meets the requirements of the defective product utilization rate analysis submodule, further improving the wastewater treatment process has little effect on improving product quality in production lines with relatively lower water quality requirements. By adjusting the process control of the treatment tank in real time, the treatment cost of the treatment tank can be saved to a certain extent, maximizing the company's profits while ensuring product quality.

[0048] Step S33: When the wastewater treatment tank volume exceeds 80% of the maximum volume, the system predicts the total wastewater treatment time by extracting the product production rate. It then obtains the historical daily average wastewater treatment workload and wastewater treatment efficiency through the data acquisition module. Based on the historical daily average wastewater treatment workload and wastewater treatment efficiency, the system calculates the estimated time for industrial wastewater treatment. Combining this with the minimum treatment requirements from the product qualification rate analysis submodule, the system adjusts the process time of the process control tank in real time to meet the treatment requirements. The error between the estimated wastewater treatment completion rate to the minimum wastewater cleanliness requirement and the predicted total treatment time is less than 5%. If the wastewater treatment status under the current process control tank meets the minimum treatment requirements of the product qualification rate analysis submodule, the system proceeds to the deep treatment tank. Step S33 is terminated and the system jumps to step S32 when the total load of the process control tank is detected to be below 70% of the maximum load during wastewater treatment.

[0049] When the wastewater treatment pressure in the wastewater treatment tank is high, the processing decision module prioritizes measures to ensure the basic needs of wastewater treatment are met, thereby maintaining the wastewater treatment pressure at a normal level. It also adjusts the treatment load of the wastewater treatment tank in real time to improve the pass rate of wastewater treatment quality. After the peak wastewater treatment period has passed, it promptly adjusts to improve the wastewater treatment process and enhance the wastewater treatment quality, thereby increasing the pass rate and reuse rate of products.

[0050] Step S34: When multiple production lines with different processes operate simultaneously and generate wastewater with different impurities, the wastewater treatment process selection submodule extracts the water quality requirements of different production lines through the production information acquisition module. Based on the water quality requirements, the production lines are ranked from highest to lowest. After wastewater treatment in steps S32-S33, the wastewater is ranked according to the wastewater treatment process in the deep water tank > process control treatment tank (from highest to lowest) to the process with the least wastewater treatment obtained by the product qualification rate analysis submodule. The wastewater treatment quality is then matched with the production lines in descending order of quality to obtain the matching degree of the selected production line. Where α1% is the ratio of the target production line's water requirements in the ranking, α2% is the ratio of the target treatment pool's wastewater treatment quality in the ranking, and W% is the ratio of the water volume already received and stored by the target production line. The water in the treatment pool will flow into the production line with the highest matching degree.

[0051] With multiple production lines operating with different processes, the water quality produced by different production lines varies, and the system treats wastewater in different ways. This results in differences in the quality of the treated wastewater after different treatment processes. The wastewater treatment process selection submodule adapts the wastewater under different treatment processes to the most suitable production line, ensuring that the water after wastewater treatment can be matched according to the requirements of the product impact analysis module. This increases the reliability of wastewater treatment and improves the controllable qualification rate of products.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or pool that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or pool.

[0053] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended 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 make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should 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, the quality inspection results of the first batch of products are used to determine the standard level that the pass rate and defect rate of the wastewater after product treatment need to be achieved. Step S2: Based on the quality inspection results of the first batch of products, further determine the standard level that the utilization rate of wastewater after the use and treatment of defective products needs to reach; Step S3: Adapt different product quality requirements to different volumes in the treatment pool, and select the appropriate treatment process based on the product quality and the quality requirements. The specific information obtained about the recyclable wastewater treatment tanks includes: a deep treatment tank, a process control treatment tank, and a general treatment tank. The deep treatment tank, process control treatment tank, and general treatment tank are located at three branches of the industrial drainage channel. The priority of industrial wastewater discharge is deep treatment tank > process control treatment tank > general treatment tank. The process control treatment tank is equipped with multiple diversion valves to control the wastewater entering the tank to undergo additional wastewater treatment processes on top of the basic treatment process in the general treatment tank. The wastewater treated in the general treatment tank and the wastewater in the process control treatment tank that does not meet the treatment standards are not used for secondary reuse. When the wastewater treatment volume of the factory is in normal circulation, and the wastewater treatment tank is in a non-production stage, the treatment time is within a controllable range, and the quality of wastewater treatment is made to meet the requirements. Since the production process has lower requirements for the quality of wastewater than other production processes, and the wastewater after different treatment processes is evenly distributed to each production line, once the treatment quality of a part of the wastewater meets the requirements, further improving the steps of the wastewater treatment process has little effect on improving the product quality of the production line with relatively low water quality requirements. Therefore, the process control of the treatment tank is adjusted in real time. Step S3 further includes: Step S31: Remotely monitor the status of the factory wastewater treatment ponds online and obtain the total volume of each wastewater treatment pond in real time; Step S32: When the volume of the factory wastewater treatment tank is less than 80% of the maximum volume of the factory wastewater treatment tank, the system predicts the total wastewater treatment time by extracting product production time and production water consumption information, and then obtains the ratio of qualified products to defective products in industrial production. After the adaptation meets the requirements, the control process control treatment tank is adjusted to add treatment processes and outputs the treatment time. If the treatment time is less than the predicted total treatment time, the adaptation meets the requirements, the number of process steps in the control process treatment tank is increased and rematched, and the treatment process of the control process treatment tank is adjusted in real time to ensure that the error between the current expected treatment of all wastewater volume and the analysis and prediction of the total treatment time is less than 5%. Step S33: When the wastewater treatment tank volume exceeds 80% of the maximum volume, the system predicts the total wastewater treatment time by extracting the product production rate, and then obtains the historical average daily wastewater treatment workload and wastewater treatment cleaning efficiency after treatment. Based on the historical average daily wastewater treatment workload and wastewater treatment cleaning efficiency, the estimated time for industrial wastewater treatment is calculated. Combined with the minimum treatment requirements for wastewater treatment, the process time of the process control tank is adjusted in real time to meet the treatment requirements. The error between the estimated wastewater treatment completion rate to the minimum wastewater cleaning rate requirement and the total treatment time predicted by the analysis is less than 5%. If the wastewater treatment situation under the current process control tank meets the minimum treatment requirements, it enters the deep treatment tank. Until the total load of the process control tank is detected to be lower than 70% of the maximum load of the process control tank during wastewater treatment, step S33 is stopped and step S32 is executed.

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