Production management method of high-stability aluminum electrolytic capacitor electrolyte

Abstract

The invention discloses a production management method for a high-stability aluminum electrolytic capacitor electrolyte, and relates to the technical field of capacitors, and the method comprises the steps: obtaining a storage material data set; identifying a target production task, and calling target production formula information; formulating a production scheme to perform simulation preparation on the target electrolyte to obtain a simulation production monitoring data set; setting a production key index set, detecting the simulated production monitoring data set based on the production key index set, and generating a simulated production quality detection data set; and an optimization suggestion is generated by simulating the production quality detection data set, the production scheme is optimized through the optimization suggestion, and the production optimization scheme is executed to perform intelligent production management on the target electrolyte. The technical problems that production data cannot be effectively collected, processed and analyzed, the production efficiency is low and the quality control is unstable due to low electrolyte production management control precision in the prior art are solved, and the technical effect of improving the production efficiency and the production stability is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of capacitors, and in particular to a production management method for an electrolyte of a high-stability aluminum electrolytic capacitor. Background Art

[0002] With the rapid development of science and technology and the continuous growth of the global economy, aluminum electrolytic capacitors, as an indispensable key component in the electronics industry, are also experiencing an increasing market demand. However, the performance and quality of aluminum electrolytic capacitors are directly affected by the properties of their electrolytes. Therefore, the production management of electrolytes is crucial to improving the overall performance of aluminum electrolytic capacitors.

[0003] In the production process of aluminum electrolytic capacitor electrolyte, controlling the key components and impurity content in the electrolyte is the key to ensuring product quality and stability. However, the traditional electrolyte production management method has problems such as low efficiency and unstable quality control. Summary of the invention

[0004] The present application provides a production management method for high-stability aluminum electrolytic capacitor electrolyte, which is used to solve the technical problems of low efficiency and unstable quality control in the prior art.

[0005] In view of the above problems, the present application provides a production management method for high-stability aluminum electrolytic capacitor electrolyte.

[0006] In a first aspect of the present application, a production management method for a high-stability aluminum electrolytic capacitor electrolyte is provided, the method comprising: Retrieve material warehouse management data of aluminum electrolytic capacitor electrolyte to obtain a warehouse material data set; identify target production tasks, establish an electrolyte formula database based on the warehouse material data set, and retrieve target production formula information; formulate a production plan according to the target production formula information to simulate the preparation of the target electrolyte, and monitor the production process of the target electrolyte to obtain a simulated production monitoring data set; set a production key indicator set, detect the simulated production monitoring data set based on the production key indicator set, and generate a simulated production quality inspection data set of the target electrolyte; generate optimization suggestions through the simulated production quality inspection data set, optimize the production plan through the optimization suggestions, and execute the production optimization plan to perform intelligent production management on the target electrolyte.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application retrieves the material storage management data of the electrolyte of the aluminum electrolytic capacitor to obtain a storage material data set; identifies the target production task, establishes an electrolyte formula database based on the storage material data set, and retrieves the target production formula information; formulates a production plan according to the target production formula information to simulate the preparation of the target electrolyte, and monitors the production process of the target electrolyte to obtain a simulated production monitoring data set; sets a production key indicator set, detects the simulated production monitoring data set based on the production key indicator set, and generates a simulated production quality detection data set for the target electrolyte; generates optimization suggestions through the simulated production quality detection data set, optimizes the production plan through the optimization suggestions, and executes the production optimization plan to perform intelligent production management on the target electrolyte. The present invention solves the technical problems of low control accuracy of electrolyte production management in the prior art, which leads to the inability to effectively collect, process and analyze production data, low production efficiency, and unstable quality control. Through intelligent management, automatic replenishment and replacement of raw materials, as well as intelligent control and quality detection are realized, human intervention is reduced, and efficient control and optimization of the electrolyte process are realized, so as to achieve the technical effect of improving production efficiency and production stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of a production management method for a high-stability aluminum electrolytic capacitor electrolyte provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The present application provides a production management method for aluminum electrolytic capacitor electrolyte with high stability, aiming to solve the technical problems in the prior art of low accuracy of electrolyte production management and control, which leads to the inability to effectively collect, process and analyze production data, low production efficiency and unstable quality control. Through intelligent management, automatic replenishment and replacement of raw materials, as well as intelligent control and quality inspection are realized, manual intervention is reduced, and efficient control and optimization of electrolyte process are realized, thereby achieving the technical effect of improving production efficiency and production stability.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices. Embodiment 1

[0013] like Figure 1 As shown, the present application provides a production management method for a high-stability aluminum electrolytic capacitor electrolyte, the method comprising: Step S100: Retrieving material storage management data of aluminum electrolytic capacitor electrolyte to obtain a storage material data set; In the embodiment of the present application, the material warehouse management data of the aluminum electrolytic capacitor electrolyte stores the types and inventory quantities of raw materials used to produce the aluminum electrolytic capacitor electrolyte.

[0014] By retrieving the material storage management data of aluminum electrolytic capacitor electrolyte, a storage material data set is obtained. The storage material data set contains information such as the type and quantity of materials used to produce aluminum electrolytic capacitor electrolyte.

[0015] Step S200: identifying the target production task, establishing an electrolyte formula database based on the storage material data set, and retrieving the target production formula information; In an embodiment of the present application, a target production task is received from a customer order or an enterprise production plan, and the target production task includes information such as determining the type of electrolyte, production quantity, etc.

[0016] An electrolyte formula database is established by matching the historical formula data record archive of aluminum electrolytic capacitor electrolyte with the storage material data set.

[0017] According to the requirements of the target production task, a database query statement, such as a SELECT statement, is used to retrieve the corresponding target production formula information from the electrolyte formula database. The retrieved target production formula information includes detailed information such as a complete raw material list, the proportion of raw materials, and the order of addition.

[0018] Step S300: formulating a production plan according to the target production formula information to simulate the preparation of the target electrolyte, and monitoring the production process of the target electrolyte to obtain a simulated production monitoring data set; In an embodiment of the present application, according to the target production formula information, a process flow design software or tool, such as AutoCAD, SolidWorks, etc., is used to formulate an electrolyte production plan, including equipment configuration, process parameter setting, etc.

[0019] Next, simulation software, such as chemical simulation software, process simulation software, etc., is used to simulate the electrolyte preparation process in a virtual environment, including the steps of feeding, mixing, and reaction of raw materials, and record various data in the simulation process.

[0020] During the simulation preparation process, sensors and data acquisition systems, such as PLC and SCADA, are used to monitor the production environment, equipment status, process parameters, etc. in real time to obtain a simulated production monitoring data set.

[0021] Step S400: setting a production key indicator set, testing the simulated production monitoring data set based on the production key indicator set, and generating a simulated production quality detection data set of the target electrolyte; In the embodiment of the present application, a production key indicator set is set by a technical expert based on the performance requirements and production experience of the target electrolyte. The production key indicator set includes the concentration, purity, conductivity, pH value, viscosity, impurity content, etc. of the electrolyte.

[0022] Extract data related to key production indicators from the simulated production monitoring data set, match the extracted data with the set key production indicator set, use statistical analysis methods such as mean and standard deviation to calculate the extracted data, compare the calculation results with the corresponding key production indicators, determine whether the production quality meets the standards, and generate a simulated production quality inspection data set for the target electrolyte.

[0023] Step S500: Generate optimization suggestions through the simulated production quality inspection data set, optimize the production plan through the optimization suggestions, and execute the production optimization plan to perform intelligent production management on the target electrolyte.

[0024] In the examples of this application, statistical methods such as variance analysis and correlation analysis are used to identify key factors affecting product quality. The experience and knowledge of experts in the field are combined to analyze the abnormal points and potential problems in the data set. Based on the analysis results, optimization suggestions are generated, such as changing the temperature, pH value or raw material ratio.

[0025] According to the optimization suggestions, adjust the key parameters in the production process, such as temperature, pressure, stirring speed, etc., to complete the optimization of the production plan.

[0026] Finally, the MES or ERP system is used to implement the production optimization plan and realize the intelligent production management of the target electrolyte.

[0027] Furthermore, step S100 in the method provided in the application embodiment also includes: Setting minimum inventory based on target production plan information for aluminum electrolytic capacitor electrolyte; Comparing the material warehouse management data set with the minimum inventory quantity, and generating inventory warning information when there is material warehouse management data less than the minimum inventory quantity in the material warehouse management data set; Extracting production forecast results based on the target production plan information, and determining the material purchase quantity according to the inventory warning information combined with the production forecast results; Material purchase order information is constructed according to the material purchase quantity, and the material warehouse management data set is updated based on the material purchase order information to generate the warehouse material data set.

[0028] In the embodiment of the present application, the quantity of electrolyte to be produced and the type and quantity of production materials required for producing the electrolyte are determined according to the target production plan information of the aluminum electrolytic capacitor electrolyte. The quantity of electrolyte to be produced is multiplied by the quantity of production materials required to set the minimum inventory. The minimum inventory is the minimum inventory required to complete the target production plan.

[0029] Use data analysis tools such as Excel and SQL query to compare the material warehouse management data set with the set minimum inventory quantity to identify materials with inventory quantities lower than the minimum inventory quantity. When there are materials with inventory quantities lower than the minimum inventory quantity in the material warehouse management data set, an inventory warning message is generated. The inventory warning message includes the quantity of warehouse materials and which warehouse materials have not reached the minimum inventory quantity.

[0030] Next, the production forecast results are extracted based on the target production plan information. The production forecast results are the quantities of each material required for production. Based on the inventory warning information and the production forecast results, the material quantity in the production forecast results is subtracted from the quantity in the storage materials to calculate the material purchase quantity.

[0031] Construct material purchase order information based on the determined material purchase quantity. Material purchase order information includes material name, quantity, etc. Use ERP system or specialized purchase management software to create and manage purchase orders. When the material purchase order is executed and the goods are received, update the material warehouse management data set according to the actual quantity received. Use ERP system or inventory management software to record and manage material warehousing operations. The updated material warehouse management data set is the new warehouse material data set.

[0032] Furthermore, step S200 in the method provided in the application embodiment also includes: Based on the historical formula data record archive of aluminum electrolytic capacitor electrolyte, an initial electrolyte formula database is constructed; According to the target production task of aluminum electrolytic capacitor electrolyte, traverse the initial electrolyte formula database to determine the initial production formula information; Extracting a recipe material list in the initial production recipe information, setting a production material requirement information set based on the recipe material list, and determining whether the warehouse material data set conforms to the production material requirement information set; If the storage material data set meets the production material demand information set, outputting the initial electrolyte formula database as the electrolyte formula database; If the storage material data set does not conform to the production material requirement information set, a supplementary instruction is generated, and the initial production formula information is updated to the formula information to be generated through the supplementary instruction, and the initial electrolyte formula database is updated based on the formula information to be generated, and the electrolyte formula database is output.

[0033] In an embodiment of the present application, aluminum electrolytic capacitor electrolyte formula data is collected from a historical formula data record archive of aluminum electrolytic capacitor electrolyte, and the aluminum electrolytic capacitor electrolyte formula data includes a formula number, a formula material list, a material ratio, and an electrolyte performance parameter. The formula material list includes, for example, aluminum oxide, polyethylene glycol, etc. The material ratio is specific to a percentage. The extracted aluminum electrolytic capacitor electrolyte formula data is integrated to generate an initial electrolyte formula database.

[0034] Next, according to the target production task of aluminum electrolytic capacitor electrolyte, specific production volume and other requirements are extracted. According to the requirements of the production task, the formula information in the initial electrolyte formula database is screened and compared to find the initial production formula information that meets the conditions.

[0035] From the selected initial production formula information, the required formula material list is extracted, including the type and proportion of each material. According to the specific requirements of the target production task and the extracted formula material list, the total demand for each material is calculated to obtain the production material demand information set.

[0036] The production material demand information set is compared with the storage material data set to determine whether the inventory meets the production demand. If the storage material meets the requirements of the production material demand information set, the initial electrolyte formula database is directly output as the electrolyte formula database.

[0037] If the storage materials do not meet the requirements of the production material demand information set, a supplementary instruction is generated based on the comparison results. The supplementary instruction contains detailed information such as the name, specification, quantity, and demand time of the missing materials. The supplementary instruction is sent to the procurement department via email or other effective means. When the supplementary instruction is generated, the initial production formula information is marked as formula information to be generated, indicating that the current formula needs to wait for the material to be replenished before production can proceed.

[0038] After the materials are replenished, the initial electrolyte formula database is updated according to the formula information to be generated, and the electrolyte formula database is output.

[0039] Furthermore, a production plan is formulated according to the target production formula information to simulate the preparation of the target electrolyte, and the production process of the target electrolyte is monitored to obtain a simulated production monitoring data set. The method also includes: Parsing the production material demand information set to obtain production material type information, production material ratio information, and production preparation step information; According to the production preparation step information, the production material type information is prepared based on the production material ratio information to determine the production plan; Performing simulated preparation of the target electrolyte through the production scheme, and determining a simulated preparation process; Based on the simulated preparation process, multiple monitoring points are set to collect data to generate the simulated production monitoring data set.

[0040] In the embodiment of the present application, the production material type information is extracted from the production material demand information set. The production material ratio information and production preparation step information are extracted from the target production formula information. The production material ratio information is the amount or ratio of each material used in the production process. The production preparation step information is the entire preparation process of producing the electrolyte, including various steps such as mixing, stirring, heating, and cooling.

[0041] According to the production material ratio information, use the calculator software to calculate the exact ratio of each material to ensure the accuracy of the ratio. Determine the production process according to the production preparation step information. Ensure that each step is carried out in the predetermined order and conditions. Through the above process, determine the production plan. The production plan includes the materials that need to be added in each step, the material ratio and the production process.

[0042] Next, use chemical process simulation software, such as Aspen Plus, to simulate the preparation of the target electrolyte. When performing simulated preparation, determine the simulated preparation process according to the production process in the production plan. The simulated preparation process includes the amount of materials added during preparation, reaction temperature, stirring speed, reaction time, etc.

[0043] Then, the stirring points and material addition points in the simulated preparation process are used as monitoring points, and data is collected at each monitoring point to obtain a simulated production monitoring data set. The simulated production monitoring data set includes key parameters such as temperature, pressure, flow rate, stirring speed, etc. in the production process.

[0044] Furthermore, the key production indicators are set, and the method further comprises: Extracting a production key information set of a target electrolyte based on the simulated preparation process, wherein the production key information set includes production feature information and production quality requirement information; Identify and obtain multiple key quality factors based on the production feature information and the production quality requirement information; Analyze the historical production quality data set according to the multiple key quality factors to determine the target range; The plurality of key quality factors are weighted based on the target range, and the weighted distribution results are added to the production key indicator set.

[0045] In an embodiment of the present application, an electrolyte preparation simulation software is used to simulate the preparation process, and key information is extracted from the simulation results using data capture or an API interface to obtain a production key information set of the target electrolyte, wherein the production key information set includes production characteristic information and production quality requirement information. The production characteristic information includes process parameters and raw material information. The process parameters include mixing ratio, stirring speed, heating or cooling temperature and time, etc. The raw material information includes raw material type, purity, etc. The production quality requirement information includes the conductivity, stability, density, pH value, etc. of the electrolyte.

[0046] Next, technical experts determine the key quality factors of production characteristic information and production quality requirement information. Key quality factors include raw material purity, reaction temperature, stirring speed, reaction time, pressure, etc.

[0047] After that, the historical production quality data set is obtained from the historical production database, including the actual values ​​of key quality factors and the performance index values ​​of the produced electrolyte. When analyzing the historical production quality data set according to multiple key quality factors, technical experts will screen and confirm the main quality factors based on industry standards and expert experience, form a list of key quality factors, and set the target range for each key quality factor based on the production process and equipment conditions.

[0048] When assigning weights to multiple key quality factors, technical experts and front-line technicians score the key quality factors to reflect their relative importance. Based on the scoring results, the weight value of each key quality factor is calculated by averaging. Finally, the weight value of each key quality factor is combined with its target range to form a complete production key indicator, which is added to the production key indicator set.

[0049] Furthermore, step S400 in the method provided in the application embodiment also includes: Traversing the simulated production monitoring data set to match with the production key indicator set, and if the match is successful, setting a preset production quality threshold based on the production key indicator set; Determining whether the production quality of the first simulated production monitoring data set of the simulated production monitoring data set reaches the preset production quality threshold; If the production quality of the first simulated production monitoring data of the simulated production monitoring data set does not reach the preset production quality threshold, the production plan is deemed to be infeasible, and the first simulated production monitoring data is marked as unqualified, and a first test result is generated; The first test result is added to the simulated production quality test data set of the target electrolyte.

[0050] In the embodiment of the present application, each set of data in the simulated production monitoring data set is traversed using database query, data comparison algorithm, etc. The key quality factor values ​​in the simulated data are compared one by one with the target range in the production key indicator set. When there is a one-to-one correspondence between the monitoring data and the indicator, it is proved that the production key indicator set can fully detect the simulated production monitoring data set, and there is no situation where there is data in the simulated production detection data set that needs to judge the production quality but there is no corresponding indicator in the key indicator set. It is determined to be matched and the production key indicator set is used as the preset production quality threshold.

[0051] A set of data is randomly selected from the simulated production monitoring data set as the first simulated production monitoring data. The first simulated production monitoring data is compared with the preset production quality threshold. When the production quality of any of the first simulated production monitoring data does not reach the preset production quality threshold, the production plan is deemed infeasible, the first simulated production monitoring data is marked as unqualified, and the first test result is generated. The first test result contains information about which production quality does not reach the preset production quality threshold.

[0052] Finally, the first test result is added to the simulated production quality test data set of the target electrolyte.

[0053] Furthermore, step S500 in the method provided in the application embodiment also includes: Based on the simulated annealing algorithm, the simulated production quality inspection data set is used as input data, and a solution space is defined according to the production key indicator set; Introducing an objective function, performing random selection based on the solution space, determining a first initial solution, and calculating and obtaining an objective function value of the first initial solution; Generating a second initial solution by performing random perturbation on the solution space, and calculating an objective function value of the second initial solution; Compare the objective function value of the first initial solution with the objective function value of the second initial solution. If the objective function value of the first initial solution is less than the objective function value of the second initial solution, the second initial solution is considered to be better, and the first initial solution is replaced by the second initial solution. If the objective function value of the first initial solution is greater than or equal to the objective function value of the second initial solution, the first initial solution is considered to be better, and the first initial solution is retained. A preset number of iterations is set, and iterative judgments of random perturbations are repeated in the solution space until the number of iterations reaches the preset number of iterations, and the i-th initial solution is output as the optimal solution. The production parameters are analyzed based on the optimal solution, the production optimization parameters to be matched are generated, and the optimization suggestions are formulated.

[0054] In the embodiments of the present application, based on the production key indicator set, adjustable parameters in the production process, such as raw material ratio, reaction temperature setting value, stirring speed setting value, etc., are used as the solution space of the simulated annealing algorithm.

[0055] Next, we introduce the objective function, which is used to evaluate the quality of the solution. For example, the objective function is the weighted sum of the raw material ratio, the reaction temperature setting value, and the stirring speed. The weight distribution is set by the technical expert. Then, we randomly select an initial solution in the solution space as the first initial solution and calculate its objective function value.

[0056] Next, the first initial solution is randomly perturbed to generate the second initial solution. The purpose of the perturbation is to explore other possible solutions in the solution space. At the same time, the objective function value of the second initial solution is calculated. The objective function values ​​of the first initial solution and the second initial solution are compared.

[0057] If the objective function value of the first initial solution is less than the objective function value of the second initial solution, the second initial solution is considered to be better and the first initial solution is replaced by the second initial solution. If the objective function value of the first initial solution is greater than or equal to the objective function value of the second initial solution, the first initial solution is considered to be better and the first initial solution is retained. When no new solution is accepted for multiple consecutive iterations, the algorithm stops searching and outputs the current solution as the optimal solution.

[0058] Compare the parameters of the optimal solution with the actual production parameters to analyze the value of the production parameters. Generate the production optimization parameters to be matched through analysis, which are fine-tuning or major changes to the original production parameters. According to the optimized parameters, formulate production optimization suggestions to improve production efficiency and product quality. Optimization suggestions include adjusting key parameters in the production process, such as temperature, raw material ratio, stirring speed, etc.

[0059] Furthermore, the production plan is optimized by using the optimization suggestion, and the method further includes: Conduct a feasibility assessment based on the optimization suggestions and generate an execution assessment result; According to the execution evaluation result, matching the production plan with the optimization suggestion, and determining the production parameters to be optimized; The production parameters to be optimized are data aligned with the production optimization parameters to be matched, and according to the alignment result, the production parameters to be optimized are optimized by the production optimization parameters to be matched to determine the production optimization plan.

[0060] In the embodiment of the present application, each parameter adjustment in the optimization suggestion is first evaluated to see whether it meets the physical limitations of the production equipment, the supply of raw materials, and the production safety standards. Through the expert review method, experts in the production field are invited to review the optimization suggestions and obtain the execution evaluation results. The execution evaluation results are divided into execution or non-execution.

[0061] When the execution evaluation result is execution, the production plan is matched according to the optimization suggestion, and the production plan with the highest matching degree with the optimization suggestion is selected. In the selected production plan, the production parameters related to the optimization suggestion are identified and used as the production parameters to be optimized.

[0062] The production parameters to be optimized and the production optimization parameters to be matched are compared one by one to ensure the consistency of parameter name, unit, value range, etc. According to the alignment results, the production optimization parameters to be matched are applied to the production parameters to be optimized to determine the production optimization plan.

[0063] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: This application retrieves the material storage management data of the electrolyte of the aluminum electrolytic capacitor to obtain a storage material data set; identifies the target production task, establishes an electrolyte formula database based on the storage material data set, and retrieves the target production formula information; formulates a production plan according to the target production formula information to simulate the preparation of the target electrolyte, and monitors the production process of the target electrolyte to obtain a simulated production monitoring data set; sets a production key indicator set, detects the simulated production monitoring data set based on the production key indicator set, and generates a simulated production quality detection data set for the target electrolyte; generates optimization suggestions through the simulated production quality detection data set, optimizes the production plan through the optimization suggestions, and executes the production optimization plan to perform intelligent production management on the target electrolyte. The present invention solves the technical problems of low control accuracy of electrolyte production management in the prior art, which leads to the inability to effectively collect, process and analyze production data, low production efficiency, and unstable quality control. Through intelligent management, automatic replenishment and replacement of raw materials, as well as intelligent control and quality detection are realized, human intervention is reduced, and efficient control and optimization of the electrolyte process are realized, so as to achieve the technical effect of improving production efficiency and production stability.

[0064] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0066] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A production management method for a high-stability aluminum electrolytic capacitor electrolyte, characterized in that: The method comprises: Retrieve the material storage management data of aluminum electrolytic capacitor electrolyte to obtain the storage material data set; Identify the target production task, establish an electrolyte formula database based on the storage material data set, and retrieve the target production formula information; Formulate a production plan according to the target production formula information to simulate the preparation of the target electrolyte, and monitor the production process of the target electrolyte to obtain a simulated production monitoring data set; Setting a production key indicator set, testing the simulated production monitoring data set based on the production key indicator set, and generating a simulated production quality detection data set of the target electrolyte; An optimization suggestion is generated through the simulated production quality inspection data set, the production plan is optimized through the optimization suggestion, and the production optimization plan is executed to perform intelligent production management on the target electrolyte.

2. The production management method of a high-stability aluminum electrolytic capacitor electrolyte according to claim 1, characterized in that: Retrieving a material storage management data set for an aluminum electrolytic capacitor electrolyte to obtain a storage material data set, the method comprising: Setting minimum inventory based on target production plan information for aluminum electrolytic capacitor electrolyte; Comparing the material warehouse management data set with the minimum inventory quantity, and generating inventory warning information when there is material warehouse management data less than the minimum inventory quantity in the material warehouse management data set; Extracting production forecast results based on the target production plan information, and determining the material purchase quantity according to the inventory warning information combined with the production forecast results; Material purchase order information is constructed according to the material purchase quantity, and the material warehouse management data set is updated based on the material purchase order information to generate the warehouse material data set.

3. The production management method of a high-stability aluminum electrolytic capacitor electrolyte according to claim 1, characterized in that: Establishing an electrolyte formula database based on the storage material data set, identifying a target production task, and retrieving target production formula information, the method includes: Based on the historical formula data record archive of aluminum electrolytic capacitor electrolyte, an initial electrolyte formula database is constructed; According to the target production task of aluminum electrolytic capacitor electrolyte, traverse the initial electrolyte formula database to determine the initial production formula information; Extracting a recipe material list in the initial production recipe information, setting a production material requirement information set based on the recipe material list, and determining whether the warehouse material data set conforms to the production material requirement information set; If the storage material data set meets the production material demand information set, outputting the initial electrolyte formula database as the electrolyte formula database; If the storage material data set does not conform to the production material requirement information set, a supplementary instruction is generated, and the initial production formula information is updated to the formula information to be generated through the supplementary instruction, and the initial electrolyte formula database is updated based on the formula information to be generated, and the electrolyte formula database is output.

4. The production management method of a high-stability aluminum electrolytic capacitor electrolyte according to claim 3, characterized in that: Formulating a production plan according to the target production formula information to simulate the preparation of the target electrolyte, and monitoring the production process of the target electrolyte to obtain a simulated production monitoring data set, the method comprising: Parsing the production material demand information set to obtain production material type information, production material ratio information, and production preparation step information; According to the production preparation step information, the production material type information is prepared based on the production material ratio information to determine the production plan; Performing simulated preparation of the target electrolyte through the production scheme, and determining a simulated preparation process; Based on the simulated preparation process, multiple monitoring points are set to collect data to generate the simulated production monitoring data set.

5. The production management method of a high-stability aluminum electrolytic capacitor electrolyte according to claim 4, characterized in that: Set production key indicators, including: Extracting a production key information set of a target electrolyte based on the simulated preparation process, wherein the production key information set includes production feature information and production quality requirement information; Identify and obtain multiple key quality factors based on the production feature information and the production quality requirement information; Analyze the historical production quality data set according to the multiple key quality factors to determine the target range; The plurality of key quality factors are weighted based on the target range, and the weighted distribution results are added to the production key indicator set.

6. The production management method of a high-stability aluminum electrolytic capacitor electrolyte according to claim 1, characterized in that: The simulated production monitoring data set is detected based on the production key indicator set to generate a simulated production quality detection data set of the target electrolyte, the method comprising: Traversing the simulated production monitoring data set to match with the production key indicator set, and if the match is successful, setting a preset production quality threshold based on the production key indicator set; Determining whether the production quality of the first simulated production monitoring data set of the simulated production monitoring data set reaches the preset production quality threshold; If the production quality of the first simulated production monitoring data of the simulated production monitoring data set does not reach the preset production quality threshold, the production plan is deemed to be infeasible, and the first simulated production monitoring data is marked as unqualified, and a first test result is generated; The first test result is added to the simulated production quality test data set of the target electrolyte.

7. The production management method of a high-stability aluminum electrolytic capacitor electrolyte according to claim 1, characterized in that: Generating optimization suggestions through the simulated production quality inspection data set, the method comprising: Based on the simulated annealing algorithm, the simulated production quality inspection data set is used as input data, and a solution space is defined according to the production key indicator set; Introducing an objective function, performing random selection based on the solution space, determining a first initial solution, and calculating and obtaining an objective function value of the first initial solution; Generating a second initial solution by performing random perturbation on the solution space, and calculating an objective function value of the second initial solution; Compare the objective function value of the first initial solution with the objective function value of the second initial solution. If the objective function value of the first initial solution is less than the objective function value of the second initial solution, the second initial solution is considered to be better, and the first initial solution is replaced by the second initial solution. If the objective function value of the first initial solution is greater than or equal to the objective function value of the second initial solution, the first initial solution is considered to be better, and the first initial solution is retained. A preset number of iterations is set, and iterative judgments of random perturbations are repeated in the solution space until the number of iterations reaches the preset number of iterations, and the i-th initial solution is output as the optimal solution. The production parameters are analyzed based on the optimal solution, the production optimization parameters to be matched are generated, and the optimization suggestions are formulated.

8. The production management method of a high-stability aluminum electrolytic capacitor electrolyte according to claim 7, characterized in that: The production plan is optimized by the optimization suggestion, and the method includes: Conduct a feasibility assessment based on the optimization suggestions and generate an execution assessment result; According to the execution evaluation result, matching the production plan with the optimization suggestion, and determining the production parameters to be optimized; The production parameters to be optimized are data aligned with the production optimization parameters to be matched, and according to the alignment result, the production parameters to be optimized are optimized by the production optimization parameters to be matched to determine the production optimization plan.

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