A Production Management Method for High-Stability Aluminum Electrolytic Capacitor Electrolyte

Abstract

This invention discloses a production management method for high-stability aluminum electrolytic capacitor electrolyte, relating to the field of capacitor technology. The method includes: obtaining a storage material dataset; identifying the target production task and retrieving the target production formula information; formulating a production plan to simulate the preparation of the target electrolyte, obtaining a simulated production monitoring dataset; setting a set of key production indicators, detecting the simulated production monitoring dataset based on the key production indicator set, generating a simulated production quality inspection dataset; generating optimization suggestions from the simulated production quality inspection dataset, optimizing the production plan based on the optimization suggestions, and executing the optimized production plan for intelligent production management of the target electrolyte. This invention solves the technical problems of low precision in electrolyte production management and control in existing technologies, which leads to the inability to effectively collect, process, and analyze production data, resulting in low production efficiency and unstable quality control, thus achieving the technical effect of improving production efficiency and production stability.

Description

Technical Field

[0001] This invention relates to the field of capacitor technology, and more specifically to a production management method for a high-stability aluminum electrolytic capacitor electrolyte. Background Technology

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

[0003] In the production process of electrolytes for aluminum electrolytic capacitors, controlling the content of key components and impurities in the electrolyte is crucial to ensuring product quality and stability. However, traditional electrolyte production management methods suffer from problems such as low efficiency and unstable quality control. Summary of the Invention

[0004] This application provides a production management method for high-stability aluminum electrolytic capacitor electrolyte, which addresses the technical problems of low efficiency and unstable quality control in existing technologies.

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

[0006] The first aspect of this application provides a production management method for a high-stability aluminum electrolytic capacitor electrolyte, the method comprising:

[0007] Material storage management data for aluminum electrolytic capacitor electrolyte is retrieved to obtain a storage material dataset; the target production task is identified, and an electrolyte formula database is established based on the storage material dataset, retrieving the target production formula information; 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 dataset; a set of key production indicators is set, and the simulated production monitoring dataset is tested based on the set of key production indicators to generate a simulated production quality inspection dataset for the target electrolyte; optimization suggestions are generated through the simulated production quality inspection dataset, and the production plan is optimized based on the optimization suggestions, and the production optimization plan is executed to perform intelligent production management of the target electrolyte.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] This application retrieves material storage management data for aluminum electrolytic capacitor electrolytes to obtain a storage material dataset; identifies target production tasks, establishes an electrolyte formula database based on the storage material dataset, and retrieves 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 dataset; sets a set of key production indicators, and tests the simulated production monitoring dataset based on the set of key production indicators to generate a simulated production quality inspection dataset for the target electrolyte; generates optimization suggestions based on the simulated production quality inspection dataset, optimizes the production plan based on the optimization suggestions, and executes the optimized production plan to achieve intelligent production management of the target electrolyte. This invention solves the technical problems of low precision in electrolyte production management and control in the prior art, which leads to the inability to effectively collect, process, and analyze production data, resulting in low production efficiency and unstable quality control. Through intelligent management, it achieves automatic replenishment and replacement of raw materials, as well as intelligent control and quality inspection, reducing manual intervention and realizing efficient control and optimization of the electrolyte process, thereby improving production efficiency and stability. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of a production management method for a high-stability aluminum electrolytic capacitor electrolyte provided in an embodiment of this application. Detailed Implementation

[0012] This application provides a production management method for high-stability aluminum electrolytic capacitor electrolytes. It addresses the technical problems in existing technologies where low precision in electrolyte production management and control leads to ineffective collection, processing, and analysis of production data, resulting in low production efficiency and unstable quality control. Through intelligent management, it achieves automatic replenishment and replacement of raw materials, as well as intelligent control and quality inspection, reducing manual intervention and realizing efficient control and optimization of the electrolyte process, thereby improving production efficiency and stability.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. 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 that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or device. Example 1

[0015] like Figure 1 As shown, this application provides a production management method for a high-stability aluminum electrolytic capacitor electrolyte, the method comprising:

[0016] Step S100: Retrieve the material storage management data of the electrolyte for aluminum electrolytic capacitors to obtain the storage material dataset;

[0017] In this embodiment of the application, the material storage management data for aluminum electrolytic capacitor electrolyte stores the types and quantities of raw materials used to produce aluminum electrolytic capacitor electrolyte.

[0018] By retrieving the material storage management data for aluminum electrolytic capacitor electrolyte, a storage material dataset was obtained. This dataset contains information such as the types and quantities of materials used in the production of aluminum electrolytic capacitor electrolyte.

[0019] Step S200: Identify the target production task, establish an electrolyte formula database based on the storage material dataset, and retrieve the target production formula information;

[0020] In this embodiment of the application, a target production task is received from a customer order or a company's production plan. The target production task includes information such as determining the type of electrolyte and the production quantity.

[0021] An electrolyte formulation database was established by matching historical formulation data records of aluminum electrolytic capacitor electrolytes with a data set of stored materials.

[0022] Based on the requirements of the target production task, database query statements, such as SELECT statements, are used to retrieve the corresponding target production formula information from the electrolyte formula database. The retrieved target production formula information includes a complete list of raw materials, the proportions of the raw materials, the order of addition, and other detailed information.

[0023] Step S300: Develop a production plan according to the target production formula information, simulate the preparation of the target electrolyte, monitor the production process of the target electrolyte, and obtain a simulated production monitoring dataset;

[0024] In this embodiment of the application, based on the target production formula information, process flow design software or tools, such as AutoCAD, SolidWorks, etc., are used to formulate an electrolyte production plan, including equipment configuration, process parameter settings, etc.

[0025] Next, simulation software, such as chemical simulation software and process simulation software, is used to simulate the preparation process of electrolyte in a virtual environment, including the feeding of raw materials, mixing, reaction and other steps, and to record various data during the simulation process.

[0026] During the simulated production process, sensors and data acquisition systems, such as PLC and SCADA, are used to monitor the production environment, equipment status, and process parameters in real time to obtain a simulated production monitoring dataset.

[0027] Step S400: Set a set of key production indicators, and test the simulated production monitoring dataset based on the set of key production indicators to generate a simulated production quality test dataset of the target electrolyte.

[0028] In this embodiment, a set of key production indicators is set by technical experts based on the performance requirements of the target electrolyte and production experience. The set of key production indicators includes the electrolyte's concentration, purity, conductivity, pH value, viscosity, and impurity content.

[0029] Data related to key production indicators is extracted from the simulated production monitoring dataset. The extracted data is matched with the set of key production indicators. Statistical analysis methods, such as mean and standard deviation, are used to calculate the extracted data. The calculation results are compared with the corresponding key production indicators to determine whether the production quality meets the standards, and a simulated production quality detection dataset for the target electrolyte is generated.

[0030] Step S500: Generate optimization suggestions using the simulated production quality inspection dataset, optimize the production plan using the optimization suggestions, and execute the production optimization plan to perform intelligent production management of the target electrolyte.

[0031] In this embodiment, statistical methods, such as analysis of variance and correlation analysis, are used to identify key factors affecting product quality. Combined with the experience and knowledge of domain experts, outliers and potential problems in the dataset are analyzed. Based on the analysis results, optimization suggestions are generated, such as changing the temperature, pH value, or raw material ratio.

[0032] Based on the optimization suggestions, key parameters in the production process, such as temperature, pressure, and stirring speed, are adjusted to optimize the production plan.

[0033] Finally, the MES or ERP system is used to implement production optimization plans and achieve intelligent production management of the target electrolyte.

[0034] Furthermore, step S100 in the method provided in the application embodiment further includes:

[0035] Setting minimum inventory levels based on target production plan information for aluminum electrolytic capacitor electrolyte;

[0036] The material storage management dataset is compared with the minimum inventory level. If there is material storage management data in the material storage management dataset that is less than the minimum inventory level, an inventory warning message is generated.

[0037] Production forecast results are extracted based on the target production plan information, and the material procurement quantity is determined based on the inventory warning information combined with the production forecast results.

[0038] Material purchase order information is constructed based on the material purchase quantity, and the material warehousing management dataset is updated based on the material purchase order information to generate the warehousing material dataset.

[0039] In this embodiment, the required quantity of electrolyte to be produced, as well as the types and quantities of production materials needed for electrolyte production, are determined based on the target production plan information for aluminum electrolytic capacitor electrolyte. The required quantity of electrolyte to be produced is multiplied by the required quantity of production materials to set a minimum inventory level. The minimum inventory level is the minimum inventory required to complete the target production plan.

[0040] Using data analysis tools such as Excel and SQL queries, the material storage management dataset is compared with the set minimum inventory levels to identify materials whose inventory levels are below the minimum. When any material in the material storage management dataset has an inventory level below the minimum, an inventory alert is generated. The inventory alert includes the quantity of stored materials and which type of material has not reached the minimum inventory level.

[0041] Next, production forecasts are extracted based on the target production plan information. These forecasts represent the quantities of each material required for production. Based on inventory warnings and production forecasts, the quantities of materials in the production forecasts are subtracted from the quantities in the warehouse to calculate the required material procurement quantities.

[0042] Based on the determined material purchase quantity, construct material purchase order information. Material purchase order information includes material name, quantity, etc. Use an ERP system or dedicated procurement management software to create and manage purchase orders. Once a material purchase order is executed and goods are received, update the material warehouse management dataset according to the actual received quantity. Use an ERP system or inventory management software to record and manage material receiving operations. The updated material warehouse management dataset is the new warehouse material dataset.

[0043] Furthermore, step S200 in the method provided in the application embodiment further includes:

[0044] An initial electrolyte formulation database was constructed based on historical formulation data records of aluminum electrolytic capacitor electrolytes.

[0045] Based on the target production task of the aluminum electrolytic capacitor electrolyte, the initial production formula information is determined by traversing the initial electrolyte formula database.

[0046] Extract the list of formula materials from the initial production formula information, set a production material demand information set based on the list of formula materials, and determine whether the storage material dataset matches the production material demand information set.

[0047] If the storage material dataset matches the production material demand information set, then the initial electrolyte formula database will be output as the electrolyte formula database.

[0048] If the storage material dataset does not match the production material demand information set, a supplementary instruction is generated. The initial production formula information is updated to the formula to be generated information using the supplementary instruction. The initial electrolyte formula database is updated based on the formula to be generated information, and the electrolyte formula database is output.

[0049] In this embodiment, aluminum electrolytic capacitor electrolyte formulation data is collected from historical formulation data records. This data includes formulation number, a list of formulation materials, material ratios, and electrolyte performance parameters. The list of formulation materials includes ingredients such as alumina and polyethylene glycol. Material ratios are specified as percentages. The extracted aluminum electrolytic capacitor electrolyte formulation data is then integrated to generate an initial electrolyte formulation database.

[0050] Next, based on the target production task for the aluminum electrolytic capacitor electrolyte, specific production volume and other requirements are extracted. According to the production task requirements, initial production formulas that meet the criteria are identified by screening and comparing formula information in the initial electrolyte formula database.

[0051] From the selected initial production formula information, extract the required formula material list, including the type and proportion of each material. Based on the specific requirements of the target production task and the extracted formula material list, calculate the total demand for each material, thus obtaining the production material demand information set.

[0052] The production material demand information set is compared with the warehouse material dataset to determine whether the inventory meets production needs. If the warehouse materials meet the requirements of the production material demand information set, the initial electrolyte formula database is directly output as the electrolyte formula database.

[0053] If the stored 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 includes detailed information such as the name, specifications, quantity, and required time of the missing materials. The supplementary instruction is sent to the purchasing department via email or other effective means. Simultaneously with the generation of the supplementary instruction, the initial production formula information is marked as pending formula generation, indicating that the current formula can only be produced after the materials are replenished.

[0054] 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.

[0055] 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 dataset. The method further includes:

[0056] The production material demand information set is analyzed to obtain information including the types of production materials, the proportions of production materials, and the production preparation steps.

[0057] According to the production preparation steps, the production materials are proportioned and prepared based on the production material ratio information to determine the production plan;

[0058] The target electrolyte was prepared using the aforementioned production scheme to determine the simulated preparation process;

[0059] Based on the simulated preparation process, multiple monitoring points are set up to collect data and generate the simulated production monitoring dataset.

[0060] In this embodiment, information on the types of production materials is extracted from the production material requirement information set. Information on the proportions of production materials and information on production preparation steps are extracted from the target production formula information. The production material proportion information refers to the amount or proportion of each material used in the production process. The production preparation step information refers to the entire preparation process for producing the electrolyte, including steps such as mixing, stirring, heating, and cooling.

[0061] Based on the material ratio information, use calculator software to calculate the precise proportions of each material to ensure accurate proportions. Determine the production process according to the production preparation steps. Ensure each step is performed in the predetermined order and under the prescribed conditions. Through this process, determine the production plan. The production plan includes the materials to be added in each step, the material ratios, and the production process.

[0062] Next, chemical process simulation software, such as Aspen Plus, is used to simulate the preparation of the target electrolyte. During the simulation, the preparation process is determined according to the production plan. The simulation process includes the amount of materials added, reaction temperature, stirring speed, and reaction time.

[0063] Then, the stirring points and material addition points in the simulated preparation process were used as monitoring points. Data was collected at each monitoring point to obtain a simulated production monitoring dataset. The simulated production monitoring dataset includes key parameters such as temperature, pressure, flow rate, and stirring speed during the production process.

[0064] Furthermore, the method also includes setting key production indicators:

[0065] Based on the simulated preparation process, a set of key production information for the target electrolyte is extracted, which includes production characteristic information and production quality requirement information.

[0066] Based on the production characteristic information and the production quality requirement information, multiple key quality factors are identified and obtained.

[0067] The historical production quality dataset was analyzed based on the aforementioned key quality factors to determine the target range;

[0068] Based on the target range, the weights of the multiple key quality factors are assigned, and the weight assignment results are added to the set of key production indicators.

[0069] In this embodiment, electrolyte preparation simulation software is used to simulate the preparation process, and key information is extracted from the simulation results using data scraping or API interfaces to obtain a set of key production information for the target electrolyte. This set includes production characteristic information and production quality requirement information. The production characteristic information includes process parameters and raw material information. Process parameters include mixing ratio, stirring speed, heating or cooling temperature and time, etc. Raw material information includes raw material type and purity, etc. Production quality requirement information includes the electrolyte's conductivity, stability, density, pH value, etc.

[0070] Next, technical experts will determine the key quality factors for production characteristics and quality requirements. These key quality factors include raw material purity, reaction temperature, stirring speed, reaction time, and pressure.

[0071] Next, historical production quality datasets are retrieved from the historical production database, including actual values ​​of key quality factors and performance index values ​​of the produced electrolyte. When analyzing the historical production quality datasets according to multiple key quality factors, technical experts, combining industry standards and expert experience, screen and confirm the main quality factors, forming a list of key quality factors. For each key quality factor, a target range is set based on the production process and equipment conditions.

[0072] When assigning weights to multiple key quality factors, technical experts and frontline 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 set of key production indicators, which is then added to the set of key production indicators.

[0073] Furthermore, step S400 in the method provided in the application embodiment further includes:

[0074] The simulated production monitoring dataset is traversed and matched with the set of key production indicators. If the match is successful, a preset production quality threshold is set based on the set of key production indicators.

[0075] Determine whether the production quality of the first simulated production monitoring data in the simulated production monitoring dataset reaches the preset production quality threshold.

[0076] If the production quality of the first simulated production monitoring data in the simulated production monitoring dataset does not reach the preset production quality threshold, the production plan is considered infeasible, and the first simulated production monitoring data is marked as unqualified, generating a first detection result.

[0077] The first test result is added to the simulated production quality test dataset of the target electrolyte.

[0078] In this embodiment, database queries and data comparison algorithms are used to traverse each set of data in the simulated production monitoring dataset. The key quality factor values ​​in the simulated data are compared one by one with the target range in the set of key production indicators. When a one-to-one correspondence exists between the monitoring data and the indicators, it proves that the set of key production indicators can completely detect the simulated production monitoring dataset, and there is no situation where there is data in the simulated production monitoring dataset that needs to be judged but the key indicator set does not have a corresponding indicator. Therefore, the match is considered successful, and the set of key production indicators is used as the preset production quality threshold.

[0079] A set of data is randomly selected from the simulated production monitoring dataset as the first simulated production monitoring data. This first simulated production monitoring data is compared with a preset production quality threshold. If the production quality of any data point in the first simulated production monitoring dataset fails to meet the preset production quality threshold, the production plan is deemed infeasible, and the first simulated production monitoring data is marked as non-compliant, generating a first detection result. The first detection result includes information on which production quality item failed to meet the preset production quality threshold.

[0080] Finally, the first test result is added to the simulated production quality test dataset of the target electrolyte.

[0081] Furthermore, step S500 in the method provided in the application embodiment further includes:

[0082] Based on the simulated annealing algorithm, the simulated production quality inspection dataset is used as input data, and the solution space is defined according to the set of key production indicators.

[0083] An objective function is introduced, and a first initial solution is determined by random selection based on the solution space. The objective function value of the first initial solution is then calculated and obtained.

[0084] A second initial solution is generated by random perturbation based on the solution space, and the objective function value of the second initial solution is calculated.

[0085] The objective function value of the first initial solution is compared 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 better, and the second initial solution replaces the first 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 better, and the first initial solution is retained. A preset number of iterations is set, and the iterative judgment with random perturbation is repeated in the solution space until the preset number of iterations is reached. Then, the i-th initial solution is output as the optimal solution.

[0086] Based on the optimal solution, the values ​​of production parameters are analyzed to generate production optimization parameters to be matched, and optimization suggestions are formulated.

[0087] In this embodiment of the application, adjustable parameters in the production process, such as raw material ratio, reaction temperature setpoint, and stirring speed setpoint, are used as the solution space of the simulated annealing algorithm based on the set of key production indicators.

[0088] Next, an objective function is introduced to evaluate the quality of the solution. For example, the objective function might be a weighted sum of the raw material ratios, the reaction temperature setpoint, and the stirring speed. The weights are assigned by technical experts. Then, an initial solution is randomly selected from the solution space as the first initial solution, and its objective function value is calculated.

[0089] Next, the first initial solution is randomly perturbed to generate a second initial solution. The purpose of the perturbation is to explore other possible solutions in the solution space. Simultaneously, the objective function value of the second initial solution is calculated. The objective function values ​​of the first and second initial solutions are then compared.

[0090] If the objective function value of the first initial solution is less than that of the second initial solution, the second initial solution is considered better, and the second initial solution replaces the first initial solution. If the objective function value of the first initial solution is greater than or equal to that of the second initial solution, the first initial solution is considered better, and the first initial solution is retained. When no new solution is accepted in several consecutive iterations, the algorithm stops searching and outputs the current solution as the optimal solution.

[0091] The parameters of the optimal solution are compared with the actual production parameters to analyze their values. This analysis generates production optimization parameters to be matched; these parameters represent minor adjustments or significant changes to the original production parameters. Based on these optimization parameters, production optimization suggestions are formulated to improve production efficiency and product quality. These suggestions include adjusting key parameters in the production process, such as temperature, raw material ratios, and stirring speed.

[0092] Furthermore, the method further includes optimizing the production plan using the optimization suggestions:

[0093] A feasibility assessment is performed based on the optimization suggestions, and an execution assessment result is generated.

[0094] Based on the evaluation results, the production plan is matched with the optimization suggestions to determine the production parameters to be optimized.

[0095] The production parameters to be optimized are aligned with the production optimization parameters to be matched. Based on the alignment results, the production optimization parameters to be matched are used to optimize the production parameters to be optimized, thereby determining the production optimization scheme.

[0096] In this embodiment, the first step is to assess whether the adjustment of each parameter in the optimization proposal complies with the physical limitations of the production equipment, the raw material supply situation, and production safety standards. An expert review process is then used to invite experts in the production field to review the optimization proposal, resulting in an implementation evaluation. The implementation evaluation result is categorized as either "implementation" or "non-implementation."

[0097] When the evaluation result is "execution", the production plan is matched according to the optimization suggestions. The production plan with the highest matching degree with the optimization suggestions is selected. Among the selected production plans, the production parameters related to the optimization suggestions are identified and these production parameters are used as production parameters to be optimized.

[0098] The production parameters to be optimized and the production optimization parameters to be matched are compared one by one to ensure consistency in parameter names, units, and value ranges. Based on 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.

[0099] In summary, the embodiments of this application have at least the following technical effects:

[0100] This application retrieves material storage management data for aluminum electrolytic capacitor electrolytes to obtain a storage material dataset; identifies target production tasks, establishes an electrolyte formula database based on the storage material dataset, and retrieves 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 dataset; sets a set of key production indicators, and tests the simulated production monitoring dataset based on the set of key production indicators to generate a simulated production quality inspection dataset for the target electrolyte; generates optimization suggestions based on the simulated production quality inspection dataset, optimizes the production plan based on the optimization suggestions, and executes the optimized production plan to achieve intelligent production management of the target electrolyte. This invention solves the technical problems of low precision in electrolyte production management and control in the prior art, which leads to the inability to effectively collect, process, and analyze production data, resulting in low production efficiency and unstable quality control. Through intelligent management, it achieves automatic replenishment and replacement of raw materials, as well as intelligent control and quality inspection, reducing manual intervention and realizing efficient control and optimization of the electrolyte process, thereby improving production efficiency and stability.

[0101] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0102] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0103] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A production management method for a high-stability aluminum electrolytic capacitor electrolyte, characterized in that, The method includes: Retrieve material storage management data for aluminum electrolytic capacitor electrolyte to obtain a data set of storage materials; Identify the target production task, establish an electrolyte formula database based on the warehouse material dataset, and retrieve the target production formula information; A production plan is developed based on 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 dataset. A set of key production indicators is set, and the simulated production monitoring dataset is tested based on the set of key production indicators to generate a simulated production quality test dataset of the target electrolyte. Optimization suggestions are generated using the simulated production quality inspection dataset. The production plan is then optimized using these suggestions, and the optimized production plan is executed to perform intelligent production management of the target electrolyte. The simulated production monitoring dataset is tested based on the set of key production indicators to generate a simulated production quality testing dataset for the target electrolyte. The method includes: The simulated production monitoring dataset is traversed and matched with the set of key production indicators. If the match is successful, a preset production quality threshold is set based on the set of key production indicators. Determine whether the production quality of the first simulated production monitoring data in the simulated production monitoring dataset reaches the preset production quality threshold. If the production quality of the first simulated production monitoring data in the simulated production monitoring dataset does not reach the preset production quality threshold, the production plan is considered infeasible, and the first simulated production monitoring data is marked as unqualified, generating a first detection result. The first test result is added to the simulated production quality test dataset of the target electrolyte.

2. The production management method for a high-stability aluminum electrolytic capacitor electrolyte as described in claim 1, characterized in that, The method for retrieving the material storage management dataset for aluminum electrolytic capacitor electrolyte to obtain the storage material dataset includes: Setting minimum inventory levels based on target production plan information for aluminum electrolytic capacitor electrolyte; The material storage management dataset is compared with the minimum inventory level. If there is material storage management data in the material storage management dataset that is less than the minimum inventory level, an inventory warning message is generated. Based on the target production plan information, production forecast results are extracted, and the material procurement quantity is determined according to the inventory warning information and the production forecast results. Material purchase order information is constructed based on the material purchase quantity, and the material warehousing management dataset is updated based on the material purchase order information to generate the warehousing material dataset.

3. The production management method for a high-stability aluminum electrolytic capacitor electrolyte as described in claim 1, characterized in that, An electrolyte formulation database is established based on the aforementioned storage material dataset. Target production tasks are identified, and target production formulation information is retrieved. The method includes: An initial electrolyte formulation database was constructed based on historical formulation data records of aluminum electrolytic capacitor electrolytes. Based on the target production task of the aluminum electrolytic capacitor electrolyte, the initial production formula information is determined by traversing the initial electrolyte formula database. Extract the list of formula materials from the initial production formula information, set a production material demand information set based on the list of formula materials, and determine whether the storage material dataset matches the production material demand information set. If the storage material dataset matches the production material demand information set, then the initial electrolyte formula database will be output as the electrolyte formula database. If the storage material dataset does not match the production material demand information set, a supplementary instruction is generated. The initial production formula information is updated to the formula to be generated information using the supplementary instruction. The initial electrolyte formula database is updated based on the formula to be generated information, and the electrolyte formula database is output.

4. The production management method for a high-stability aluminum electrolytic capacitor electrolyte as described in claim 3, characterized in that, A production plan is formulated based on 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 dataset. The method includes: The production material demand information set is analyzed to obtain information including the types of production materials, the proportions of production materials, and the production preparation steps. According to the production preparation steps, the production materials are proportioned and prepared based on the production material ratio information to determine the production plan; The target electrolyte was prepared using the aforementioned production scheme to determine the simulated preparation process; Based on the simulated preparation process, multiple monitoring points are set up to collect data and generate the simulated production monitoring dataset.

5. The production management method for a high-stability aluminum electrolytic capacitor electrolyte as described in claim 4, characterized in that, Setting key production indicators can be achieved through methods including: Based on the simulated preparation process, a set of key production information for the target electrolyte is extracted, which includes production characteristic information and production quality requirement information. Based on the production characteristic information and the production quality requirement information, multiple key quality factors are identified and obtained. The historical production quality dataset was analyzed based on the aforementioned key quality factors to determine the target range; Based on the target range, the weights of the multiple key quality factors are assigned, and the weight assignment results are added to the set of key production indicators.

6. The production management method for a high-stability aluminum electrolytic capacitor electrolyte as described in claim 1, characterized in that, The method for generating optimization suggestions from the simulated production quality inspection dataset includes: Based on the simulated annealing algorithm, the simulated production quality inspection dataset is used as input data, and the solution space is defined according to the set of key production indicators. An objective function is introduced, and a first initial solution is determined by random selection based on the solution space. The objective function value of the first initial solution is then calculated and obtained. A second initial solution is generated by random perturbation based on the solution space, and the objective function value of the second initial solution is calculated. The objective function value of the first initial solution is compared 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 better, and the second initial solution replaces the first 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 better, and the first initial solution is retained. A preset number of iterations is set, and the iterative judgment with random perturbation is repeated in the solution space until the preset number of iterations is reached. Then, the i-th initial solution is output as the optimal solution. Based on the optimal solution, the values ​​of production parameters are analyzed to generate production optimization parameters to be matched, and optimization suggestions are formulated.

7. The production management method for a high-stability aluminum electrolytic capacitor electrolyte as described in claim 6, characterized in that, The production plan is optimized using the aforementioned optimization suggestions, and the method includes: A feasibility assessment is performed based on the optimization suggestions, and an execution assessment result is generated. Based on the evaluation results, the production plan is matched with the optimization suggestions to determine the production parameters to be optimized. The production parameters to be optimized are aligned with the production optimization parameters to be matched. Based on the alignment results, the production optimization parameters to be matched are used to optimize the production parameters to be optimized, thereby determining the production optimization scheme.

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