Intelligent source searching and order optimization system based on AI

Abstract

The invention is suitable for the technical field of intelligent order management, and provides an AI-based intelligent source searching and order optimization system, which comprises a data acquisition module, a newly added equipment determination module, a specified defective product proportion determination module and a parameter correction module, and the processing module is used for determining delivery stability parameters of the to-be-executed order after determining that the product of the to-be-executed order is the new type product, and obtaining a production historical data set of a production unit to which the to-be-executed order belongs. According to the method, the specified defective product ratio of the target production flow is calculated based on the production historical data set, and the deviation value is calculated in combination with the reference defective product ratio of the reference production flow, so that the correction factor is constructed, and dynamic correction of the delivery stability parameters is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent order management, and in particular relates to an AI-based intelligent sourcing and order optimization system. Background Art

[0002] In the prior art, order management systems are widely used in the fields of manufacturing, supply chain management, etc., aiming to improve production efficiency, shorten delivery cycles and reduce production costs through analysis and optimization of order data. In the order management process, a production forecasting method based on historical data is usually adopted to optimize material procurement, production scheduling and order delivery plans. Some technical solutions combine the operating status of production equipment, historical production efficiency and inventory data, and allocate optimal production resources to orders through algorithm analysis. However, in the order management process of new model products, due to the adjustment of production process flow, the update of production equipment and the change of process parameters, the delivery stability parameters calculated by traditional order management systems based on existing model product data or fixed models are often unable to accurately predict the production stability of new model products, resulting in deviations between order optimization strategies and actual production conditions.

[0003] In order to solve the order optimization problem of new product models, some existing technologies introduce machine learning or artificial intelligence algorithms to improve the intelligence level of order optimization by analyzing historical production data, equipment operation and maintenance records, and order execution. However, these solutions mainly establish prediction models based on existing production data, and are unable to dynamically correct changes in production stability caused by the introduction of new special equipment. In addition, although some technical solutions take into account the process differences between different types of products, their analysis methods are usually based on fixed classification rules or empirical parameter settings, and lack the ability to make real-time adjustments during the production process of new product models, resulting in low prediction accuracy and inability to effectively guide the execution of order optimization strategies. Summary of the invention

[0004] The purpose of the present invention is to provide an AI-based intelligent sourcing and order optimization system, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented as follows: an AI-based intelligent sourcing and order optimization system, the system comprising: a data acquisition module, a new equipment determination module, a designated defective product ratio determination module and a parameter correction module, wherein:

[0006] A data acquisition module is used to determine the delivery stability parameters of the order to be executed after determining that the product of the order to be executed is a new model product, and to obtain the production history data set of the production unit to which the order to be executed belongs;

[0007] A new equipment determination module is used to determine the existing model products corresponding to the new model products in the production unit, obtain the production process flow of the new model products and the existing model products, and determine the new special equipment introduced for the production of the new model products based on the difference between the two production process flows;

[0008] The designated defective product ratio determination module is used to select a number of production process flows containing newly added special equipment from the production history data set, match the production process flow that matches the production background of the production process flow of the new model product as the target production process, and determine the designated defective product ratio caused by the newly added special equipment in the target production process within the preset running time;

[0009] The parameter correction module is used to calculate the benchmark defective product ratio caused by the same category of equipment as the newly added special equipment in the same category of products as the new model product based on the production history data set, and to correct the delivery stability parameters based on the deviation between the specified defective product ratio and the benchmark defective product ratio.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the newly added device determination module specifically includes:

[0011] An existing model determination unit is used to determine the improved product with the closest process flow to the new model product in the production unit, and to regard the product as the existing model product;

[0012] A new process determination unit is added to obtain the production process flow of new and existing models of products, and to analyze the newly added key process links in the production process flow of new models of products compared with the production process flow of existing models of products;

[0013] The newly added equipment determination unit is used to identify the production equipment involved in the newly added key process links and set the production equipment as the newly added special equipment.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the designated defective product ratio determination module specifically includes:

[0015] The target production process determination unit is used to select a number of production process flows containing newly added special equipment from the production history data set, and match the production process flow that is closest to the production process flow of the new model product in terms of process complexity and production environment conditions, and set the production process flow as the target production process;

[0016] A defective product statistics unit is used to count all defective products generated by the target production process within a preset running time based on the production history data set, and identify the specific equipment that causes each defective product;

[0017] The designated defective product ratio determination unit is used to calculate the designated defective product ratio caused by the newly added special equipment among all defective products in the target production process.

[0018] As a further limitation of the technical solution of the embodiment of the present invention, the parameter correction module specifically includes:

[0019] A reference production process acquisition unit is used to screen out several reference production processes of products of the same category as the new model product based on the production history data set, and analyze the process complexity of each reference production process;

[0020] A process complexity analysis unit, used to determine the process complexity of the target production process, and calculate the process complexity adjustment factor corresponding to each reference production process based on the process complexity of the target production process and the process complexity of each reference production process;

[0021] A benchmark defective product ratio calculation unit is used to count the defective product ratio caused by the same type of equipment as the newly added special equipment in each reference production process within a preset period of time, and calculate the benchmark defective product ratio based on the process complexity adjustment factor and the corresponding defective product ratio of all reference production processes;

[0022] The parameter correction execution unit is used to correct the delivery stability parameter based on the deviation between the specified defective proportion of the target production process and the benchmark defective proportion.

[0023] As a further limitation of the technical solution of the embodiment of the present invention, in the process of calculating the benchmark defective product ratio based on the process complexity adjustment factors and the corresponding defective product ratios of all reference production processes, a preset benchmark defective product ratio calculation formula is used;

[0024] The calculation formula for the benchmark defective product ratio is: ,in Refers to the benchmark defective ratio, Refers to the total quantity of the reference production process, Refers to the A process complexity adjustment factor corresponding to the reference production process, Refers to the The percentage of defective products caused by the same type of equipment as the newly added special equipment in a reference production process within a preset period;

[0025] In the calculation formula for the benchmark defective product ratio: ,in Refers to the A reference to the process complexity of the production process, Refers to the process complexity of the target production process.

[0026] As a further limitation of the technical solution of the embodiment of the present invention, the parameter correction execution unit specifically includes:

[0027] A correction factor setting subunit is used to calculate the deviation between the specified defective product ratio of the target production process and the benchmark defective product ratio, and set it as the correction factor;

[0028] The parameter adjustment subunit is used to call a preset parameter correction formula, adjust the delivery stability parameter based on the correction factor, and apply the corrected delivery stability parameter to the optimization of the orders to be executed.

[0029] As a further limitation of the technical solution of the embodiment of the present invention, the parameter correction formula is: ,in refers to the modified delivery stability parameter, refers to the uncorrected delivery stability parameters, Refers to the specified defective proportion of the target production process, Refers to the benchmark defective ratio, Refers to the correction factor, which is the deviation between the specified defective percentage of the target production process and the benchmark defective percentage. Refers to the adjustment coefficient corresponding to the correction factor.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention calculates the designated defective ratio of the target production process based on the production history data set, and calculates the deviation in combination with the benchmark defective ratio of the reference production process to construct a correction factor to achieve dynamic correction of the delivery stability parameters. Compared with the prior art, the method of calculating the delivery stability parameters by relying solely on the historical data of existing models of products or fixed models cannot accurately reflect the changes in production stability caused by the introduction of new special equipment for new models of products. The present invention, through comparison and correction based on actual production data, enables the delivery stability parameters to dynamically adapt to the production characteristics of new models of products, thereby improving the accuracy and reliability of order optimization.

[0032] The present invention ensures that the benchmark data is more in line with the actual production situation of the new model product by matching the target production process that is closest to the process complexity and production environment conditions of the new model product, avoiding optimization errors caused by data deviation. The calculation of the benchmark defective product ratio is optimized by calculating the process complexity adjustment factor, making the benchmark comparison more scientific and reasonable, ensuring the stability and applicability of order optimization, improving the accuracy of production scheduling, reducing the delivery risk caused by production fluctuations, and improving the reliability and intelligence level of order execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1An application architecture diagram of a system provided by an embodiment of the present invention;

[0034] Figure 2 A structural block diagram of a newly added device determination module in a system provided by an embodiment of the present invention;

[0035] Figure 3 A structural block diagram of a module for determining a proportion of defective products in a system provided by an embodiment of the present invention;

[0036] Figure 4 A structural block diagram of a parameter correction module in a system provided by an embodiment of the present invention;

[0037] Figure 5 This is a structural block diagram of a parameter correction execution unit in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0039] Furthermore, Figure 1 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0040] Among them, in another preferred embodiment provided by the present invention, an AI-based intelligent sourcing and order optimization system includes:

[0041] The data acquisition module 100 is used to determine the delivery stability parameter of the order to be executed after determining that the product of the order to be executed is a new model product, and obtain the production history data set of the production unit to which the order to be executed belongs.

[0042] In an embodiment of the present invention, determining that the product of the pending order is a new model product can be achieved by comparing the product information of the pending order with the historical production data set of the production unit. Specifically, the product model information specified in the pending order is obtained, and the product model information involved in the historical production records is extracted from the production history data set of the production unit. If the product model specified in the pending order does not exist in the production history data set or exists but there are significant changes such as process adjustments, material changes, and production equipment updates, the product is identified as a new model product. This method can effectively identify differences in production characteristics caused by factors such as product upgrades, material replacements, or production equipment changes, and ensure that appropriate optimization strategies can be adopted for the characteristics of new model products in the subsequent optimization process.

[0043] The delivery stability parameter is used to characterize the stability of pending orders during the production process and reflects the delivery reliability of the order products within the planned delivery period. The delivery stability parameter can be generated based on the mature stability evaluation model in the prior art. The specific generation method includes obtaining the historical production data set of the production unit to which the pending order belongs, combining the defective rate, equipment failure rate, production cycle stability, process parameter fluctuations, raw material supply stability and other data recorded in the historical production data set, and using the data analysis model to quantitatively analyze the above parameters and generate parameter values ​​that can comprehensively reflect the production stability of the pending order. The delivery stability parameter plays an important role in the order optimization process. Based on this parameter, the order production plan, material supply plan, personnel scheduling plan and other aspects can be dynamically adjusted to reduce the risk of delayed delivery due to unstable production factors and improve the reliability of order execution and production efficiency.

[0044] The production history data set of the production unit to which the pending order belongs can be extracted from the production management system of the production unit. The content of the production history data set includes the production records of all previous orders in the production unit, including order number, product model, production batch, production time, production cycle, output, quality inspection results, defective rate, production abnormality records and their treatment measures; records of all previous production process flows in the production unit, including the production process flow, process steps, process parameter settings, equipment configuration, processing materials, production environment conditions, quality control standards and process adjustment history of each product; records of all previous equipment usage in the production unit, including equipment number, equipment type, equipment operating status, equipment maintenance records, equipment failure records, equipment replacement and upgrade records, equipment operating parameter fluctuation data, equipment and defective rate correlation analysis data and other information.

[0045] The extraction and organization of production history data sets can be based on data query in the production management system, or batch data mining can be performed through database interfaces or big data analysis systems. In practical applications, the content of the production history data set is the basic data source for subsequent modules to analyze. By fully acquiring this data set, it can be ensured that each module has sufficient reference information during the analysis process, avoiding calculation deviations or inaccurate optimization caused by incomplete or missing data.

[0046] Furthermore, the AI-based intelligent sourcing and order optimization system also includes:

[0047] The new equipment determination module 200 is used to determine the existing model products corresponding to the new model products in the production unit, obtain the production process flow of the new model products and the existing model products, and determine the new special equipment introduced for the production of the new model products based on the differences between the two production process flows.

[0048] Specifically, Figure 2 It shows a structural block diagram of a newly added device determination module 200 in a system provided by an embodiment of the present invention.

[0049] Among them, in the preferred implementation manner provided by the present invention, the newly added device determination module 200 specifically includes:

[0050] An existing model determination unit 201 is used to determine a product in a production unit that is improved and has a process flow that is closest to the new model product, and use the product as an existing model product;

[0051] A newly added process determination unit 202 is used to obtain the production process flow of the new model product and the existing model product, and analyze the newly added key process links of the production process flow of the new model product compared with the production process flow of the existing model product;

[0052] The newly added equipment determination unit 203 is used to identify the production equipment involved in the newly added key process link and set the production equipment as the newly added special equipment.

[0053] In an embodiment of the present invention, determining the improved product corresponding to the new model product in the production unit and having the closest process flow can be achieved by analyzing the production history data set of the production unit. Specifically, first obtain the design specifications, material composition, target production process and manufacturing requirements of the new model product, and then filter out mass-produced products of the same or similar category as the new model product from the production history data set, and further analyze the production process flow, processing steps, equipment configuration, quality control standards and other information of these products. Next, calculate the process flow complexity and production process similarity of each candidate product, and combine the design change points of the new model product to determine the existing product that is closest to the new model product in terms of process flow, production environment, processing steps, etc., and use it as the existing model product.

[0054] The reason for choosing this product as an existing model product is that new model products are usually optimized, improved or upgraded based on the original products. Therefore, the existing model products that are closest to the process flow of the new model products can provide the most valuable historical production data for reference, which is convenient for analyzing the impact of process changes on the production process and can provide a basis for the identification of new equipment. In addition, the production data of existing model products can be used to calculate the production stability estimate of new model products, equipment adaptability assessment, process optimization parameter setting, etc., to ensure that the production of new model products can be optimized and adjusted based on the existing mature process, improve production efficiency and reduce the uncertainty caused by process adjustments.

[0055] Obtain the production process flow of new model products and existing model products, and analyze the newly added key process links of the production process flow of new model products compared with the production process flow of existing model products. This can be achieved specifically through the production process flow comparison method. First, extract the complete production process flow of existing model products from the production history data set, including each processing step, process parameter setting, production equipment configuration, processing materials and quality inspection standards, and establish a standard process flow model. Then, obtain the design process flow of the new model product, and compare the production process flow of the new model product with the standard process flow of the existing model product according to the dimensions of processing sequence, process characteristics, equipment adaptability, etc., identify the newly added processing steps, process adjustment points, equipment demand change points, material replacement points and other information in the production process of the new model product, and finally extract the newly added key process links of the new model product. This method can effectively identify the process improvement part of the new model product compared with the existing model product, and ensure the pertinence of production adjustments.

[0056] Identify the production equipment involved in the newly added key process links, and set the production equipment as newly added special equipment, which can be specifically achieved through the process equipment association analysis method. First, based on the equipment usage records in the production history data set, analyze the production equipment used in each process step of the existing model product, and establish a mapping relationship between the process flow and the equipment. Then, based on the newly added key process links of the new model product obtained by the aforementioned comparison analysis, determine the production steps, process parameter adjustments, and material changes involved in the process link, and combine the mapping relationship between the process flow and the equipment to determine the type of equipment that can execute the process link. If the type of equipment involved in the process link has not been used in the production process of the existing model product, or the equipment needs to be upgraded to meet the new process requirements, the equipment is identified as a newly added special equipment. In this way, the new production equipment required for the new model product can be accurately identified, and it is ensured that the production equipment configuration meets the process requirements of the new model product.

[0057] Furthermore, the AI-based intelligent sourcing and order optimization system also includes:

[0058] The designated defective product ratio determination module 300 is used to screen out several production process flows that include newly added special equipment from the production history data set, match the production process flow that is consistent with the production background of the production process flow of the new model product as the target production process, and determine the designated defective product ratio caused by the newly added special equipment in the target production process within a preset operating time.

[0059] Specifically, Figure 3 It shows a structural block diagram of a module 300 for determining a designated defective product ratio in a system provided by an embodiment of the present invention.

[0060] Among them, in the preferred embodiment provided by the present invention, the designated defective product ratio determination module 300 specifically includes:

[0061] The target production process determination unit 301 is used to select a number of production process flows containing newly added special equipment from the production history data set, and match the production process flow that is closest to the production process flow of the new model product in terms of process complexity and production environment conditions, and set the production process flow as the target production process;

[0062] A defective product statistics unit 302 is used to count all defective products generated by the target production process within a preset running time based on the production history data set, and identify the specific equipment that causes each defective product;

[0063] The designated defective product ratio determination unit 303 is used to calculate the designated defective product ratio caused by the newly added special equipment among all defective products in the target production process.

[0064] In an embodiment of the present invention, the preset running time refers to the shortest time required for delivery of an order, that is, the shortest production time required to complete the target production process under the premise of meeting the order delivery time constraint. The main purpose of setting this time range is to ensure that the statistical defective data can truly reflect the production stability within the order delivery cycle, while avoiding the introduction of historical data irrelevant to order execution due to a long statistical cycle, thereby affecting the accuracy of the evaluation. By limiting the statistical time range to the shortest time required for delivery of an order, the impact of newly added special equipment on the stability of order production in the actual production process can be more accurately identified, ensuring that the calculated designated defective ratio can be directly used to correct the delivery stability parameters, so that order optimization can be adjusted based on more accurate production stability evaluation results, improving the predictability of order execution and the rationality of production scheduling.

[0065] The acquisition of process complexity can be calculated based on the quantitative methods used in the prior art to measure the complexity of production processes. Specifically, process complexity is usually composed of data in multiple dimensions, including the number of processing steps, the degree of sophistication of process parameter control, the level of automation of equipment, the stability of production rhythm, technical requirements of operators, etc. By analyzing the production processes recorded in the production history data set, characteristic data related to process complexity can be extracted, and the process complexity value of each production process can be obtained through weighted calculation. Common methods in the prior art include process complexity calculation based on rule models, complexity modeling based on statistical regression, and process complexity prediction based on machine learning. These methods can be used to calculate the process complexity of the target production process and the reference production process to provide basic data for production process matching and optimization.

[0066] Match the production process that is closest to the production process of the new model product in terms of process complexity and production environment conditions, and set the production process as the target production process. This can be achieved specifically through the production process feature matching method. First, all production process flows containing newly added special equipment are screened out from the production history data set, and their corresponding process complexity, production environment parameters and equipment configuration are obtained. Then, based on the production process of the new model product, its process complexity value is calculated, and the production environment conditions involved in the production process are analyzed, including temperature and humidity control requirements, cleanliness standards, equipment types, production cycle stability and other indicators. Next, the process complexity value of each candidate production process flow is compared with the process complexity value of the new model product, and the complexity deviation between the two is calculated. At the same time, the production environment parameters of the candidate production process flow are matched with the production environment requirements of the new model product, and the environmental adaptability score is calculated. Finally, based on the comprehensive evaluation results of the complexity deviation and the environmental adaptability score, the production process flow with the highest comprehensive matching degree is selected as the target production process. The significance of formulating a target production process is that the production process can reflect the production conditions of new product models to the greatest extent, and provide a reasonable production stability assessment based on existing production data, ensuring that subsequent production optimization and stability correction can be analyzed based on historical data with high similarity, thereby improving forecast accuracy and reducing the risk of production adjustments.

[0067] Based on the production history data set, all defective products generated by the target production process within the preset running time are counted, and the specific equipment that causes each defective product is identified. This can be achieved specifically through the defective product traceability analysis method. First, the production data is extracted from the historical production log corresponding to the target production process, including product batches, production time, process parameter settings, equipment operation records, quality inspection results and defective statistics. Then, all products recorded as defective products are screened out from the production log, and the production time points, production steps and process parameter states corresponding to the defective products are analyzed. The production equipment and process links involved in the occurrence of defective products are further extracted to establish the association between defective products and equipment. Next, according to the equipment operation status, fault records and process adjustments in the production log, determine whether each defective product is caused by the newly added special equipment, and count the number of defective products caused by the newly added special equipment among all defective products in the target production process, and calculate the proportion of designated defective products.

[0068] The generation of the designated defective ratio is of great significance and is mainly used to quantify the impact of newly added special equipment on production stability. Since newly added special equipment may introduce new process uncertainties in the production process of new models, resulting in reduced production stability, it is necessary to evaluate the stability performance of the equipment in the actual production environment by calculating the designated defective ratio of the target production process. This parameter can not only be used to determine whether the newly added special equipment has a higher risk of failure or defective products, but also provide reference data for subsequent delivery stability corrections, so that the final order optimization can be adjusted based on more accurate production stability evaluation results, thereby improving the delivery reliability of new models.

[0069] Furthermore, the AI-based intelligent sourcing and order optimization system also includes:

[0070] The parameter correction module 400 is used to calculate the baseline defective product ratio caused by the same category of equipment as the newly added special equipment in the same category of products as the new model product based on the production history data set, and to correct the delivery stability parameter based on the deviation between the specified defective product ratio and the baseline defective product ratio.

[0071] Specifically, Figure 4 It shows a structural block diagram of the parameter correction module 400 in the system provided by the embodiment of the present invention.

[0072] Among them, in the preferred embodiment provided by the present invention, the parameter correction module 400 specifically includes:

[0073] The reference production process acquisition unit 401 is used to screen out several reference production processes of products of the same category as the new model product based on the production history data set, and analyze the process complexity of each reference production process;

[0074] A process complexity analysis unit 402 is used to determine the process complexity of the target production process, and calculate a process complexity adjustment factor corresponding to each reference production process based on the process complexity of the target production process and the process complexity of each reference production process;

[0075] The benchmark defective product ratio calculation unit 403 is used to count the defective product ratio caused by the same type of equipment as the newly added special equipment in each reference production process within a preset period of time, and calculate the benchmark defective product ratio based on the process complexity adjustment factors and corresponding defective product ratios of all reference production processes;

[0076] The parameter correction execution unit 404 is used to correct the delivery stability parameter based on the deviation between the specified defective product ratio and the benchmark defective product ratio of the target production process.

[0077] In the process of calculating the benchmark defective ratio based on the process complexity adjustment factors and the corresponding defective ratios of all reference production processes, a preset benchmark defective ratio calculation formula is used;

[0078] The calculation formula for the benchmark defective product ratio is: ,in Refers to the benchmark defective ratio, Refers to the total quantity of the reference production process, Refers to the A process complexity adjustment factor corresponding to the reference production process, Refers to the The percentage of defective products caused by the same type of equipment as the newly added special equipment in a reference production process within a preset period;

[0079] In the calculation formula for the benchmark defective product ratio: ,in Refers to the A reference to the process complexity of the production process, Refers to the process complexity of the target production process.

[0080] In an embodiment of the present invention, the same category generally refers to products that belong to the same product category in the product classification system. Specifically, these products have high similarities in production process, processing materials, equipment configuration, process parameter control range, etc. For example, in the field of electronic manufacturing, the same category may refer to circuit board products of the same size or the same function. In the field of mechanical processing, the same category may refer to parts that use the same processing method or the same material. In the implementation of the present invention, products of the same category as the new model product are selected as the basis for the reference production process in order to ensure that the selected reference production process has a high degree of consistency with the production environment and process requirements of the new model product, so that the calculation of the benchmark defective product ratio is more valuable for reference.

[0081] The process complexity adjustment factor is used to measure the impact of the process complexity of each reference production process on the target production process. If the process complexity adjustment factor is greater than 1, it means that the process complexity of the reference production process is higher than that of the target production process. If the process complexity adjustment factor is less than 1, it means that the process complexity of the reference production process is lower than that of the target production process. Through the calculation of this factor, in the process of calculating the benchmark defective ratio, the reference production processes with different process complexities can be reasonably weighted to ensure that the calculated benchmark defective ratio can accurately reflect the production environment characteristics of the target production process.

[0082] The significance of the benchmark defective ratio is that it provides a reference standard for evaluating the production stability of the target production process in the production environment of the same category of products, and for correcting the delivery stability parameters. By calculating the benchmark defective ratio, it is possible to reduce the calculation deviation caused by abnormalities in individual production processes, improve the accuracy of the evaluation of the production stability of the target production process, and provide more reliable data support for order optimization of new models.

[0083] Specifically, Figure 5 It shows a structural block diagram of the parameter correction execution unit 404 in the system provided by the embodiment of the present invention.

[0084] In the preferred embodiment of the present invention, the parameter correction execution unit 404 specifically includes:

[0085] The correction factor setting subunit 4041 is used to calculate the deviation between the specified defective product ratio and the reference defective product ratio of the target production process, and set it as the correction factor;

[0086] The parameter adjustment subunit 4042 is used to call a preset parameter correction formula, adjust the delivery stability parameter based on the correction factor, and apply the corrected delivery stability parameter to the optimization of the orders to be executed.

[0087] The parameter correction formula is: ,in refers to the modified delivery stability parameter, refers to the uncorrected delivery stability parameters, Refers to the specified defective proportion of the target production process, Refers to the benchmark defective ratio, Refers to the correction factor, which is the deviation between the specified defective percentage of the target production process and the benchmark defective percentage. Refers to the adjustment coefficient corresponding to the correction factor.

[0088] In an embodiment of the present invention, the main purpose of calculating the deviation between the designated defective ratio and the benchmark defective ratio of the target production process and setting it as the correction factor is to ensure that the delivery stability parameter can accurately reflect the actual impact of the newly added special equipment on the production stability of the new model product. Since the production process of the new model product may have been adjusted compared to the existing model product, especially the introduction of new special equipment may lead to changes in production stability, the delivery stability parameters calculated only by relying on historical data or a benchmark model may not truly reflect the production status of the new model product. By calculating the designated defective ratio of the target production process and comparing it with the benchmark defective ratio, the degree of influence of the newly added special equipment on the defective rate can be quantified, so that the correction of the delivery stability parameters has clear data support.

[0089] Adjusting the delivery stability parameter based on the correction factor, and applying the corrected delivery stability parameter to the optimization of pending orders can improve the accuracy and stability of order execution. The delivery stability parameter directly affects the production scheduling, material supply, quality control and delivery time prediction in the order production process. If the parameter is calculated inaccurately, it may lead to misjudgment of the production plan, resulting in production capacity fluctuations, quality problems or delivery delays during order execution. The present invention ensures that the calculation of the delivery stability parameter is not only based on historical data, but also can adaptively correct the stability performance of new models in a real production environment through a correction method based on the deviation amount, so that order optimization can be more in line with actual production conditions and improve the scientific nature of overall production management.

[0090] The core innovation of the present invention is that by calculating the designated defective ratio of the target production process and calculating the deviation in combination with the benchmark defective ratio, a correction factor is constructed in a data-driven manner, so that the correction of the delivery stability parameter not only depends on static historical data, but also can dynamically adapt to changes in the production characteristics of new models of products. This method breaks through the limitations of the existing technology that only relies on historical data of existing models of products or fixed models to calculate delivery stability parameters, so that the delivery stability parameters can more accurately predict the production stability of new models of products, thereby improving the reliability and execution effect of order optimization.

[0091] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0092] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0093] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An AI-based intelligent sourcing and order optimization system, characterized in that: The system includes: a data acquisition module, a new equipment determination module, a designated defective product ratio determination module and a parameter correction module, wherein: A data acquisition module is used to determine the delivery stability parameters of the order to be executed after determining that the product of the order to be executed is a new model product, and to obtain the production history data set of the production unit to which the order to be executed belongs; A new equipment determination module is used to determine the existing model products corresponding to the new model products in the production unit, obtain the production process flow of the new model products and the existing model products, and determine the new special equipment introduced for the production of the new model products based on the difference between the two production process flows; The designated defective product ratio determination module is used to select a number of production process flows containing newly added special equipment from the production history data set, match the production process flow that matches the production background of the production process flow of the new model product as the target production process, and determine the designated defective product ratio caused by the newly added special equipment in the target production process within the preset running time; The parameter correction module is used to calculate the benchmark defective product ratio caused by the same category of equipment as the newly added special equipment in the same category of products as the new model product based on the production history data set, and to correct the delivery stability parameters based on the deviation between the specified defective product ratio and the benchmark defective product ratio.

2. The AI-based intelligent sourcing and order optimization system according to claim 1, characterized in that: The newly added device determination module specifically includes: An existing model determination unit is used to determine the improved product with the closest process flow to the new model product in the production unit, and to regard the product as the existing model product; A new process determination unit is added to obtain the production process flow of new and existing models of products, and to analyze the newly added key process links in the production process flow of new models of products compared with the production process flow of existing models of products; The newly added equipment determination unit is used to identify the production equipment involved in the newly added key process links and set the production equipment as the newly added special equipment.

3. The AI-based intelligent sourcing and order optimization system according to claim 1, characterized in that: The designated defective product ratio determination module specifically includes: The target production process determination unit is used to select a number of production process flows containing newly added special equipment from the production history data set, and match the production process flow that is closest to the production process flow of the new model product in terms of process complexity and production environment conditions, and set the production process flow as the target production process; A defective product statistics unit is used to count all defective products generated by the target production process within a preset running time based on the production history data set, and identify the specific equipment that causes each defective product; The designated defective product ratio determination unit is used to calculate the designated defective product ratio caused by the newly added special equipment among all defective products in the target production process.

4. The AI-based intelligent sourcing and order optimization system according to claim 3 is characterized in that: The parameter correction module specifically includes: A reference production process acquisition unit is used to screen out several reference production processes of products of the same category as the new model product based on the production history data set, and analyze the process complexity of each reference production process; A process complexity analysis unit, used to determine the process complexity of the target production process, and calculate the process complexity adjustment factor corresponding to each reference production process based on the process complexity of the target production process and the process complexity of each reference production process; A benchmark defective product ratio calculation unit is used to count the defective product ratio caused by the same type of equipment as the newly added special equipment in each reference production process within a preset period of time, and calculate the benchmark defective product ratio based on the process complexity adjustment factor and the corresponding defective product ratio of all reference production processes; The parameter correction execution unit is used to correct the delivery stability parameter based on the deviation between the specified defective proportion of the target production process and the benchmark defective proportion.

5. The AI-based intelligent sourcing and order optimization system according to claim 4 is characterized in that: In the process of calculating the benchmark defective ratio based on the process complexity adjustment factors and the corresponding defective ratios of all reference production processes, a preset benchmark defective ratio calculation formula is used; The calculation formula for the benchmark defective product ratio is: ,in Refers to the benchmark defective ratio, Refers to the total quantity of the reference production process, Refers to the A process complexity adjustment factor corresponding to the reference production process, Refers to the The percentage of defective products caused by the same type of equipment as the newly added special equipment in a reference production process within a preset period; In the calculation formula for the benchmark defective product ratio: ,in Refers to the A reference to the process complexity of the production process, Refers to the process complexity of the target production process.

6. The AI-based intelligent sourcing and order optimization system according to claim 4, characterized in that: The parameter correction execution unit specifically includes: A correction factor setting subunit is used to calculate the deviation between the specified defective product ratio of the target production process and the benchmark defective product ratio, and set it as the correction factor; The parameter adjustment subunit is used to call a preset parameter correction formula, adjust the delivery stability parameter based on the correction factor, and apply the corrected delivery stability parameter to the optimization of the orders to be executed.

7. The AI-based intelligent sourcing and order optimization system according to claim 6, characterized in that: The parameter correction formula is: ,in refers to the modified delivery stability parameter, refers to the uncorrected delivery stability parameters, Refers to the specified defective proportion of the target production process, Refers to the benchmark defective ratio, Refers to the correction factor, which is the deviation between the specified defective percentage of the target production process and the benchmark defective percentage. Refers to the adjustment coefficient corresponding to the correction factor.

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