Aluminum wheel quality index intelligent management method and system and storage medium

Abstract

The invention belongs to the technical field of aluminum wheel production quality inspection, and provides an aluminum wheel quality index intelligent management method and system and a storage medium, based on real-time data acquisition and field intelligent terminal manual data filling and reporting, the rate of finished products, the rework rate, the number of waste products and other data of each process are calculated, and through a preset threshold value and color marking, the quality index of the aluminum wheel is obtained. According to the method, the index state is fed back in real time, multi-dimensional visual analysis of data is realized through dynamic graph display and data drilling, response adjustment of an optimal process is carried out on a production machine in combination with an analysis result and processing suggestions, performance appraisal of field production personnel is associated based on each shift quality index, and the enthusiasm of the field production personnel is aroused. AI modeling is formed through data accumulation to gradually improve a quality index management system, the method is suitable for multi-link production scenes such as casting, machining and coating in the aluminum wheel manufacturing process, the stability of the production quality of aluminum wheel products is improved, and the quality abnormity response time is shortened.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of aluminum wheel manufacturing, and in particular to an aluminum wheel quality index intelligent management method and system and a storage medium BACKGROUND

[0002] At present, traditional aluminum wheel quality index management relies on manual paper records and experience judgment, and has problems such as data collection lag, single analysis dimension, slow response speed, low data accuracy, and the like, and it is difficult to meet the requirements of real-time, accuracy and intelligence of modern production. For example, manual statistics of quality data is prone to errors and omissions, and the root cause of quality fluctuation cannot be quickly located; the data of each production link is scattered, and there is a lack of systematic correlation analysis, which leads to difficulty in defect cause tracing; the quality index is disconnected with the production plan and personnel performance, and an effective management closed loop cannot be formed. With the deep application of industrial Internet of Things, big data analysis and artificial intelligence technology, real-time collection and fusion analysis of multi-source data such as process, quality inspection, energy consumption and environment in the production and manufacturing process have become the latest trend. However, most current enterprises have not established a perfect quality index intelligent management system, and cannot fully utilize massive data to realize dynamic monitoring, intelligent early warning and active optimization of quality. An aluminum wheel quality index intelligent management method and system that integrates multi-source data collection, intelligent analysis, performance linkage and mobile terminal collaboration solves the problems of data island, slow response and rough decision-making in traditional quality control, and promotes the transformation and upgrading of aluminum wheel manufacturing quality control from passive response to active prevention and from experience-driven to data-driven. SUMMARY

[0003] The application provides an aluminum wheel quality index intelligent management method, system and storage medium, based on real-time data collection and manual data filling of on-site intelligent terminals, calculates data such as yield rate, rework rate and scrap quantity of each process, feeds back the index state in real time through a preset threshold and color marking, realizes multi-dimensional visual analysis of data through dynamic graph display and data drilling, and adjusts the response of the production machine to the optimal process in combination with the analysis result and processing opinion, and mobilizes the enthusiasm of on-site production personnel based on the correlation between the quality index of each shift and the performance evaluation of on-site production personnel. Through data accumulation, AI modeling is gradually improved to form a quality index management system, which is suitable for aluminum wheel manufacturing whole-process quality statistics and analysis, including aluminum wheel low-pressure casting industry, mechanical processing, coating and other multi-industry production scenes, and improves the stability of product quality in the production industry.

[0004] To achieve the above-mentioned purpose, the application provides the following technical solutions: In a first aspect, on-site production machines and quality inspection equipment detection information are received in real time, data collection is performed on the quality inspection information, and manual entry of the quality inspection information by on-site mobile terminals is supported.

[0005] Through the collection and aggregation of on-site quality inspection data, real-time calculation of important quality inspection indicators is realized, reflecting the current quality management situation.

[0006] According to the multi-dimensional analysis of products, defects, and responsible processes, the key problem nodes of quality are found, and the knowledge base of quality improvement problems is formed through rectification tracking, which helps to improve the efficiency of solving similar quality problems in the later period.

[0007] In some embodiments, the steps include: building a "automatic + manual" dual-track data collection system. The system integrates various device interfaces and data collection standard protocols on the market through the background, and can realize real-time data collection of production equipment and quality inspection equipment through visual configuration, while supporting manual real-time input function of handheld terminal on site, ensuring data integrity and accuracy. After collecting data, relying on defect data statistics and finished product data statistics double modules, from three dimensions of process, machine and product, the comprehensive yield and comprehensive rework rate are deeply calculated, providing multi-angle and refined quality analysis and decision basis for production management.

[0008] In some embodiments, the steps include: setting target values for key quality indicators, and dynamically presenting the execution status of the indicators through a visual color identification system: Red: represents that the indicator does not meet the standard, triggering an early warning response; Green: indicates that the indicator meets the target value, showing compliance status.

[0009] Relying on real-time quality data collection and intelligent calculation model, a dynamic on-site quality monitoring system is built to realize rapid identification, accurate feedback and agile disposal of quality status.

[0010] In some embodiments, the steps include: the system automatically calculates the number of consecutive days that meet the target and the number of consecutive days that do not meet the target based on the preset quality indicator target value, and builds a dynamic trend monitoring model. Support triggering data drilling function through meeting / not meeting days, which can quickly retrieve quality indicator raw data of the last 7 days or custom time range, and dynamically present the indicator fluctuation track through line chart, trend chart and other visual maps, to help quality management personnel accurately locate abnormal period and analyze fluctuation causes, providing full-dimensional and traceable quantitative decision basis for quality improvement strategy formulation.

[0011] In some embodiments, the steps include: the system automatically analyzes the correlation of the two core quality indicators, comprehensive yield rate and rework rate, based on the defect data collected by the real-time collection engine, and screens and sorts the key defect factors with TOP / N impact degree. Through color distinction process identification, the responsibility process corresponding to the defect cause is visualized and marked, combined with the product life cycle analysis module and the defect tree traceability model, the minute-level accurate positioning of the problem occurrence link is realized. The system dynamically generates a daily waste product quantity comparison board (including the same period trend), superimposes the current period scrap product structure analysis (such as category proportion, scrap level distribution), and constructs a defect impact quantification model; at the same time, it supports data drilling penetration, which can be decomposed layer by layer to single product defect details, and intuitively presents the defect type proportion map (such as column chart, San-Ke diagram) of the defect high-occurrence product. The system automatically generates a defect quantity trend warning curve through a time series prediction algorithm to identify abnormal fluctuation risks in advance; at the same time, it integrates a multi-machine process parameter linkage analysis platform to support cross-correlation analysis of defect trend maps and equipment parameters (such as temperature, pressure, speed) to form a parameter-defect correlation matrix. The system automatically matches double-index influence coefficients (comprehensive yield rate influence value, rework rate influence value) for each defect data and scrap product data, and dynamically displays the contribution of each factor to the quality indicators through visualization components such as spider chart and waterfall chart, to assist quality management personnel to provide more comprehensive data analysis for on-site quality.

[0012] In some embodiments, the steps include: the system customizes exclusive data boards for each production process, dynamically presents the defect type distribution, data fluctuation trend and quality indicator change curve specific to the process through visual chart matrix. Support for sorting by responsibility yield rate and rework rate in forward / reverse order to quickly locate high-quality processes and improvement links. The system also realizes index penetration analysis in each process interface: clicking on the yield rate or rework rate data of any process can trigger the data drilling function, which can be decomposed layer by layer to batch-level and equipment-level quality details, combined with trend comparison chart and cross-sectional comparison with the same type of process, to accurately locate the fluctuation source. At the same time, taking waste product quantity and defect type as analysis dimensions, the system automatically generates a responsibility process defect attribution map, which intuitively presents the weak points of the process quality by quantifying the proportion of each defect cause. The board integrates dynamic data analysis module, supports custom filtering conditions and cross-analysis, and assists quality management personnel to mine data value from multiple perspectives of process-product-defect, to provide data-driven decision-making basis for process optimization and resource allocation.

[0013] In some embodiments, the system includes the following steps: in the waste analysis link of the system, when the system detects that the waste quantity exceeds the set threshold, the system automatically pushes the abnormal problem to the mobile phone of the preset problem person in the form of an alarm. The operator can real-time record the cause analysis and disposal measures of the current process waste. The system automatically triggers the whole-process tracking mechanism, and a special person follows up the measure execution progress and processing result to ensure problem closed-loop management. Intelligent knowledge recommendation and experience accumulation Intelligent solution recommendation: based on historical problem data, the system uses machine learning algorithms to automatically identify high-frequency defects, real-time push historical solutions for similar problems, and label the application success rate and recommendation probability of each solution to help users quickly locate effective treatment strategies. Dynamic knowledge base construction: the system automatically collects problem description, cause analysis, disposal measures and final results to construct a structured knowledge graph and form a dynamically updated quality problem solution knowledge base, realizing efficient accumulation and reuse of enterprise quality experience. Device process intelligent optimization For high-frequency device problems, the system automatically generates process parameter optimization suggestions through data analysis. After manual review and confirmation, it can automatically write back to the device control system to adjust the process parameters in real time, realizing the intelligent quality improvement closed loop of "problem discovery - solution recommendation - parameter optimization" and continuously improving product quality stability.

[0014] In some embodiments, the system binds the core quality indicators such as yield rate and rework rate with the performance evaluation system of the shift personnel, automatically generates individual scores and team ratings based on the real-time calculation of the index achievement, and synchronously updates to the performance evaluation system. This closed-loop management mode directly converts the quality control effect into quantifiable incentive basis, driving all employees to participate in quality improvement.

[0015] In some embodiments, the system realizes two-way data intercommunication with the production plan management system. After receiving the production plan instruction, it automatically retrieves the yield rate data of each process in the recent period, the retrieval time range can be automatically adjusted, and the theoretical comprehensive yield rate is calculated dynamically. Combined with the order demand quantity, the system reversely deduces and optimizes the production plan quantity of each process. Based on the past comprehensive yield rate of the product and the order demand quantity, the system automatically suggests the initial production plan quantity as the order demand quantity / comprehensive yield rate, and synchronously generates the process production scheduling priority list and resource demand warning prompt to ensure that the production plan meets the delivery requirements and effectively balances quality loss and capacity load. In some embodiments, the system newly develops a cross-platform mobile application, is compatible with iOS and Android operating systems, is deployed based on an industrial-grade secure network architecture, and ensures the full-link security of industrial production data in the mobile terminal through multiple protection mechanisms such as data encryption transmission, permission hierarchical control, access behavior auditing, and the like.

[0016] In a second aspect, an information collection module is configured to collect production product information, mold information, and production process information of a production device, collect product information, defect inspection information, and defect inspection pictures of a quality inspection device, collect real-time energy consumption information on an energy bus, collect on-site manual input of quality inspection defect information through a handheld terminal of the system, and reversely receive process and energy index adjustment instructions from the system to quickly adjust the optimal working condition of product production.

[0017] A processing module is configured to collect product information, production process information, quality inspection defect information, and energy consumption information, to define a bottom index algorithm, to calculate and aggregate quality inspection index data of each product in various dimensions based on the collected data, and to calculate and aggregate quality inspection index data of each product in various dimensions based on the collected data.

[0018] An analysis module is configured to aggregate and display single data or index result values calculated based on an index algorithm, to realize display and multi-index aggregation analysis of different dimensions of the index through a data graph, to record human analysis and judgment through human-computer interaction, to form a knowledge base, and to gradually form a processing mechanism for an optimal plan for on-site quality inspection information feedback and early warning.

[0019] In a third aspect, an embodiment of the present application further provides a storage medium for storing non-transitory computer-readable logic, which, when executed, can perform any one of the methods of claims 1-10.

[0020] In a fourth aspect, an embodiment of the present application further provides an aluminum wheel quality index intelligent management method, which includes: a memory; a processor; one or more computer system modules stored in the memory and configured to be executed by the processor, the one or more computer system modules including logic for implementing any one of the methods of claims 1-10, while recording each operation record for later analysis of the system and improvement of the overall quality of products.

[0021] Compared with the prior art, the present application has the following beneficial effects: By constructing an industrial Internet of Things data acquisition network, real-time and accurate acquisition of multi-source data such as production equipment, quality inspection equipment, and energy bus is realized, avoiding the lag, mistakes, and omissions of traditional manual acquisition. At the same time, the system analyzes and deeply mines the collected data in real time, realizes the integrity of the data, and provides comprehensive and reliable data support for quality control. Based on the preset quality index target value, the number of consecutive days of meeting / not meeting the standard is automatically counted, supporting data drilling and multi-dimensional analysis, and presenting the quality fluctuation trend through a visual map. The system can also cross-compare multi-station process parameters and defect trends, automatically generate quality index influence coefficients, accurately locate the root cause of quality problems, and improve decision-making efficiency and accuracy. The quality index is deeply bound with the on-duty personnel and team performance to form a "data - score - performance" closed-loop management mode, stimulating the quality responsibility consciousness of all personnel. The system is connected with the production plan management system, dynamically adjusts the product production plan quantity based on the historical yield, realizes the coordination of plan scheduling and quality control, and supports real-time alarm pushing, processing, and historical data query and analysis on the mobile terminal, breaking the time and space restrictions and improving the abnormal response speed and processing efficiency. A dynamic knowledge base is constructed, and solution recommendation probabilities are provided according to historical application situations to realize knowledge reuse and experience inheritance. For high-frequency equipment problems, the system can automatically rewrite the equipment modification process after manual confirmation, forming a continuous improvement closed loop from problem discovery to solution recommendation to final process optimization, and continuously improving product quality stability. The mobile terminal is deployed based on an industrial-grade security network architecture, and multiple protection mechanisms such as data encryption and permission grading are used to ensure data flow safety. At the same time, it is compatible with Apple and Android systems to meet diversified use requirements, ensuring data security and realizing convenient and efficient quality control. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to describe the technical solutions in the embodiments of the present application more clearly, the following will describe the logic of the system with more detailed description of the accompanying drawings. The accompanying drawings in the following description are some embodiments of the present application, and other accompanying drawings can be obtained by those skilled in the art without creative labor on the premise of not paying creative labor.

[0023] Figure 1 is a structure principle diagram of a quality index intelligent management system of the present application; Figure 2 is a flow of a quality index intelligent management system of the present application Figure 1 ; Figure 3 is a detailed flow of a quality index intelligent management system of the present application Figure 2 ; Figure 4 is a structure principle diagram of a quality index intelligent management system of the present application. DETAILED DESCRIPTION

[0024] The terms "first", "second", "third", and "fourth" and the like in the description and in the claims of the present application and the accompanying drawings are used for distinguishing between similar objects, not necessarily described in a particular order. Also, the terms "comprise", "comprising", "including", and "having" and any variations thereof in the specification are intended to cover a non-exclusive inclusion such that a process, method, system, product, or apparatus that comprises a list of steps or units are not necessarily limited to those listed steps or units but can include other not-listed steps or units, or can further include additional steps or units inherent to the process, method, product, or apparatus.

[0025] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. It is expressly understood that the embodiments described herein are merely examples from a whole class of comparable embodiments which those skilled in the art will readily appreciate. It is also expressly understood that the embodiments described herein have a wide application.

[0026] To solve the problems of traditional quality index management relying on manual paper records and experience judgment, such as data collection lag, single analysis dimension, slow response, and difficulty in meeting the requirements of real-time, accuracy and intelligence of modern production, the applicant proposes a method and system for aluminum wheel quality index intelligent management, which integrates multi-source data collection, intelligent analysis, performance linkage and mobile terminal collaboration, solves the problems of data island, slow response and rough decision-making in traditional quality control, and promotes the transformation and upgrading of manufacturing quality control from passive response to active prevention and from experience-driven to data-driven.

[0027] In a first aspect, real-time production machine and quality inspection equipment detection information is received, data collection is performed on the quality inspection information, and manual entry of the quality inspection information by a mobile terminal is supported.

[0028] Through collection and aggregation of on-site quality inspection data, real-time calculation of important quality inspection indexes is achieved, reflecting the current quality management situation.

[0029] According to the product, defect, responsibility process multi-dimensional analysis to find the key problem node of quality, through rectification tracking quality improvement problem to form knowledge base, auxiliary to improve the efficiency of solving similar quality problems in later period.

[0030] In some embodiments, including the step of constructing an "automatic + manual" dual-track data acquisition system. The system integrates various device interfaces and data acquisition standard protocols on the market through the background, and can realize automatic real-time data acquisition of production equipment and quality inspection equipment through visual configuration, while supporting manual handheld terminal real-time input function on site, ensuring data integrity and accuracy. After collecting data, relying on defect data statistics and finished product data statistics double modules, from the three dimensions of process, machine and product, the comprehensive yield and comprehensive rework rate are calculated in depth, providing multi-angle and fine quality analysis and decision basis for production management.

[0031] In some embodiments, including the step of setting target values for key quality indicators, and dynamically presenting the indicator execution state through a visual color identification system: Red: represents that the indicator does not meet the standard, triggering an early warning response; Green: indicates that the indicator meets the target value, showing compliance status.

[0032] Relying on real-time quality data acquisition and intelligent calculation model, a dynamic on-site quality monitoring system is constructed to realize rapid identification, accurate feedback and agile disposal of quality status.

[0033] In some embodiments, including the step of: the system automatically calculates the number of consecutive days that meet the target and the number of consecutive days that do not meet the target based on the preset quality indicator target value, and constructs a dynamic trend monitoring model. Support through the number of days that meet / do not meet the target to trigger data drilling function, can quickly retrieve quality indicator original data of continuous 7 days or custom time range, to dynamically present the indicator fluctuation track through line chart, trend chart and other visual maps, to assist quality management personnel to accurately locate abnormal period, analyze fluctuation causes, and provide full-dimensional and traceable quantitative decision basis for quality improvement strategy formulation.

[0034] In some embodiments, the steps include: the system automatically analyzes the correlation of the two core quality indicators, comprehensive yield rate and rework rate, based on the defect data collected by the real-time collection engine, and screens and sorts the key defect factors with TOP / N impact degree. Through color distinction process identification, the responsibility process corresponding to the defect cause is visualized and marked, combined with the product life cycle analysis module and the defect tree traceability model, the minute-level accurate positioning of the problem occurrence link is realized. The system dynamically generates a daily waste product quantity comparison board (including the same period trend), superimposes the current period scrap product structure analysis (such as category proportion, scrap level distribution), and constructs a defect impact quantification model; at the same time, it supports data drilling penetration, which can be decomposed layer by layer to single product defect details, and intuitively presents the defect type proportion map (such as column chart, San-Ke diagram) of the defect high-occurrence product. The system automatically generates a defect quantity trend warning curve through a time series prediction algorithm to identify abnormal fluctuation risks in advance; at the same time, it integrates a multi-machine process parameter linkage analysis platform to support cross-correlation analysis of defect trend maps and equipment parameters (such as temperature, pressure, speed) to form a parameter-defect correlation matrix. The system automatically matches double-index impact coefficients (comprehensive yield rate impact value, rework rate impact value) for each defect data and scrap product data, and dynamically displays the contribution of each factor to the quality indicators through visualization components such as spider chart and waterfall chart, to assist quality management personnel to provide more comprehensive data analysis for on-site quality.

[0035] In some embodiments, the steps include: the system customizes exclusive data boards for each production process, dynamically presents the defect type distribution, data fluctuation trend and quality indicator change curve specific to the process through visual chart matrix. Support for intelligent sorting in ascending / descending order according to responsibility yield rate and rework rate to quickly locate high-quality processes and improvement links. The system also realizes index penetration analysis in each process interface: clicking on the yield rate or rework rate data of any process can trigger the data drilling function, which can be decomposed layer by layer to batch-level and equipment-level quality details, combined with trend comparison chart and cross-sectional comparison with the same type of process, to accurately locate the fluctuation source. At the same time, taking waste product quantity and defect type as analysis dimensions, the system automatically generates a responsibility process defect attribution map, which intuitively presents the weak points of the process quality by quantifying the proportion of each defect cause. The board integrates dynamic data analysis module, supports custom filtering conditions and cross-analysis, and assists quality management personnel to mine data value from multiple perspectives of process-product-defect, to provide data-driven decision-making basis for process optimization and resource allocation.

[0036] In some embodiments, the system includes the following steps: in the waste analysis link of the system, when the system detects that the waste quantity exceeds the set threshold, the system automatically pushes the abnormal problem to the mobile phone of the preset problem person in the form of an alarm. The operator can real-time record the cause analysis and disposal measures of the current process waste. The system automatically triggers the whole-process tracking mechanism, and a special person follows up the measure execution progress and processing result to ensure problem closed-loop management. Intelligent knowledge recommendation and experience accumulation Intelligent solution recommendation: based on historical problem data, the system uses machine learning algorithms to automatically identify high-frequency defects, real-time push historical solutions for similar problems, and label the application success rate and recommendation probability of each solution to help users quickly locate effective treatment strategies. Dynamic knowledge base construction: the system automatically collects problem description, cause analysis, disposal measures and final results to construct a structured knowledge graph and form a dynamically updated quality problem solution knowledge base, realizing efficient accumulation and reuse of enterprise quality experience. Intelligent optimization of equipment and process For high-frequency equipment problems, the system automatically generates process parameter optimization suggestions through data analysis. After manual review and confirmation, it can automatically write back to the equipment control system to adjust the process parameters in real time, realizing the intelligent quality improvement closed loop of "problem discovery - solution recommendation - parameter optimization" and continuously improving product quality stability.

[0037] In some embodiments, the system binds core quality indicators such as yield rate and rework rate with performance evaluation system of shift personnel, automatically generates individual scores and team ratings based on real-time calculation of index achievement, and synchronously updates to performance evaluation system. This closed-loop management mode directly converts quality control effectiveness into quantifiable incentive basis, driving all employees to participate in quality improvement.

[0038] In some embodiments, the system realizes two-way data intercommunication with production plan management system. After receiving production plan instructions, it automatically retrieves yield rate data of each process in recent periods, automatically adjusts the time range of retrieval, dynamically calculates theoretical overall yield rate, combines order demand quantity to reversely deduce and optimize production plan quantity of each process. Based on past overall yield rate of products and order demand quantity, the system automatically suggests initial production plan quantity as order demand quantity / overall yield rate, and synchronously generates process production scheduling priority list and resource demand warning prompt to ensure that production plan meets delivery requirements and effectively balances quality loss and capacity load. In some embodiments, the system newly develops a cross-platform mobile application, is compatible with iOS and Android operating systems, is deployed based on an industrial-grade secure network architecture, and ensures the full-link security of industrial production data in the mobile terminal through multiple protection mechanisms such as data encryption transmission, permission hierarchical control, access behavior auditing, and the like.

[0039] In a second aspect, an information collection module is configured to collect production product information, mold information, and production process information of a production device, collect product information, defect inspection information, and defect inspection pictures of a quality inspection device, collect real-time energy consumption information on an energy bus, collect on-site manual input of quality inspection defect information through a handheld terminal of the system, and reversely receive process and energy index adjustment instructions from the system to quickly adjust the optimal working condition of product production.

[0040] A processing module is configured to collect product information, production process information, quality inspection defect information, and energy consumption information, to define a bottom index algorithm, to calculate and aggregate quality inspection index data of each product in various dimensions based on the collected data, and to calculate the index algorithm.

[0041] An analysis module is configured to aggregate and display single data or index result values calculated based on the index algorithm, to realize display and multi-index aggregate analysis of different dimensions of the index through a data graph, to record human analysis and judgment through human-computer interaction, to form a knowledge base, and to gradually form a processing mechanism for an optimal plan for on-site quality inspection information feedback and early warning.

[0042] In a third aspect, an embodiment of the present application further provides a storage medium for storing non-transitory computer-readable logic, which, when executed, can perform any one of the methods of claims 1-10.

[0043] In a fourth aspect, an embodiment of the present application further provides an aluminum wheel quality index intelligent management method, which comprises: a memory; a processor; one or more computer system modules stored in the memory and configured to be executed by the processor, the one or more computer system modules comprising logic for implementing any one of the methods of claims 1-10, while recording each operation record for system later analysis and improving overall product quality.

[0044] Compared with the prior art, the present application has the following beneficial effects: By constructing an industrial Internet of Things data acquisition network, real-time and accurate acquisition of multi-source data such as production equipment, quality inspection equipment, and energy bus is realized, avoiding the lag, errors, and omissions of traditional manual acquisition. At the same time, the system analyzes and deeply mines the collected data in real time, realizes the integrity of the data, and provides comprehensive and reliable data support for quality control. Based on the preset quality index target value, the number of consecutive days of meeting / not meeting the standard is automatically counted, supporting data drilling and multi-dimensional analysis, and presenting the quality fluctuation trend through a visual map. The system can also cross-compare multi-station process parameters and defect trends, automatically generate quality index influence coefficients, accurately locate the root cause of quality problems, and improve decision-making efficiency and accuracy. The quality index is deeply bound with the on-duty personnel and team performance to form a "data - score - performance" closed-loop management mode, stimulating the quality responsibility consciousness of all personnel. It is connected with the production plan management system, dynamically adjusts the product production plan quantity combined with the historical yield, realizes the coordination of plan scheduling and quality control, and supports real-time alarm pushing, processing, and historical data query and analysis on the mobile terminal, breaking the time and space restrictions, and improving the abnormal response speed and processing efficiency. A dynamic knowledge base is constructed, and solution recommendation probability is provided according to historical application conditions to realize knowledge reuse and experience inheritance. For high-frequency equipment problems, after manual confirmation, the device modification process can be automatically written, forming a continuous improvement closed loop from problem discovery to solution recommendation to final process optimization, continuously improving product quality stability. The mobile terminal is deployed based on an industrial-grade security network architecture, and through multiple protection mechanisms such as data encryption and permission grading, the safety of data flow is ensured. At the same time, it is compatible with Apple and Android systems, meeting the diversified use requirements, realizing convenient and efficient quality control under the premise of ensuring data security.

[0045] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application. Embodiments

[0046] As shown in Figure 1 Embodiment 1 provides a quality index intelligent management system, which includes an information acquisition module, a processing module, and an analysis module.

[0047] The information collection module is used for real-time connection of production equipment, quality inspection equipment, an energy bus and a terminal manual input, collection of production product information, mold information and production process information of the production equipment, collection of product information, defect inspection information and defect inspection pictures of the quality inspection equipment, collection of real-time energy consumption information on the energy bus, collection of on-site manual personnel input quality inspection defect information through a handheld terminal of the system, and reverse reception of process and energy index adjustment instructions given by the system, and rapid adjustment of optimal working conditions of product production through system instructions.

[0048] The processing module is used for collection of product information, production process information, quality inspection defect information and energy consumption information, self-defined bottom layer index algorithm, calculation and summary of quality inspection index data of each product in various dimensions based on algorithm reference collected data.

[0049] The analysis module is used for summary and display of single data or index result values calculated based on index algorithm, display and multi-index summary analysis of different dimensions of indexes through a data atlas, human-computer interaction, recording of human analysis and determination, formation of a knowledge base, and gradual formation of a processing mechanism of an optimal plan for on-site quality inspection information feedback warning.

[0050] As shown in Figure 2 A flowchart of an aluminum wheel quality index intelligent management method is given: an aluminum wheel quality index intelligent management method, comprising the following steps: S01: real-time collection of product information, production process information, quality inspection results, defect information, energy consumption data and environmental data, implementation of a reverse instruction function of the system to the equipment bus, and implementation of a data interaction function with other systems; S02: analysis and processing of extracted quality inspection information in the system, search of product types and mold information in product information for distinguishing production equipment machines, collection of collected data by the system, and formation of independent data source points by each collected data point; S03: configuration of a quality index algorithm for a field quality management method in the system, introduction of corresponding algorithms of collected data sources, display of final required index calculation data in the system, setting of corresponding target values for quality indexes, formation of a drillable data tree structure system by the system for the upper quality management indexes combined with the introduced data sources of the algorithm, convenient data drilling of quality management personnel through quality index data, and intuitive analysis of an influence coefficient and a proportion of each data source on upper or top quality management indexes through atlas introduction of data, to provide better data support for quality management personnel.

[0051] S04: The system records the handling of each quality indicator anomaly. The system integrates process components to form a complete closed-loop management mechanism for each quality issue, from occurrence and handling to final review. The system collects data on each quality anomaly, including keyword processing of handling opinions and changes in operating condition data indicators, to form a knowledge base. When the system detects a similar problem, it automatically and intelligently retrieves information from the knowledge base and recommends solutions with high to low success rates.

[0052] SO5: Based on the summarized knowledge base information, the system integrates more operating parameters and makes suggestions on on-site process, energy consumption, environment and other operating indexes through manual confirmation before rewriting instructions. At the same time, the system interacts with the quality indicator data, the performance management system and the production planning system to help improve personnel initiative and the feasibility of production plans.

[0053] like Figure 3 The diagram shows a detailed flowchart of an intelligent management system for quality indicators in Example 1.

[0054] 1) The system employs a dual-mode approach—timed acquisition and conditional triggering—to synchronously collect real-time data from production equipment, energy-consuming equipment, and quality inspection equipment. It also supports manual data entry at the terminal as a supplement, ensuring the integrity and timeliness of the quality inspection data source. Based on a custom index algorithm, the system aggregates and correlates multi-source data, automatically generating calculated quality index data. The system supports multi-level data penetration queries, allowing users to drill down based on calculation results, tracing the next level of data related to the index calculation formula down to the underlying original data source, achieving full-chain data traceability. Through the visualization analysis module, the system intuitively displays the impact percentage of data from various dimensions on the top-level quality index in various chart formats. Combined with the dynamic curve comparison function, it can present the correlation trend between changes in operating parameters and fluctuations in quality inspection indicators in real time, assisting quality management personnel in accurately identifying key factors affecting quality fluctuations and providing quantitative basis for process optimization and decision-making.

[0055] For example, the system collects production machine, energy consumption bus, quality inspection equipment data in real time to form the bottom data source. The latest value of real-time point can be updated in the system to view the real-time collection of each data source. The system front end can realize the configuration of custom index algorithm, support the corresponding configuration function of introducing index algorithm to the data source independently by the user. When the index algorithm points correspond to the corresponding data source, the system will automatically display the current latest quality index calculation result according to the calculation result. For example, the comprehensive yield, the system supports the downward drilling of the calculation result value, opens the data tree structure of calculating the comprehensive yield, and the system can view the total amount of products and the number of defective products in the current or last period. It can also continue to view the number of defective products and the number of defects in two dimensions. It can also continue to use the chart component to view the proportion of each product or each defect to the comprehensive yield. The system can also view the impact of a certain defect of a certain product on the comprehensive yield or the proportion of the product defect rate under the defect product dimension. Moreover, the system can view the trend of the comprehensive yield through the chart component, and also compare the trend of the underlying indicators. The system also supports the real-time trend change of a certain production process value or energy consumption combined with the comprehensive yield and other indicators for unified comparison, which can help quality inspectors analyze the on-site quality inspection and improve the on-site comprehensive yield.

[0056] 2) The system provides an abnormal data analysis module for each quality index abnormal data. Quality inspectors can write executable opinions on quality abnormal indicators combined with the data collected by the system and the on-site situation. They can also add on-site process data or working condition data involved in the modification scheme in the system. Through the system process component, the system will push the executable opinions to the responsible personnel and managers. After the responsible personnel and managers approve the process, the on-site operators can execute the operation according to the modification opinions until the abnormal data is restored to normal. Then the on-site operators review the process to indicate the success of the abnormal record processing. The system will complete the full data chain record according to the abnormal processing process to form a knowledge base, which will recommend intelligent opinions for similar abnormal data in the future.

[0057] For example, when a certain product defect index is abnormal on site, the quality inspector can complete the analysis through the system combined with the actual situation on site, and can initiate the opinion processing flow for the current abnormal index. The flow can reference the current abnormal index value, write the processing scheme, and load the modified on-site process and working condition parameters. When the flow is initiated, through the self-defined flow, the scheme will be gradually transferred to the quality responsible person and the production management personnel. When the responsible person and the management personnel approve the flow, the on-site operator can execute the modification scheme according to the processing opinion. If the on-site defect index abnormal result is improved, the on-site operator performs review, the system collects the data related to the operation machine, and forms a knowledge base. When similar defects are encountered next time, the system will directly recommend the processing opinions of the previous times to assist the on-site personnel in quickly positioning and solving the abnormality.

[0058] 3) The system is connected with the performance management system and the production plan management system. The target value of the quality index can be set. The current quality index value can be compared with the target value every period, and the performance coefficient can be calculated. The performance of the shift team or personnel can be directly calculated and sent to the human resource or performance management system. The quality inspector can be directly encouraged through the personnel salary to control the on-site quality inspection system. The system also calculates the comprehensive yield rate of several consecutive shifts or a single shift and a single product. When the system collects the next production plan quantity of the product, the production plan can be directly calculated through the comprehensive yield rate, which brings great convenience to the production plan work.

[0059] For example, when setting the comprehensive yield rate index, if the target value is set to 97%, and the performance coefficient is less than 1% with a deduction of 1000 yuan, and more than 1% with an award of 1000 yuan, in the system, the performance index can be directly configured through the algorithm. The system will automatically calculate the bonus or locked salary of each team according to the algorithm according to the comprehensive yield rate of each team. The on-site quality control situation of each team can be directly fed back through the salary in hand. The system can also bind the single index with the independent individual bonus. Finally, the monthly bonus can be directly executed according to the performance system data.

[0060] Or for a single product, the comprehensive yield rate last month is 97.5%. Each shift has a comprehensive yield rate. We need to deliver 10000 products next month. The system will automatically collect the next month plan of the production plan system combined in the system. The production plan personnel can choose to calculate according to the comprehensive yield rate last month or according to the average comprehensive yield rate of the last few shifts (which can be self-defined). For example, according to the comprehensive yield rate 97.5% last month, the production plan next month is 10000 / 97.5%≈10256, and the system will automatically generate data and send it to the production plan system. EMBODIMENT

[0061] A storage medium is provided in embodiment 2 for storing non-transitory computer readable logic that, when executed, can perform a logic that implements any one of claims 1-10. With the storage medium, for example, a quality index intelligent management system, the stored non-transitory computer readable logic is executed by a computer to collect in real time production equipment, quality inspection equipment, production product information on the energy consumption bus, mold information, production process information, X-ray machine inspection results, X-ray machine inspection defects, X-ray machine inspection pictures, size defect information, paint film thickness defects, and other quality inspection information, as well as compressed air volume, water flow, electricity consumption, natural gas flow, and other energy consumption information. The system stores all quality inspection defect records on site, corresponding production equipment information, production process information, energy consumption information, and subsequent system optimization records for each quality improvement plan, according to quality inspection defect inspection statistical rules, to facilitate the generation of a knowledge base for later knowledge storage.

[0062] The storage medium can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may, for example, include random access memory (RAM) and / or cache memory, etc. Non-volatile memory may, for example, include read-only memory (ROM), a hard disk, erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, a flash memory, etc. One or more computer program modules can be stored on the computer readable storage medium, and the processor can run one or more computer program modules to implement the index calculation rules of multi-data source matching multi-algorithm. Various application programs and various data used and / or generated by the application programs can also be stored in the computer readable storage medium. In some embodiments, the memory medium can be provided at the server end (or cloud end), for example, the non-transitory computer readable logic can be stored in the cloud or cloud disk, while storing all quality inspection important index data related data. Embodiment

[0063] As Figure 4 As shown in the embodiment of the present application, a quality index intelligent management system is also provided, which includes a memory, a processor, a communication interface, and one or more computer system modules. The one or more computer system modules are stored in the memory and configured to be executed by the processor, and the one or more computer system modules include logic for implementing a method described in any of the above embodiments. The processor is signal connected to the memory and the communication interface, and the memory, the processor, and the communication interface can be interconnected through a bus system and / or other forms of connection mechanism (not shown).

[0064] The processor is configured to execute non-transitory computer-readable logic that, when executed by the processor, can perform one or more steps of a method recited in any of the embodiments described above. For example, the processor can be a central processing unit (CPU), a digital signal processor (DSP), or other form of processing unit that has data processing and / or program execution capabilities, such as a field-programmable gate array (FPGA), etc. For example, the central processing unit (CPU) can be an X86 or ARM architecture, etc. The processor can be a general purpose processor or a special purpose processor. The processor can be configured to implement the quality inspection management personnel's control over the on-site quality execution from various dimensions by collecting various parameters and combining with the underlying configuration algorithm.

[0065] The memory is configured to store non-transitory computer-readable logic (one or more computer program modules). For example, the memory can include any combination of one or more computer system products, which can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. For example, the volatile memory can include random access memory (RAM), cache memory, etc. For example, the non-volatile memory can include read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), compact disc read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer system modules can be stored on the computer-readable storage medium, and the processor can execute the one or more computer system modules to achieve real-time calculation accuracy of multiple computing indicators. Various systems and various data and various data used and / or generated by application programs can also be stored in the computer-readable storage medium.

[0066] In some embodiments, the memory and the processor, etc. can also be arranged at the server end (or cloud end), for example, one or more computer program modules can be stored in the cloud or cloud disk.

[0067] The communication interface connects the system, all on-site production equipment, quality inspection equipment, energy consumption bus and other application systems, and receives and issues information. In some embodiments, the real-time production equipment on the production equipment machine, the production product information on the energy consumption bus, the mold information, the production process information, the X-ray machine inspection results, the X-ray machine inspection defects, the X-ray machine inspection pictures, the size defect information, the paint film thickness defect information and the compressed air volume, the water flow, the electricity consumption, the natural gas flow and other energy consumption information can be linked through the communication interface (such as wired local area network, wireless local area network, 3G / 4G / 5G communication network, Bluetooth, etc.) based on the corresponding communication protocol to realize the real-time interaction of the system bottom layer data. For example, the communication protocol can be any applicable communication protocol such as Bluetooth communication protocol, Ethernet, serial interface communication protocol, parallel interface communication protocol, etc., and the embodiments of the present disclosure do not make any limitation.

[0068] The above describes the embodiments of the present application in detail, and the principles and implementation modes of the present application are described by applying specific examples. The above description of the embodiments is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed; in view of the above, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for intelligent management of aluminum wheel quality indicators, characterized in that, Includes the following steps: It can receive real-time testing information from production machines and quality inspection equipment on site, collect data on quality inspection information, and also support manual entry of quality inspection information by on-site mobile terminals; By collecting and summarizing on-site quality inspection data, the system enables real-time calculation of key quality inspection indicators, reflecting the current quality management situation. By analyzing products, defects, and responsible processes from multiple dimensions, key quality issues are identified. Through rectification and tracking of quality improvement issues, a knowledge base is formed to help improve the efficiency of resolving similar quality problems in the future.

2. The intelligent management method for aluminum wheel quality indicators according to claim 1, characterized in that, Including the following steps: The system constructs an "automatic + manual" dual-track data acquisition system. Through the backend, it integrates various equipment interfaces and data acquisition standard protocols available on the market. It can realize automated real-time data acquisition from production equipment and quality inspection equipment through visual configuration, while also supporting real-time on-site data entry via handheld terminals to ensure data integrity and accuracy. After the collected data is aggregated and cleaned in real time on the local server, it relies on two modules: defect data statistics and finished product data statistics. From the three dimensions of process, machine, and product, it deeply calculates the comprehensive yield rate and comprehensive rework rate, providing multi-perspective and refined quality analysis and decision-making basis for production management.

3. The intelligent management method for aluminum wheel quality indicators according to claim 2, characterized in that, Including the following steps: Set target values ​​for key quality indicators and dynamically present the indicator execution status through a visual color-coding system: Red: Indicates that the indicator has not been met, triggering an early warning response; Green: Indicates that the target value has been achieved, showing the compliance status; By relying on real-time quality data acquisition and intelligent computing models, a dynamic on-site quality monitoring system is constructed to achieve rapid identification, accurate feedback, and agile handling of quality conditions.

4. The intelligent management method for aluminum wheel quality indicators according to claim 2, characterized in that, Including the following steps: Based on preset quality indicator target values, the system automatically counts the number of consecutive days that meet the standards and the number of consecutive days that do not meet the standards, and builds a dynamic trend monitoring model. It supports data drill-down function triggered by the number of days that meet or do not meet the standards, which can quickly retrieve the original data of quality indicators for 7 consecutive days or a custom time range, and dynamically present the indicator fluctuation trajectory in visual charts such as line charts and trend charts. This helps quality management personnel to accurately locate abnormal cycles and analyze the causes of fluctuations, and provides a full-dimensional and traceable quantitative decision-making basis for the formulation of quality improvement strategies.

5. The intelligent management method for aluminum wheel quality indicators according to claim 2, characterized in that, Including the following steps: Based on a real-time defect data acquisition engine, the system automatically performs correlation analysis on two core quality indicators—overall yield and rework rate—daily, screening and ranking the top / N most influential critical defect factors. It uses color-coded process identifiers to visually label the responsible processes corresponding to defect causes, and combines a product lifecycle analysis module with a defect tree-based tracing model to achieve minute-level precise location of the problem's occurrence. The system dynamically generates daily scrap quantity comparison dashboards (including month-on-month / year-on-year trends), overlaid with current period scrap product structure analysis (such as category proportion and scrap grade distribution), constructing a quantitative model of defect impact. It also supports data drill-down, breaking down the data layer by layer to individual product defect details, intuitively presenting a defect type proportion chart (such as bar charts and Sankey diagrams) for products with high defect incidence. The system automatically generates a defect quantity trend warning curve through a time-series prediction algorithm to identify abnormal fluctuation risks in advance. Furthermore, it integrates a multi-machine process parameter linkage analysis platform, supporting cross-correlation analysis of defect trend charts with equipment parameters (such as temperature, pressure, and speed) to form parameter- Defect correlation matrix: The system automatically matches the dual-indicator influence coefficients (overall yield influence value and rework rate influence value) for each defect data and scrapped product data, and dynamically displays the contribution of each factor to the quality indicators through visualization components such as spider diagrams and waterfall charts, assisting quality management personnel in providing more comprehensive data analysis on on-site quality.

6. The intelligent management method for aluminum wheel quality indicators according to claim 5, characterized in that, Including the following steps: The system provides customized data dashboards for each production process, dynamically presenting the distribution of defect types, data fluctuation trends, and quality indicator change curves specific to each process through a visual chart matrix. It supports intelligent sorting by responsible finished product rate and rework rate in ascending / descending order, quickly identifying high-performing processes and areas for improvement. The system also implements indicator drill-down analysis within each process interface: clicking on the finished product rate or rework rate data for any process triggers a data drill-down function, breaking down the data layer by layer to batch-level and equipment-level quality details. Combined with trend comparison charts and horizontal benchmarking against similar processes, the system accurately pinpoints the root causes of fluctuations. Simultaneously, using scrap quantity and defect type as analytical dimensions, the system automatically generates defect attribution maps for responsible processes, intuitively presenting process quality weaknesses by quantifying the proportion of each defect cause. The dashboard integrates a dynamic data analysis module, supporting customizable filtering conditions and cross-analysis, assisting quality management personnel in mining data value from multiple perspectives—process, product, and defect—providing data-driven decision-making support for process optimization and resource allocation.

7. The intelligent management method for aluminum wheel quality indicators according to claim 2, characterized in that, Including the following steps: In the waste analysis stage of the system, when the system detects that the amount of waste exceeds the set threshold, the system will automatically push the abnormal problem to the mobile phone of the preset person in charge of the problem in the form of an alarm. Operators can enter the cause analysis and disposal measures of the waste in the current process in real time. The system automatically triggers the full-process tracking mechanism, and a dedicated person follows up on the progress of the measures and the results of the handling to ensure closed-loop management of the problem. Intelligent knowledge recommendation and experience accumulation Intelligent solution recommendation: Based on historical problem data, the system uses machine learning algorithms to automatically identify high-frequency defects, push historical solutions for similar problems in real time, and mark the application success rate and recommendation probability of each solution to help users quickly locate effective handling strategies; Dynamic knowledge base construction: The system automatically collects problem descriptions, cause analyses, handling measures, and final results to build a structured knowledge graph, forming a dynamically updated knowledge base for quality problem solutions, enabling the efficient accumulation and reuse of enterprise quality experience; Intelligent optimization of equipment processes: For issues with high-frequency equipment, the system automatically generates process parameter optimization suggestions through data analysis. After manual review and confirmation, the suggestions can be automatically written back to the equipment control system to adjust process parameters in real time. This achieves an intelligent quality improvement closed loop of "problem discovery - solution recommendation - parameter optimization," continuously improving product quality stability.

8. The intelligent management method for aluminum wheel quality indicators according to claim 2, characterized in that, Including the following steps: The system deeply integrates core quality indicators such as overall yield and rework rate with the performance evaluation system of on-duty personnel. Based on the real-time calculation of indicator achievement, it automatically generates individual scores and team ratings, and updates them to the performance appraisal system simultaneously. This closed-loop management model directly transforms the effectiveness of quality control into quantifiable incentive criteria, driving all employees to participate in quality improvement.

9. The intelligent management method for aluminum wheel quality indicators according to claim 2, characterized in that, Including the following steps: The system achieves two-way data exchange with the production planning management system. After receiving production planning instructions, it automatically retrieves the yield rate data of each process in recent periods. The time range for retrieval can be automatically adjusted. By dynamically calculating the theoretical comprehensive yield rate and combining it with the order demand quantity, the system reverse-engineers and optimizes the production plan quantity of each process. Based on the product's past comprehensive yield rate and the order demand quantity, the system automatically suggests an initial production plan quantity of order demand quantity / comprehensive yield rate, and simultaneously generates a process scheduling priority list and resource demand warning prompts to ensure that the production plan not only meets delivery requirements but also effectively balances quality loss and capacity load.

10. The intelligent management method for aluminum wheel quality indicators according to claim 1, characterized in that, Including the following steps: The system features a newly developed cross-platform mobile application that is fully compatible with iOS and Android operating systems. Deployed based on an industrial-grade secure network architecture, it ensures the security of industrial production data throughout the mobile terminal through multiple protection mechanisms, including encrypted data transmission, hierarchical permission control, and access behavior auditing.

11. A quality indicator intelligent management system, characterized in that, Includes the following steps: The information acquisition module is used to connect production equipment, quality inspection equipment, energy bus and manual input terminal in real time. It collects production product information, mold information and production process information from production equipment, product information, defect inspection information and defect inspection pictures from quality inspection equipment, and real-time energy consumption information from the energy bus. Through the system's handheld terminal, it collects quality inspection defect information entered by on-site manual personnel and receives process and energy index adjustment instructions from the system. It can quickly adjust the optimal working conditions for product production through system instructions. The processing module is used to collect product information, production process information, quality inspection defect information, and energy consumption information. It can customize the underlying indicator algorithm, and calculate and summarize the quality inspection indicator data of each product under various dimensions based on the collected data and the algorithm reference. The analysis module is used to summarize and display individual data or indicator results calculated based on indicator algorithms. It realizes the display of different dimensions of indicators and the summary analysis of multiple indicators through data graphs. Through human-computer interaction, it records human analysis and judgment, forms a knowledge base, and gradually forms a processing mechanism for the optimal plan for on-site quality inspection information feedback and early warning.

12. A storage medium for storing non-transitory computer-readable logic, which, when executed, can perform any one of the logics of claims 1-10.

13. A method for intelligent management of aluminum wheel quality indicators, comprising: Memory; processor; One or more computer system modules, the one or more computer system modules being stored in the memory and configured to be executed by the processor, the one or more computer system modules including logic for implementing a method according to any one of claims 1-10, while recording each operation record for later system analysis to improve overall product quality.

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