Integrated quality acquisition and budget dynamic regulation and control system based on multi-source data

Abstract

The invention discloses an integrated quality acquisition and budget dynamic regulation and control system based on multi-source data, and relates to the field of production management systems. The regulation and control system comprises a budget dynamic regulation and control system, and the budget dynamic regulation and control system is composed of a budget evaluation module, a real-time data acquisition module, a dynamic model establishment module, a visual interaction module and an integrated regulation and control module. The dynamic model establishing module performs model updating based on real-time data, and the integrated regulation and control module regulates and controls the production process through fused data. According to the system, real-time and intelligent budgeting of the whole process is achieved by collecting production data in real time, updating budgeting results in real time and preventing data lag, the quality of the whole project is monitored in real time by analyzing reasons of defective products, the reject ratio trend is predicted based on the real-time data, technological parameters are automatically adjusted according to the prediction results, and the budgeting efficiency is improved. And the production quality is improved, and the production process is dynamically monitored and intelligently analyzed.

Description

Technical Field

[0001] This invention relates to the field of production management systems, specifically a system for quality acquisition and dynamic budget control based on multi-source data integration. Background Technology

[0002] Traditional budget management is a management tool that uses financial data as its core and adopts fixed and static budgeting methods to plan, control, and evaluate a company's operating activities and financial results in a specific future period. In essence, it is a top-down resource allocation and control mechanism for companies to achieve their strategic goals. Budget management is typically based on production data and resource control. However, the complexity of traditional overall production processes, the large number of small-scale products, the large fluctuations in planned quantities, and the significant differences in actual production quality across different projects make project troubleshooting difficult. It is also difficult to interconnect the production status of each project in a real-time and effective manner, which increases the difficulty of budget management. Production data is mostly updated manually, resulting in data transmission and update delays. This makes it difficult to handle defective products in a timely manner during production, leading to high scrap costs. Furthermore, it is impossible to accurately and quickly identify the responsible projects, causing quality deterioration and reducing the real-time performance and accuracy of budget management. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a quality acquisition and budget dynamic control system based on multi-source data integration, which solves the problems of data lag, poor quality control effect, and low automation level of enterprise quality management.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a multi-source data integration quality acquisition and budget dynamic control system, comprising a budget dynamic control system, which consists of a budget evaluation module, a real-time data acquisition module, a dynamic model building module, a visualization interaction module, and an integrated control module. The budget evaluation module performs a preliminary budget assessment by comparing predicted production costs with historical data. The real-time data acquisition module uses a sensor network to acquire real-time data from the production process and provides a data foundation. The dynamic model building module establishes a dynamic model of production data and budget evaluation and updates the model based on the acquired real-time data. The visualization interaction module displays the budget evaluation and dynamic model. The integrated control module fuses the acquired multi-source data and performs real-time control of the production process and budget based on the fused data.

[0005] Preferably, the budget assessment module includes a database, a budget model, a model optimization module, and a budget comparison module. The database stores historical production data and real-time production data based on PostgreSQL. The budget model constructs a linear model between production costs and production data using linear regression and gradient boosting algorithms and assesses the production budget. The model optimization module optimizes the budget model using linear programming and genetic algorithms. The budget comparison module calculates actual values ​​from historical data in the database and compares them with the budget assessment.

[0006] Preferably, the real-time data acquisition module includes a monitoring camera, a photoelectric sensor, a temperature sensor, and a smart meter. The monitoring camera is installed in the production workshop to monitor the production process, manpower, and equipment. The photoelectric sensor is set on the production line and equipment to measure the quantity of products and the quantity of defective products. The temperature sensor is used to detect the operating temperature of the equipment. The smart meter is used to monitor the power consumption of the production equipment.

[0007] Preferably, the dynamic model building module includes a defect analysis module, a dynamic model, a model update module, and a chart module. The defect analysis module analyzes the causes of defective products based on visual AI detection and data entry. The dynamic model uses discrete event simulation software to construct a production dynamic model based on production data and budget assessment. The model update module updates the original model based on Bayesian regression and online learning. The Bayesian regression uses historical data to perform prior distribution on the original model to form a new prior. The acquired real-time data is input into the model after prior distribution to combine the real-time data with the model and obtain a new data likelihood. The new prior and the new data likelihood are substituted into Bayes' theorem to calculate a new model. The chart module establishes a curve of predicted value versus time change based on updated cost prediction, completion time prediction, and deviation analysis.

[0008] Preferably, the integrated control module includes a spatiotemporal unification module, a data association module, a situation assessment module, and a process optimization module. The spatiotemporal unification module unifies the collected multi-source data into a single time and space coordinate system through coordinate transformation and clock alignment, calibrating the temporal and spatial differences generated by independent sensor acquisition. The data association module associates different source data with entity objects through data fusion. Furthermore, the data association module filters data and trajectories to narrow the range of associated data, calculates the numerical cost of the filtered data and trajectories to represent the probability that they belong to the same target, and allocates associations between data and trajectories based on the principle of minimizing the total association cost. The situation assessment module constructs an overall situation understanding based on associated data and assesses the relationships between entity objects. The situation assessment module is based on logical reasoning and machine learning methods, and combines knowledge graphs to associate perception and cognition, and establishes connections between associated data and entity objects. The process optimization module optimizes data source selection, resource allocation and fusion strategy by monitoring the fusion process itself. The process optimization module continuously measures and evaluates the performance indicators and resource status of the fusion system, compares the current performance with the expected performance, identifies performance bottlenecks, and predicts the potential impact of different resource allocation strategies on system performance and task efficiency. Based on the assessment and prediction, the performance bottlenecks are optimized.

[0009] Preferably, the gradient boosting algorithm ranks the importance of features through nonlinear relationships, the linear programming performs revenue optimization based on multiple cost and constraint conditions, the genetic algorithm is used to optimize constrained and nonlinear problems, and the budget comparison module maps keys to fixed-length hash values ​​using a hash function, the hash value corresponds to the address of the storage bucket, the location of the data stored in the address, obtains the order production cost data in the data storage address, and calculates the actual value according to the order quantity ratio.

[0010] Preferably, the real-time data acquisition module processes data on working hours, output, equipment status, work order progress, and defective product quantity based on the manufacturing execution system, and uses the data acquisition and monitoring control system and IoT sensors to process real-time energy consumption, equipment speed, temperature, pressure, and process parameters.

[0011] Preferably, the defect analysis module integrates IoT, machine vision, data analysis, and AI systems. The module comprises a core analysis layer, a visualization layer, and an action feedback layer. The core analysis layer locates the root causes of defects through multidimensional analysis, correlation analysis, and anomaly detection. The multidimensional analysis uses drill-down, slicing, and block operations to cross-analyze the distribution of defective data from multiple dimensions. The correlation analysis employs hypothesis testing to explore the statistical correlation between defective phenomena and potential causes. The anomaly detection analyzes data from the real-time data acquisition module to provide early warnings for defective data. The visualization layer presents the analysis results in charts and graphs. The action feedback layer links the analyzed root causes to corrective measures, improves the root causes of defects, and tracks the implementation status of these measures.

[0012] Preferably, the Bayesian regression uses real-time collected production data and calculates the posterior distribution of parameters according to Bayes' theorem; the online learning utilizes an algorithm that supports incremental learning and makes small updates to the model based on real-time collected data; and the deviation analysis calculates the difference between planned value, earned value, and actual cost and generates early warnings.

[0013] Preferably, the visualization interaction module performs data conversion, visual design, and human-computer interaction based on a chart type library, a mapping rule engine, a visualization base library, an interaction layer, and a interface layer, displaying production data and budget assessments on the screen. The mapping rule engine is used to map data fields to visual channels, and the interaction layer enables users to interact with the visualization charts through basic interaction, data interaction, and query-based interaction.

[0014] This invention discloses a system for quality acquisition and dynamic budget control based on multi-source data integration, which has the following beneficial effects: A budget assessment model is established using data from the database. Production data is collected in real time using a real-time data acquisition module. The production data is input into the budget assessment model, and the budget results are updated in real time to prevent data lag. This achieves real-time and intelligent budgeting throughout the entire process. Furthermore, by analyzing the causes of defective products, the quality of the entire process is monitored in real time. Based on real-time data, the defect rate trend is predicted, and process parameters are automatically adjusted according to the prediction results to improve production quality. The multi-source data collected in real time is integrated and correlated to dynamically monitor and intelligently analyze the production process, providing accurate budgeting and rapid decision-making. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the dynamic control system framework of the present invention; Figure 2 This is a schematic diagram of the budget evaluation module framework of the present invention; Figure 3 This is a schematic diagram of the real-time data acquisition module framework of the present invention; Figure 4 A schematic diagram of the module framework for the dynamic model of this invention; Figure 5 This is a schematic diagram of the integrated control module framework of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This application provides a multi-source data integration-based quality acquisition and dynamic budget control system, which solves the problems of data lag, poor quality control, and low automation in enterprise quality management. It establishes a budget evaluation model using data from a database, collects production data in real time using a real-time data acquisition module, inputs the production data into the budget evaluation model, and updates the budget results in real time to prevent data lag. This achieves real-time and intelligent budgeting throughout the entire process. Furthermore, by analyzing the causes of defective products, it monitors the quality of the entire process in real time, predicts defect rate trends based on real-time data, and automatically adjusts process parameters according to the prediction results to improve production quality. The system integrates and correlates multi-source data collected in real time to dynamically monitor and intelligently analyze the production process, providing accurate budgeting and rapid decision-making.

[0019] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0020] This invention discloses a system for quality acquisition and dynamic budget control based on multi-source data integration.

[0021] According to the appendix Figures 1-5As shown, the system includes a dynamic budget control system, which consists of a budget assessment module, a real-time data acquisition module, a dynamic model building module, a visualization and interaction module, and an integrated control module. The budget assessment module performs a preliminary assessment of the budget by comparing the predicted production cost with historical data. The real-time data acquisition module uses a sensor network to acquire real-time data from the production process and provides a data foundation. The dynamic model building module establishes a dynamic model of production data and budget assessment and updates the model based on the acquired real-time data. The visualization and interaction module is used to display the budget assessment and dynamic model. The integrated control module fuses the acquired multi-source data and performs real-time control of the production process and budget based on the fused data.

[0022] A method for quality acquisition and dynamic budget control based on multi-source data integration includes the following steps: Step S1: Establish a database based on historical production data, preprocess data on human resources, equipment, raw materials, construction period and capacity, import the standardized data into the database, and update the database in real time. Step S2: By extracting features from historical data in the database, establish a linear relationship between project costs and features, construct a budget evaluation model, evaluate production costs, and rank features according to their importance. Step S3: By retrieving the historical data of the product in the previous quarter, calculate the average actual production cost, compare the average with the budget assessment value, determine the rationality of the budget assessment value, and optimize and adjust the budget assessment. Step S4: Use surveillance cameras and sensor arrays to collect data on equipment, personnel, materials and output during the production process, detect the number of defective products, process the collected data, input it into the budget evaluation model, and update the budget results in real time. Step S5: By monitoring the product manufacturing process, analyze the causes of defective products and classify and number them. Store the numbering information in the database, match the causes of defective products with the manufacturing process, and improve the manufacturing process according to the defective product cause number. Step S6: Build a dynamic model based on the data collected in step S4. Use the dynamic model to generate an initial budget prediction based on the feature values ​​of the collected data. Input new production data and calculate the posterior distribution of the parameters according to Bayes' theorem to update the dynamic model. Step S7: Through dynamic modeling, forecast costs and completion times, perform deviation analysis on costs and completion times, calculate the differences between planned values, earned values ​​and actual costs, generate early warnings, and display the curve of budget forecast values ​​changing over time.

[0023] The budget assessment module includes a database, a budget model, a model optimization module, and a budget comparison module. The database, based on PostgreSQL, stores historical and real-time production data. PostgreSQL is an open-source object-relational database management system that uses and extends the SQL language to store historical production data, real-time data, and budget assessments. The budget model constructs a linear model between production costs and production data using linear regression and gradient boosting algorithms to assess the production budget. By importing production data into the budget model, the production cost budget is evaluated to obtain a preliminary estimate. The model optimization module uses linear programming and genetic algorithms to optimize the budget model. The system optimizes and continuously monitors model performance. As new projects are completed, new data is collected, and the model is retrained periodically to maintain its accuracy. The budget comparison module calculates the actual value from historical data in the database and compares it with the budget assessment. By comparing the estimated value generated by the preliminary assessment with the actual value calculated based on historical data, if the difference between the actual value and the estimated value is greater than 10%, it indicates that the cost of the budget assessment far exceeds the actual production cost, the budget assessment is unreasonable, the estimated value is judged to be inaccurate, and the production cost budget is recalculated. If the difference between the actual value and the estimated value is less than or equal to 10%, it indicates that the cost of the budget assessment is close to the actual production cost, and the estimated value is judged to be accurate.

[0024] Gradient boosting algorithms rank feature importance through nonlinear relationships. They sequentially train a series of weak learners or simple models, each focusing on correcting errors made by the previous model. These weak models are then combined into a powerful ensemble model, which corrects errors through gradient descent. This model can capture highly complex patterns and interactions in the data, achieving extremely high accuracy. Linear programming optimizes revenue under multiple cost and constraint conditions, maximizing revenue or minimizing costs under constraints of multiple decision variables, total budget, manpower, and time. Genetic algorithms optimize constrained and nonlinear problems, approximating the optimal solution through evolution and natural selection. The budget comparison module uses hash functions to find the corresponding data location by key and selects production cost data for the same product order. It calculates the actual value based on the order quantity ratio, calculates the difference between the actual and estimated values, and uses a hash function to map the key to a fixed-length hash value. This value directly corresponds to the address of a storage bucket containing the data location, allowing for retrieval of the corresponding data in the database.

[0025] The formula for linear regression is shown below: Cost = + * + * +...+ *

[0026] Introducing vectors, weight vectors: W=

[0027] Feature vector: X=

[0028] At this point, the model formula can be written as the dot product of two vectors: Cost = *1+ * + * +...+ *

[0029] The real-time data acquisition module includes surveillance cameras, photoelectric sensors, temperature sensors, and smart meters. Surveillance cameras are installed in the production workshop to monitor the production process, manpower, and equipment. By placing multiple cameras in the workshop, production operations are monitored. Photoelectric sensors are placed on the production line and equipment to measure the quantity of products and defective products. Temperature sensors are used to detect the operating temperature of the equipment. Smart meters are used to monitor the energy consumption of the production equipment. The real-time data acquisition module processes data on working hours, output, equipment status, work order progress, and defective product quantity based on the Manufacturing Execution System (MES). The MES is a workshop-level management information system located between the upper-level Enterprise Resource Planning (ERP) system and the lower-level Industrial Control System (ICS), acting as an information hub and execution engine. It utilizes data acquisition and monitoring control systems and IoT sensors to process real-time energy consumption, equipment speed, temperature, pressure, and process parameters, achieving real-time acquisition of production data, monitoring of the production process, facilitating process improvement, and dynamically adjusting budget allocation through real-time data to avoid resource waste and improve enterprise cost control and operational efficiency.

[0030] The dynamic model building module includes a defect analysis module, a dynamic model, a model update module, and a charting module. The defect analysis module analyzes the causes of defects based on visual AI detection and data entry. It integrates IoT, machine vision, data analysis, and AI systems, and consists of a core analysis layer, a visualization layer, and an action feedback layer. The core analysis layer locates the root causes of defects through multidimensional analysis, correlation analysis, and anomaly detection. Multidimensional analysis uses drill-down, slicing, and block operations to cross-analyze the distribution of defect data from multiple dimensions. Correlation analysis uses hypothesis testing to explore the statistical correlation between defective phenomena and potential causes. Anomaly detection analyzes data from the real-time data acquisition module and provides early warnings for defective data. The visualization layer presents the analysis results in charts. The action feedback layer links the analyzed root causes to corrective measures, improves the root causes of defects, tracks the implementation status of measures, automatically adjusts process parameters, monitors the quality of the entire process in real time, reduces the non-conforming rate and customer complaint risk, and enhances brand reputation and market competitiveness. The dynamic model uses discrete event simulation software to build a dynamic production model from production data and budget assessment. The model update module is based on Bayesian regression and... Linear learning updates the existing model by generating initial budget forecasts from a baseline model. Production data is collected periodically via API and manual input, and the features of new production data are input to dynamically adjust the baseline model. Bayesian regression calculates the posterior distribution of parameters using real-time collected production data and Bayes' theorem. It then uses historical data to create a prior distribution for the original model, forming a new prior. The acquired real-time data is input into the model with the prior distribution, combining the real-time data with the model to obtain a new data likelihood. Substituting the new prior and the new data likelihood into Bayes' theorem, a new model is calculated. The online learning model utilizes algorithms that support incremental learning, specifically the SGDRegressor algorithm. It combines real-time data collection to make small updates to the model. SGDRegressor is designed for models that use stochastic gradient descent for regression tasks. The graph module establishes curves of predicted values ​​versus time based on updated cost predictions, completion time predictions, and deviation analysis, facilitating the observation and judgment of production status. Deviation analysis calculates the differences between planned values, earned values, and actual costs and generates early warnings, enabling real-time monitoring of production status and timely adjustments.

[0031] The integrated control module includes a spatiotemporal unification module, a data association module, a situation assessment module, and a process optimization module. The spatiotemporal unification module unifies the collected multi-source data into a single time and space coordinate system through coordinate transformation and clock alignment, calibrating the temporal and spatial differences caused by independent sensor acquisition. Coordinate transformation maps points in the source coordinate system to the target coordinate system using a precise coordinate transformation model. Clock alignment ensures all data streams have a unified, high-precision timestamp and handles transmission delays and sampling clock deviations. The data association module associates different source data with entity objects through data fusion. It filters data and trajectories to narrow down the range of associated data. First, it performs a coarse screening to exclude obviously impossible association pairs. Based on the predicted position and uncertainty of the target trajectory, an association gate is established. Using threshold filtering, for data and trajectory pairs that pass the threshold, the numerical cost of the filtered data and trajectory is calculated, representing the probability that they belong to the same target. Based on the principle of minimizing the total association cost, further optimization is performed. The system associates and allocates data and trajectories. The situation assessment module constructs an overall situational understanding and evaluates the relationships between entity objects based on the associated data. The situation assessment module uses logical reasoning and machine learning methods, combined with knowledge graphs, to associate perception and cognition, establishing connections between associated data and entity objects. Logical reasoning is based on predicate logic and descriptive logic for deductive reasoning. The knowledge graph uses entities as nodes and relationships as edges to form a huge semantic network. The process optimization module optimizes data source selection, resource allocation, and fusion strategies by monitoring the fusion process itself. The process optimization module continuously measures and evaluates the performance indicators and resource status of the fusion system, compares the current performance with the expected performance, identifies performance bottlenecks, and predicts the potential impact of different resource allocation strategies on system performance and task efficiency. Based on the evaluation and prediction, performance bottlenecks are optimized. Through the fusion and association of multi-source data by the real-time data acquisition module, the production process is dynamically monitored and intelligently analyzed, providing accurate budget execution feedback to enterprise management and supporting rapid decision-making.

[0032] The visualization interaction module, based on a chart type library, mapping rule engine, visualization base library, interaction layer, and interface layer, performs data transformation, visual design, and human-computer interaction. It displays production data and budget assessments on the screen. The mapping rule engine maps data fields to visual channels. By receiving input data, it performs data transformation, calculation, or routing decisions according to a set of predefined and configurable business rules and generates output data. The interaction layer facilitates dialogue between users and visualization charts through basic interaction, data interaction, and query-based interaction. As the physical and logical interface between users and the system, it is responsible for capturing user intent, capturing raw input events from devices such as mouse, keyboard, touchscreen, gestures, and voice, parsing the raw events into meaningful interactive actions, and displaying data and charts through the interface layer.

[0033] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A system for quality acquisition and dynamic budget control based on multi-source data integration, characterized in that, include: Budget dynamic adjustment system; The budget assessment module performs a preliminary assessment of the budget by comparing the predicted production costs with historical data. A real-time data acquisition module, which uses a sensor network to acquire real-time data during the production process and provide a data foundation; A dynamic model building module, which builds a dynamic model of production data and budget assessment and updates the model based on collected real-time data; A visualization and interaction module is used to display budget assessments and dynamic models; The system comprises an integrated control module, which integrates multi-source data and adjusts the production process and budget in real time based on the integrated data.

2. The system for quality acquisition and dynamic budget control based on multi-source data integration according to claim 1, characterized in that, The budget assessment module includes a database, a budget model, a model optimization module, and a budget comparison module. The database stores historical production data and real-time production data based on PostgreSQL. The budget model constructs a linear model between production costs and production data using linear regression and gradient boosting algorithms, and assesses the production budget. The model optimization module optimizes the budget model using linear programming and genetic algorithms. The budget comparison module calculates actual values ​​from historical data in the database and compares them with the budget assessment.

3. The system for quality acquisition and dynamic budget control based on multi-source data integration according to claim 1, characterized in that, The real-time data acquisition module includes a monitoring camera, a photoelectric sensor, a temperature sensor, and a smart meter. The monitoring camera is installed in the production workshop to monitor the production process, manpower, and equipment. The photoelectric sensor is set on the production line and equipment to measure the quantity of products and the quantity of defective products. The temperature sensor is used to detect the operating temperature of the equipment. The smart meter is used to monitor the power consumption of the production equipment.

4. The system for quality acquisition and dynamic budget control based on multi-source data integration according to claim 1, characterized in that, The dynamic model building module includes a defect analysis module, a dynamic model, a model update module, and a charting module. The defect analysis module analyzes the causes of defective products based on visual AI detection and data entry. The dynamic model uses discrete event simulation software to construct a dynamic production model based on production data and budget assessment. The model update module updates the original model based on Bayesian regression and online learning. The Bayesian regression uses historical data to perform prior distribution on the original model to form a new prior. The acquired real-time data is input into the model after prior distribution to combine the real-time data with the model and obtain a new data likelihood. The new prior and the new data likelihood are substituted into Bayes' theorem to calculate a new model. The charting module establishes curves of predicted values ​​versus time changes based on updated cost predictions, completion time predictions, and deviation analysis.

5. The system for quality acquisition and dynamic budget control based on multi-source data integration according to claim 1, characterized in that, The integrated control module includes a spatiotemporal unification module, a data association module, a situation assessment module, and a process optimization module. The spatiotemporal unification module unifies the collected multi-source data into a single time and space coordinate system through coordinate transformation and clock alignment, calibrating the temporal and spatial differences generated by independent sensor acquisition. The data association module associates different source data with entity objects through data fusion. It filters data and trajectories, calculates the numerical cost of the filtered data and trajectories to represent the probability that they belong to the same target, and allocates associations between data and trajectories based on the principle of minimizing the total association cost. The situation assessment module… The module constructs an overall situational understanding and assesses the relationships between entity objects based on the associated data. The situational assessment module is based on logical reasoning and machine learning methods, and combines knowledge graphs to associate perception and cognition, and establishes connections between associated data and entity objects. The process optimization module optimizes data source selection, resource allocation and fusion strategy by monitoring the fusion process itself. The process optimization module continuously measures and evaluates the performance indicators and resource status of the fusion system, compares the current performance with the expected performance, identifies performance bottlenecks, and predicts the potential impact of different resource allocation strategies on system performance and task efficiency. Based on the evaluation and prediction, the module optimizes the performance bottlenecks.

6. The system for quality acquisition and dynamic budget control based on multi-source data integration according to claim 2, characterized in that, The gradient boosting algorithm ranks features by nonlinear relationships; the linear programming performs revenue optimization under multiple cost and constraint conditions; the genetic algorithm is used to optimize constrained and nonlinear problems; and the budget comparison module maps keys to fixed-length hash values ​​using a hash function, where the hash value corresponds to the address of a storage bucket, the location where the data is stored, obtains the order production cost data in the storage address, and calculates the actual value based on the order quantity ratio.

7. The system for quality acquisition and dynamic budget control based on multi-source data integration according to claim 3, characterized in that, The real-time data acquisition module processes data on working hours, output, equipment status, work order progress, and defective product quantity through monitoring cameras, photoelectric sensors, temperature sensors, and smart meters. It also utilizes data acquisition and monitoring control, along with IoT sensors, to process real-time energy consumption, equipment speed, temperature, and pressure process parameters.

8. The system for quality acquisition and dynamic budget control based on multi-source data integration according to claim 4, characterized in that, The defect analysis module integrates IoT, machine vision, data analysis, and AI systems. It consists of a core analysis layer, a visualization layer, and an action feedback layer. The core analysis layer locates the root causes of defects through multidimensional analysis, correlation analysis, and anomaly detection. The multidimensional analysis uses drill-down, slicing, and block-slicing operations to cross-analyze the distribution of defective data from multiple dimensions. The correlation analysis employs hypothesis testing to explore the statistical correlation between defective phenomena and potential causes. The anomaly detection analyzes data from the real-time data acquisition module to issue early warnings for defective data. The visualization layer presents the analysis results in charts and graphs. The action feedback layer links the analyzed root causes to corrective measures, improves the root causes of defects, and tracks the implementation status of these measures.

9. A system for quality acquisition and dynamic budget control based on multi-source data integration according to claim 4, characterized in that, The Bayesian regression calculates the posterior distribution of parameters based on real-time collected production data and Bayes' theorem. The online learning utilizes algorithms that support incremental learning and updates the model based on real-time collected data. The deviation analysis calculates the difference between planned value, earned value, and actual cost and generates early warnings.

10. A system for quality acquisition and dynamic budget control based on multi-source data integration according to claim 2, characterized in that, The visualization interaction module performs data transformation, visual design, and human-computer interaction based on a chart type library, mapping rule engine, visualization base library, interaction layer, and interface layer. It displays production data and budget assessment on the screen. The mapping rule engine is used to map data fields to visual channels. The interaction layer enables users to interact with the visualization charts through basic interaction, data interaction, and query-based interaction.

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