A circuit board production management system and method

Abstract

This invention discloses a circuit board production management system and method, relating to the field of circuit board technology, comprising the following steps: Step 1: The system receives new orders, analyzes the orders using a deep learning model, predicts the production cycle and resource requirements of the orders, and automatically allocates production resources according to the priority and resource requirements of the orders; Step 2: The system predicts material consumption using a recurrent neural network and automatically adjusts the material replenishment plan based on the trend of material loss during production; Step 3: The system optimizes production scheduling using a multi-objective genetic algorithm and a reinforcement learning model; Step 4: The system monitors product quality indicators in real time using sensors and image recognition technology; Step 5: Based on data from the feedback control module, the system automatically adjusts resource configuration according to real-time circuit board production data, and adjusts in real time according to changes in demand and equipment failures; Step 6: The system sets a self-learning cycle, and updates the deep learning model and optimization algorithm with data.

Description

Technical Field

[0001] This invention relates to the field of printed circuit board technology, and specifically to a printed circuit board production management system and method. Background Technology

[0002] With the continuous development of electronic products, circuit boards, as core electronic components, are facing increasingly complex production processes in modern circuit board manufacturing due to customized demands and fierce market competition. However, with the advancement of information technology, traditional circuit board production management systems have the following drawbacks:

[0003] 1. Traditional production management systems typically rely on manual scheduling and fixed schedules, lacking a flexible resource allocation mechanism. This results in low utilization of equipment and materials, potentially leading to idle equipment, material waste, and an inability to efficiently match production demands.

[0004] 2. When faced with rapid changes in orders or unexpected situations, traditional systems often struggle to make timely adjustments. For example, when orders change or equipment malfunctions, traditional methods typically require a considerable amount of time to readjust production plans, leading to reduced production efficiency.

[0005] 3. Traditional quality control mainly relies on manual inspection and static inspection models, which fails to achieve real-time and accurate quality monitoring. Once a quality problem occurs during the production process, it is usually only discovered after the product is completed, making it impossible to adjust the production process in time to prevent the spread of defects.

[0006] 4. Traditional production management systems lack adaptability when facing changes in the production environment (such as equipment failure, raw material supply problems, etc.), and fail to achieve dynamic optimization and adjustment based on data analysis during the production process, resulting in the system being unable to respond effectively to the actual situation.

[0007] 5. Because traditional scheduling methods fail to comprehensively consider various complex constraints in the production process (such as equipment status, personnel arrangement, etc.), scheduling efficiency is low, which can easily lead to production bottlenecks, production line shutdowns, and other problems.

[0008] 6. Traditional production management systems often struggle to track and optimize the entire production process, especially during large-scale production. Managers often find it difficult to accurately grasp the status of each stage, making it hard to maximize the efficiency and quality of production management.

[0009] Therefore, in order to address the above issues, there is an urgent need for a circuit board production management system to improve data processing efficiency, protect data privacy, and achieve more flexible system integration. Summary of the Invention

[0010] The purpose of this invention is to provide a circuit board production management system and method to solve the problems mentioned in the background art.

[0011] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0012] A circuit board production management system includes a data acquisition and analysis module, an intelligent scheduling and optimization module, a multi-layer quality monitoring module, a multi-feedback control module, a resource dynamic allocation and adaptive adjustment module, and a self-learning and continuous optimization module.

[0013] The data acquisition and analysis module includes a deep learning model, which collects and analyzes multi-dimensional data such as the consumption of circuit board production materials, equipment status, and order progress in real time based on the deep learning model.

[0014] The intelligent scheduling and optimization module: Based on the data acquisition and analysis module, it collects relevant circuit board data and comprehensively considers equipment utilization, order priority, and material usage, and automatically generates the optimal schedule through a multi-objective optimization algorithm;

[0015] The multi-layer quality monitoring module includes sensors and data acquisition devices. Through the sensors and data acquisition devices, a multi-layer circuit board production quality monitoring system is formed based on the intelligent scheduling and optimization module to realize real-time monitoring of circuit board dimensions, electrical performance and surface quality, etc.

[0016] The multi-feedback control module, combined with the circuit board quality monitoring module, enables adaptive adjustment of the circuit board production process and handling of abnormal situations.

[0017] The resource dynamic allocation and adaptive adjustment module, based on the data from the feedback control module, automatically adjusts the resource configuration according to the real-time data of circuit board production.

[0018] The dynamic resource scheduling cycle and real-time optimization module adjusts the allocation of materials, equipment, and human resources in real time based on the real-time data from the dynamic resource allocation and adaptive adjustment module to achieve efficient production.

[0019] A further improvement to the technical solution of this invention lies in the following: the data acquisition and analysis module includes an order analysis and task allocation unit and a material loss intelligent prediction and adaptive replenishment unit. The order analysis and task allocation unit, after receiving an order, first analyzes the order demand using a deep learning model to predict the order's production cycle, resource requirements, and potential bottlenecks. The specific analysis using the deep learning model is as follows:

[0020]

[0021] Where R i P is represented as the resource demand index for order i. i This is represented as order priority; f(Q) iT is a non-linear function of order quantity, used to characterize the resource impact of large-volume orders; i E represents the estimated production time. i Expressed as equipment efficiency; δ i This represents a potential bottleneck in deep learning model predictions.

[0022] A further improvement to the technical solution of this invention lies in the following: the order analysis and task allocation unit: the system intelligently predicts material loss, combines historical loss data and environmental factors, and uses a recurrent neural network model to predict the real-time consumption of materials. The loss prediction model is as follows:

[0023] L t =RNN(M t-1 ,X t ,θ)

[0024] Where L t M represents the predicted material loss at the current time t; t-1 X represents the remaining amount of material at the previous moment; t θ represents the current environmental conditions (temperature, humidity, material properties, etc.); θ represents the RNN model parameters, and M is set accordingly. min The system sets a safe threshold for remaining material. If the remaining material falls below this threshold, the system will activate an adaptive replenishment mechanism to automatically adjust the amount and frequency of material replenishment and issue a replenishment reminder.

[0025] A further improvement to the technical solution of this invention lies in the following: The intelligent scheduling and optimization module performs intelligent scheduling based on a combination of multi-objective genetic algorithm and reinforcement learning. Specifically, the objective is to simultaneously optimize order completion time, equipment utilization, and material consumption during the production process. The objective function is as follows:

[0026]

[0027]

[0028] Where C i S i Let W represent the completion and start times of order i, respectively. i Represented as order weight; U j T j R represents the usage time of device j and the total device time, respectively; k Represented as resource utilization rate; G(Q) k () represents the material savings.

[0029] A further improvement to the technical solution of this invention lies in that: the multi-layer quality monitoring module includes embedded sensors and image recognition technology, which monitor product dimensions, electrical characteristics, and surface quality in real time. For each quality indicator, the system sets an adaptive dynamic threshold Q. th

[0030] Q th =μ+σ·G(Q) v )

[0031] Q th Represented as adaptive quality threshold; μ is the mean, σ is the standard deviation; G(Q) v ) represents the relationship function between the production environment and quality deviation.

[0032] A further improvement of the technical solution of the present invention is that the multi-feedback control module adopts a feedback control mechanism based on deep Q-networks, adjusts production parameters according to real-time monitoring data, and continuously updates the production model to achieve quality stability and adaptive control. Through this feedback mechanism, the system can dynamically adjust to complex production environments and quickly adapt when production conditions change.

[0033] A further improvement of the technical solution of the present invention is that: the resource dynamic allocation and adaptive adjustment module: based on the data of the feedback control module, the system automatically adjusts the resource configuration according to the real-time data of circuit board production. During the production process, the system makes real-time adjustments according to changes in demand and equipment failures.

[0034] A further improvement of the technical solution of the present invention is that: the self-learning and continuous optimization module includes a learning loop unit and an optimization unit, wherein the learning loop unit is configured with a self-learning loop cycle, and the system automatically executes the following steps in each cycle:

[0035] S1: Data Update and Model Iteration: Update the deep learning model and reinforcement learning strategy based on the latest data;

[0036] S2: Real-time scheduling re-optimization: Re-optimize the scheduling based on the current equipment status, material inventory, and order update status;

[0037] S3: Dynamic resource allocation: The system monitors resource status in real time and automatically adjusts resource allocation to cope with sudden orders or equipment failures;

[0038] S4: Feedback control and adaptive parameter adjustment: The system adjusts the PID control parameters in real time according to production conditions to make the production process dynamically stable;

[0039] S5: Data Storage and Trend Analysis: The system stores data in the database and analyzes production trends based on historical data, allowing management to monitor them at any time.

[0040] A further improvement of the technical solution of the present invention is that the optimization unit of the self-learning and continuous optimization module: the system gradually optimizes the production process through a multi-dimensional feedback mechanism and realizes adaptive control of the production environment.

[0041] A further improvement of the technical solution of the present invention lies in: a circuit board production management method, used to implement the circuit board production management system according to any one of claims 1-9, comprising the following steps:

[0042] Step 1: The system receives new orders and analyzes the order's demand, quantity, priority, etc., through a deep learning model to predict the order's production cycle and resource requirements. Based on the order's priority and resource requirements, the system automatically allocates production resources.

[0043] Step 2: Simultaneously, the system predicts material consumption using a recurrent neural network (RNN) and automatically adjusts the material replenishment plan based on the trend of material loss during production. When the remaining material amount is lower than the set safety threshold, the system automatically issues a replenishment reminder to ensure uninterrupted production.

[0044] Step 3: The system uses a multi-objective genetic algorithm and reinforcement learning model to optimize production scheduling, taking into account factors such as equipment utilization, order priority, and production time, to generate the optimal schedule. The schedule is dynamically adjusted to ensure that each link is completed efficiently in the shortest possible time.

[0045] Step 4: The system monitors product quality indicators (such as dimensions, electrical characteristics, surface quality, etc.) in real time through sensors and image recognition technology. If the product quality is found to be non-compliant with standards, the system adjusts production parameters through deep Q-network feedback to ensure that the product quality meets the requirements.

[0046] Step 5: The system automatically adjusts resource allocation based on real-time data (such as equipment status, material usage, worker progress, etc.). During the production process, the system makes real-time adjustments based on changes in demand and equipment failures to optimize resource use and avoid production bottlenecks.

[0047] Step Six: The system sets a self-learning cycle, updates the deep learning model and optimization algorithm through real-time data, and continuously improves the accuracy and efficiency of production scheduling, resource allocation and quality monitoring. The system gradually optimizes the production process through a multi-dimensional feedback mechanism and achieves adaptive control of the production environment.

[0048] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows:

[0049] 1. This invention provides an innovative circuit board production management system and method. This invention reduces production waiting time, improves equipment utilization, and saves production costs through intelligent optimization algorithms;

[0050] 2. The circuit board production management system and method provided by the present invention, through the use of a multi-dimensional quality monitoring model, ensures that the products meet high standards and makes real-time adjustments when they are unqualified;

[0051] 3. The circuit board production management system and method provided by the present invention are based on deep learning prediction and real-time monitoring, which automatically allocates materials and human resources to reduce production interruptions;

[0052] 4. The circuit board production management system and method provided by the present invention achieve adaptive adjustment of complex conditions in the production process by combining deep Q network with PID control.

[0053] 5. The circuit board production management system and method provided by the present invention record and analyze all production data in real time, enabling management to fully control the production status and improve management decision-making efficiency. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0055] Figure 1 This is a flowchart of a circuit board production management system and method according to the present invention; Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0057] Examples, such as Figure 1 A circuit board production management system includes a data acquisition and analysis module, an intelligent scheduling and optimization module, a multi-layer quality monitoring module, a multi-feedback control module, a resource dynamic allocation and adaptive adjustment module, and a self-learning and continuous optimization module.

[0058] Data acquisition and analysis module: includes a deep learning model, which collects and analyzes multi-dimensional data such as the consumption of circuit board production materials, equipment status, and order progress in real time based on the deep learning model;

[0059] Intelligent scheduling and optimization module: Based on the data acquisition and analysis module, relevant circuit board data is collected and the optimal schedule is automatically generated through a multi-objective optimization algorithm, taking into account equipment utilization, order priority, and material usage.

[0060] Multi-layer quality monitoring module: including sensors and data acquisition equipment, which form a multi-layer circuit board production quality monitoring system based on intelligent scheduling and optimization module through sensors and data acquisition equipment, to realize real-time monitoring of circuit board size, electrical performance and surface quality, etc.

[0061] Multi-feedback control module: Combined with the circuit board quality monitoring module, it enables adaptive adjustment of the circuit board production process and handling of abnormal situations;

[0062] The resource dynamic allocation and adaptive adjustment module, based on data from the feedback control module, allows the system to automatically adjust resource configuration according to real-time circuit board production data.

[0063] Dynamic resource scheduling loop and real-time optimization module: Based on real-time data from the resource dynamic allocation and adaptive adjustment module, adjust the allocation of materials, equipment and human resources in real time to achieve efficient production.

[0064] Specifically, the data acquisition and analysis module includes an order analysis and task allocation unit and a material loss intelligent prediction and adaptive replenishment unit. The order analysis and task allocation unit: After receiving an order, the system first analyzes the order demand using a deep learning model to predict the order's production cycle, resource requirements, and potential bottlenecks. The deep learning model's specific analysis is as follows:

[0065]

[0066] Where R i P is represented as the resource demand index for order i. i This is represented as order priority; f(Q) i T is a non-linear function of order quantity, used to characterize the resource impact of large-volume orders; i E represents the estimated production time. i Expressed as equipment efficiency; δ i This represents a potential bottleneck in deep learning model predictions.

[0067] Based on the resource demand predicted by deep learning, the system calculates the resource demand index R of the order. i The system prioritizes orders and allocates resources accordingly, using a fuzzy logic algorithm to dynamically adjust priorities.

[0068] Specifically, in the order analysis and task allocation unit: the system intelligently predicts material loss, combining historical loss data and environmental factors, and uses a recurrent neural network model to predict the real-time consumption of materials. The loss prediction model is as follows:

[0069] L t =RNN(M t-1 ,X t ,θ)

[0070] Where L t M represents the predicted material loss at the current time t; t-1 X represents the remaining amount of material at the previous moment; t θ represents the current environmental conditions (temperature, humidity, material properties, etc.); θ represents the RNN model parameters, and M is set accordingly. min The system sets a safe threshold for remaining material. If the remaining material falls below this threshold, the system will activate an adaptive replenishment mechanism to automatically adjust the amount and frequency of material replenishment and issue a replenishment reminder.

[0071] The system employs a dual strategy: quantitative replenishment and dynamic inventory optimization to ensure uninterrupted production.

[0072] Specifically, the intelligent scheduling and optimization module uses a combination of multi-objective genetic algorithms and reinforcement learning to perform intelligent scheduling. The specific objectives are to simultaneously optimize order completion time, equipment utilization, and material consumption during production. The objective function is as follows:

[0073]

[0074]

[0075] Where C i S i Let W represent the completion and start times of order i, respectively. i Represented as order weight; U j T j R represents the usage time of device j and the total device time, respectively; k Represented as resource utilization rate; G(Q) k () represents the material savings.

[0076] Through reinforcement learning models, the system dynamically updates scheduling parameters under real-time data-driven conditions, ensuring that the system always approaches the optimal solution in dynamic environments.

[0077] Specifically, the multi-layer quality monitoring module includes embedded sensors and image recognition technology. Through these technologies, it monitors product dimensions, electrical characteristics, and surface quality in real time. For each quality indicator, the system sets an adaptive dynamic threshold Q. th

[0078] Q th =μ+σ·G(Q) v )

[0079] Q th Represented as adaptive quality threshold; μ is the mean, σ is the standard deviation; G(Q) v ) represents the relationship function between the production environment and quality deviation.

[0080] Specifically, the multi-feedback control module: The system adopts a feedback control mechanism based on deep Q-networks, which adjusts production parameters according to real-time monitoring data and continuously updates the production model to achieve quality stability and adaptive control. Through this feedback mechanism, the system can dynamically adjust to complex production environments and quickly adapt to changes in production conditions.

[0081] The system adopts a feedback control mechanism based on deep Q-network (DQN) to adjust production parameters according to real-time detection data and continuously update the production model to achieve quality stability and adaptive control. Through this feedback mechanism, the system can dynamically adjust to complex production environments and quickly adapt to changes in production conditions.

[0082] Specifically, the resource dynamic allocation and adaptive adjustment module: Based on the data from the feedback control module, the system automatically adjusts the resource configuration according to the real-time data of circuit board production. During the production process, the system makes real-time adjustments based on changes in demand and equipment failures.

[0083] Specifically, the self-learning and continuous optimization module includes a learning loop unit and an optimization unit. The learning loop unit is set to a self-learning loop cycle, and the system automatically executes the following steps in each cycle:

[0084] S1: Data Update and Model Iteration: Update the deep learning model and reinforcement learning strategy based on the latest data;

[0085] S2: Real-time scheduling re-optimization: Re-optimize the scheduling based on the current equipment status, material inventory, and order update status;

[0086] S3: Dynamic resource allocation: The system monitors resource status in real time and automatically adjusts resource allocation to cope with sudden orders or equipment failures;

[0087] S4: Feedback control and adaptive parameter adjustment: The system adjusts the PID control parameters in real time according to production conditions to make the production process dynamically stable;

[0088] S5: Data Storage and Trend Analysis: The system stores data in the database and analyzes production trends based on historical data, allowing management to monitor them at any time.

[0089] Specifically, the optimization unit of the self-learning and continuous optimization module: the system gradually optimizes the production process through a multi-dimensional feedback mechanism and achieves adaptive control of the production environment.

[0090] The system sets a self-learning cycle and continuously improves the accuracy and efficiency of production scheduling, resource allocation and quality monitoring by updating the deep learning model and optimization algorithm through real-time data. Through this series of steps, the system can efficiently manage the production process, optimize resource allocation, improve production efficiency, reduce losses, and ensure the quality stability of products.

[0091] Specifically, a circuit board production management method, used to implement the circuit board production management system of any one of claims 1-9, includes the following steps:

[0092] Step 1: The system receives new orders and analyzes the order's demand, quantity, priority, etc., through a deep learning model to predict the order's production cycle and resource requirements. Based on the order's priority and resource requirements, the system automatically allocates production resources.

[0093] Step 2: Simultaneously, the system predicts material consumption using a recurrent neural network (RNN) and automatically adjusts the material replenishment plan based on the trend of material loss during production. When the remaining material amount is lower than the set safety threshold, the system automatically issues a replenishment reminder to ensure uninterrupted production.

[0094] Step 3: The system uses a multi-objective genetic algorithm and reinforcement learning model to optimize production scheduling, taking into account factors such as equipment utilization, order priority, and production time, to generate the optimal schedule. The schedule is dynamically adjusted to ensure that each link is completed efficiently in the shortest possible time.

[0095] Step 4: The system monitors product quality indicators (such as dimensions, electrical characteristics, surface quality, etc.) in real time through sensors and image recognition technology. If the product quality is found to be non-compliant with standards, the system adjusts production parameters through deep Q-network feedback to ensure that the product quality meets the requirements.

[0096] Step 5: The system automatically adjusts resource allocation based on real-time data (such as equipment status, material usage, worker progress, etc.). During the production process, the system makes real-time adjustments based on changes in demand and equipment failures to optimize resource use and avoid production bottlenecks.

[0097] Step Six: The system sets a self-learning cycle, updates the deep learning model and optimization algorithm through real-time data, and continuously improves the accuracy and efficiency of production scheduling, resource allocation and quality monitoring. The system gradually optimizes the production process through a multi-dimensional feedback mechanism and achieves adaptive control of the production environment.

[0098] Working principle: The system receives new orders and processes them using a deep learning model:

[0099]

[0100] Where R i P is represented as the resource demand index for order i. i This is represented as order priority; f(Q) i T is a non-linear function of order quantity, used to characterize the resource impact of large-volume orders; i E represents the estimated production time. i Expressed as equipment efficiency; δ i This represents a potential bottleneck in the deep learning model's predictions. The system analyzes order demand, quantity, and priority to predict production cycles and resource requirements. Based on order priority and resource needs, the system automatically allocates production resources. Simultaneously, the system uses a recurrent neural network (RNN) to predict material consumption; that is, the RNN model predicts real-time material consumption. The loss prediction model is as follows:

[0101] L t =RNN(M t-1 ,X t ,θ)

[0102] Where L t M represents the predicted material loss at the current time t; t-1 X represents the remaining amount of material at the previous moment; t θ represents the current environmental conditions (temperature, humidity, material properties, etc.); θ represents the RNN model parameters, and M is set accordingly. min The system sets a safe threshold for remaining material. If the remaining material falls below this threshold, an adaptive replenishment mechanism will be activated to automatically adjust the replenishment amount and frequency, and issue a replenishment reminder to ensure uninterrupted production. The system employs a multi-objective genetic algorithm and reinforcement learning model for production scheduling optimization, considering factors such as equipment utilization, order priority, and production time to generate an optimal schedule. The schedule is dynamically adjusted to ensure that each stage is completed efficiently in the shortest possible time. The objective function is as follows:

[0103]

[0104]

[0105] Where C i Si Let W represent the completion and start times of order i, respectively. i Represented as order weight;

[0106] U j T j R represents the usage time of device j and the total device time, respectively; k Represented as resource utilization rate; G(Q) k The benefit is represented by material savings. The system monitors product quality indicators (such as dimensions, electrical characteristics, surface quality, etc.) in real time using sensors and image recognition technology. If the product quality does not meet the standards, the system adjusts production parameters through deep Q-network feedback to ensure that the product quality meets the requirements. That is, the system will set an adaptive dynamic threshold Q. th

[0107] Q th =μ+σ·G(Q) v )

[0108] Q th Represented as adaptive quality threshold; μ is the mean, σ is the standard deviation; G(Q) v The system, denoted as the function relating the production environment and quality deviation, employs a feedback control mechanism based on a deep Q-network. It adjusts production parameters based on real-time monitoring data and continuously updates the production model to achieve quality stability and adaptive control. Through this feedback mechanism, the system can dynamically adjust to complex production environments and quickly adapt to changes in production conditions. The system automatically adjusts resource allocation based on real-time data (such as equipment status, material usage, and worker progress). During production, the system makes real-time adjustments based on changes in demand and equipment failures to optimize resource utilization and avoid production bottlenecks. The system sets a self-learning cycle, updating the deep learning model and optimization algorithm through real-time data to continuously improve the accuracy and efficiency of production scheduling, resource allocation, and quality monitoring. Through a multi-dimensional feedback mechanism, the system gradually optimizes the production process and achieves adaptive control of the production environment.

[0109] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A circuit board production management system, characterized in that: It includes a data acquisition and analysis module, an intelligent scheduling and optimization module, a multi-layer quality monitoring module, a multi-feedback control module, a dynamic resource allocation and adaptive adjustment module, and a self-learning and continuous optimization module; The data acquisition and analysis module includes a deep learning model. Based on this model, it collects and analyzes multi-dimensional data in real time, including circuit board production material consumption, equipment status, and order progress. The module further includes an order analysis and task allocation unit and a material loss intelligent prediction and adaptive replenishment unit. The order analysis and task allocation unit, upon receiving an order, first analyzes the order requirements using a deep learning model to predict the order's production cycle, resource needs, and potential bottlenecks. The specific analysis using the deep learning model is as follows: in This is represented as the resource demand index for order i; This is represented as order priority; It is a non-linear function of order quantity, used to characterize the resource impact of large-volume orders; This is represented as the estimated production time; Expressed as equipment efficiency; This represents a potential bottleneck in deep learning model predictions. The order analysis and task allocation unit: The system intelligently predicts material loss, combining historical loss data and environmental factors, and uses a recurrent neural network model to predict the real-time consumption of materials. The loss prediction model is as follows: in The predicted material loss at the current time t; This represents the remaining amount of material at the previous moment. This is expressed as the current environmental conditions, including temperature, humidity, and material properties. Represented as RNN model parameters, and set simultaneously. The system sets a safe threshold for remaining material. If the remaining material falls below this threshold, the system will activate an adaptive replenishment mechanism to automatically adjust the amount and frequency of replenishment and issue a replenishment reminder. The intelligent scheduling and optimization module: Based on the data acquisition and analysis module, it collects relevant circuit board data and comprehensively considers equipment utilization, order priority, and material usage. It then automatically generates the optimal schedule using a multi-objective optimization algorithm. The intelligent scheduling is based on a combination of multi-objective genetic algorithms and reinforcement learning. Specifically, the objective is to simultaneously optimize order completion time, equipment utilization, and material consumption during production. The objective function is as follows: in Let these represent the completion and start times of order i, respectively. Represented as order weight; These represent the usage time of device j and the total device time, respectively. This is expressed as resource utilization rate; This is expressed as material savings. The multi-layer quality monitoring module includes sensors and data acquisition devices. Through the sensors and data acquisition devices, a multi-layer circuit board production quality monitoring system is formed based on the intelligent scheduling and optimization module, realizing real-time monitoring of the circuit board's dimensions, electrical performance, and surface quality. The multi-feedback control module, combined with the circuit board quality monitoring module, enables adaptive adjustment of the circuit board production process and handling of abnormal situations. The multi-layer quality monitoring module includes embedded sensors and image recognition technology. It monitors product dimensions, electrical characteristics, and surface quality in real time using these technologies. For each quality indicator, the system sets an adaptive dynamic threshold. in Represented as an adaptive quality threshold; This is the average value. Standard deviation; This can be expressed as a function relating the production environment and quality deviation. The resource dynamic allocation and adaptive adjustment module, based on the data from the feedback control module, automatically adjusts the resource configuration according to the real-time data of circuit board production. The dynamic resource scheduling cycle and real-time optimization module adjusts the allocation of materials, equipment, and human resources in real time based on the real-time data from the dynamic resource allocation and adaptive adjustment module to achieve efficient production.

2. The circuit board production management system according to claim 1, characterized in that: The multi-feedback control module: The system adopts a feedback control mechanism based on deep Q-networks, which adjusts production parameters according to real-time detection data and continuously updates the production model to achieve quality stability and adaptive control. Through this feedback mechanism, the system can dynamically adjust to complex production environments and quickly adapt to changes in production conditions.

3. The circuit board production management system according to claim 1, characterized in that: The resource dynamic allocation and adaptive adjustment module: Based on the data from the feedback control module, the system automatically adjusts the resource configuration according to the real-time data of circuit board production. During the production process, the system makes real-time adjustments based on changes in demand and equipment failures.

4. The circuit board production management system according to claim 1, characterized in that: The self-learning and continuous optimization module includes a learning loop unit and an optimization unit. The learning loop unit is set to a self-learning loop cycle, and the system automatically executes the following steps in each cycle: S1: Data Update and Model Iteration: Update the deep learning model and reinforcement learning strategy based on the latest data; S2: Real-time scheduling re-optimization: Re-optimize the scheduling based on the current equipment status, material inventory, and order update status; S3: Dynamic resource allocation: The system monitors resource status in real time and automatically adjusts resource allocation to cope with sudden orders or equipment failures; S4: Feedback control and adaptive parameter adjustment: The system adjusts the PID control parameters in real time according to production conditions to make the production process dynamically stable; S5: Data Storage and Trend Analysis: The system stores data in the database and analyzes production trends based on historical data, allowing management to monitor them at any time.

5. The circuit board production management system according to claim 1, characterized in that: The optimization unit of the self-learning and continuous optimization module: The system gradually optimizes the production process through a multi-dimensional feedback mechanism and achieves adaptive control of the production environment.

6. A circuit board production management method for implementing the circuit board production management system according to any one of claims 1-5, comprising the following steps: Step 1: The system receives new orders and analyzes the order's demand, quantity, and priority using a deep learning model. It then predicts the order's production cycle and resource requirements and automatically allocates production resources based on the order's priority and resource requirements. Step 2: Simultaneously, the system predicts material consumption using a recurrent neural network (RNN) and automatically adjusts the material replenishment plan based on the trend of material loss during production. When the remaining material amount is lower than the set safety threshold, the system automatically issues a replenishment reminder to ensure uninterrupted production. Step 3: The system uses a multi-objective genetic algorithm and reinforcement learning model to optimize production scheduling, taking into account factors such as equipment utilization, order priority, and production time, to generate the optimal schedule. The schedule is dynamically adjusted to ensure that each link is completed efficiently in the shortest possible time. Step 4: The system monitors product quality indicators in real time through sensors and image recognition technology. If the product quality is found to be non-compliant with standards, the system adjusts production parameters through deep Q-network feedback to ensure that the product quality meets the requirements. Step 5: Based on the feedback control module data, the system automatically adjusts resource configuration according to real-time circuit board production data. During the production process, the system makes real-time adjustments based on changes in demand and equipment failures to optimize resource utilization and avoid production bottlenecks. Step Six: The system sets a self-learning cycle, updates the deep learning model and optimization algorithm through real-time data, and continuously improves the accuracy and efficiency of production scheduling, resource allocation and quality monitoring. The system gradually optimizes the production process through a multi-dimensional feedback mechanism and achieves adaptive control of the production environment.

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