Mobile phone component instant production scheduling method and system

Abstract

The invention provides a mobile phone component instant production scheduling method and system, and the method comprises the steps: obtaining data, and carrying out the preprocessing of the data, the data comprising historical sales data, current sales data, market demand data, component supply data, and production line data; construction of a demand prediction model: constructing the demand prediction model based on the historical sales data and the market demand data, and predicting the component demand in a period of time in the future; obtaining a dynamic priority of each production task according to the component demand quantity and the component supply data in a future period of time; for each production task, based on production line data, a production line optimization configuration algorithm based on deep learning is adopted to adjust production line configuration; data in the production process are obtained in real time, and production line configuration is adjusted in real time. According to the method, multiple factors such as market demands, component supply and production line capability can be comprehensively considered, instant adjustment and optimization of the production plan are realized, the production efficiency is improved, and the production cycle is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of production scheduling in mobile phone manufacturing industry, and in particular to a method and system for real-time production scheduling of mobile phone components. Background Art

[0002] With the rapid development of the smartphone market, consumers' demands for mobile phone components are becoming increasingly diversified, which places higher demands on the production scheduling capabilities of mobile phone manufacturers. Traditional production scheduling methods are often based on static production plans and fixed production line configurations, which are difficult to adapt to rapidly changing market demand and component supply. Therefore, a production scheduling method is needed that can respond to market changes in real time and dynamically adjust production plans. Summary of the invention

[0003] The main purpose of the present invention is to overcome the above-mentioned defects in the prior art and to propose a method for real-time production scheduling of mobile phone components. The method can comprehensively consider multiple factors such as market demand, component supply, production line capacity, etc., realize real-time adjustment and optimization of production plans, improve production efficiency, and reduce production cycles.

[0004] The present invention adopts the following technical solution: A method for real-time production scheduling of mobile phone components comprises the following steps: Acquire and pre-process data, including historical sales data and current sales data, market demand data, component supply data, and production line data; Construction of demand forecasting model: Based on historical sales data and market demand data, a demand forecasting model is constructed to predict the demand for components in the future; According to the component demand and component supply data in the future, the dynamic priority of each production task is obtained; For each production task, based on the production line data, a production line optimization configuration algorithm based on deep learning is used to adjust the production line configuration; The data of the production process is acquired in real time, updated to the production line data, and the production line configuration is adjusted in real time based on the production line optimization configuration algorithm. The data of the production process includes but is not limited to the quality qualification rate.

[0005] Specifically, the market demand data includes but is not limited to: the demand quantity and urgency of each mobile phone component; the component supply data includes but is not limited to: supplier inventory, transportation time, and supply stability; the production line data includes but is not limited to: the production capacity of the production line, current production status, and equipment failure rate.

[0006] Specifically, the demand forecasting model is constructed based on historical sales data and market demand data to predict the demand for components in the future. Specifically: Construct a demand forecasting model, use historical sales data as input of the demand forecasting model, use market demand data as output of the demand forecasting model, train the demand forecasting model, and obtain a trained demand forecasting model; Input the current sales data into the trained demand forecasting model to obtain the market demand data for a period of time in the future, and then obtain the component demand for a period of time in the future.

[0007] Specifically, according to the component demand and component supply data in the future, the dynamic priority of each production task is obtained, which is as follows: Dynamic priority = initial priority + α * demand urgency + β * component demand + γ * component supply data Among them, initial priority: the priority determined according to the initial setting of the production task or historical data; Demand urgency is the demand urgency in the market demand data. α, β, γ: They are weight coefficients of demand urgency, component demand, and component supply data, respectively. Each weight coefficient can be adjusted according to actual conditions.

[0008] Specifically, for each production task, based on the production line data, a production line optimization configuration algorithm based on deep learning is used to adjust the production line configuration, specifically: Obtain historical equipment scheduling parameters, historical worker allocation data, and historical production efficiency; Build a production line optimization configuration model based on deep learning, use historical equipment scheduling parameters and historical worker allocation data as inputs to the production line optimization configuration model, use historical production efficiency as the production line optimization configuration model, train the production line optimization configuration model, and obtain a trained production line optimization configuration model; Adjust the input of the trained production line optimization configuration model, namely the equipment scheduling parameters and worker allocation data, until the production efficiency reaches the set standard, thereby determining the equipment scheduling parameters and worker allocation data.

[0009] Specifically, according to claim 1, a method for real-time production scheduling of mobile phone components is characterized in that data in the production process is acquired in real time, updated to production line data, and production line configuration is adjusted in real time based on a production line optimization configuration algorithm, specifically: Calculate the effective production efficiency based on the data obtained in real time during the production process to obtain the effective production efficiency; When the effective production efficiency is lower than the set standard; Adjust the trained production line optimization configuration model input, i.e., equipment scheduling parameters and worker allocation data, until the effective production efficiency reaches the set standard, and determine the adjusted equipment scheduling parameters and worker allocation data.

[0010] Specifically, the real-time production efficiency is calculated based on the data obtained in the real-time production process to obtain the adjusted production efficiency, which is specifically: Effective production efficiency = output * quality pass rate / time.

[0011] The embodiment of the present invention also provides a mobile phone component real-time production scheduling system, including: Data acquisition unit: acquires data and pre-processes the data, wherein the data includes historical sales data and current sales data, market demand data, component supply data and production line data; Demand forecasting model building unit: Demand forecasting model building, based on historical sales data and market demand data, building a demand forecasting model to predict component demand in the future; Dynamic priority calculation unit: obtains the dynamic priority of each production task based on the component demand and component supply data in the future; Production line configuration unit: For each production task, based on the production line data, the production line optimization configuration algorithm based on deep learning is used to adjust the production line configuration; Feedback adjustment unit: acquires data from the production process in real time, updates the data to the production line data, and adjusts the production line configuration in real time based on the production line optimization configuration algorithm. The data from the production process includes but is not limited to the quality pass rate.

[0012] On the other hand, the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements steps of a method for just-in-time production scheduling of mobile phone components when executing the computer program.

[0013] Yet another aspect of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, a method for just-in-time production scheduling of mobile phone components is implemented.

[0014] It can be seen from the above description of the present invention that, compared with the prior art, the present invention has the following beneficial effects: 1) The present invention proposes a method for real-time production scheduling of mobile phone components, comprising the following steps: acquiring data and preprocessing the data, the data including historical sales data and current sales data, market demand data, component supply data and production line data; constructing a demand forecasting model based on historical sales data and market demand data to predict the demand for components in the future; obtaining the dynamic priority of each production task based on the component demand and component supply data in the future; for each production task, based on the production line data, using a production line optimization configuration algorithm based on deep learning to adjust the production line configuration; acquiring data in the production process in real time, updating it to the production line data, and adjusting the production line configuration in real time based on the production line optimization configuration algorithm, the data in the production process includes but is limited to the quality pass rate. The present invention proposes a method for real-time production scheduling of mobile phone components, which can comprehensively consider multiple factors such as market demand, component supply, and production line capacity, realize real-time adjustment and optimization of production plans, improve production efficiency, and reduce production cycles.

[0015] 2) The method of the present invention can adjust the production plan in real time according to market demand and component supply, avoiding inventory backlog and waste.

[0016] 3) The method of the present invention uses a real-time scheduling algorithm to quickly respond to changes in market demand and improve market response speed and flexibility.

[0017] 4) The method of the present invention can reduce production costs by optimizing production plans and reducing inventory backlogs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a method for real-time production scheduling of mobile phone components provided by an embodiment of the present invention; Figure 2 A structural diagram of a mobile phone component real-time production scheduling system provided by an embodiment of the present invention; Figure 3 A schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention; Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is further described below through specific implementation modes.

[0020] The present invention proposes a method for real-time production scheduling of mobile phone components, which can comprehensively consider multiple factors such as market demand, component supply, production line capacity, etc., realize real-time adjustment and optimization of production plans, improve production efficiency, and reduce production cycles.

[0021] like Figure 1 The present invention provides a method for real-time production scheduling of mobile phone components; the method specifically comprises the following steps: A method for real-time production scheduling of mobile phone components comprises the following steps: S101: Acquire data and pre-process the data, wherein the data includes historical sales data and current sales data, market demand data, component supply data, and production line data; Specifically, the market demand data includes but is not limited to: the demand quantity and urgency of each mobile phone component; the component supply data includes but is not limited to: supplier inventory, transportation time, and supply stability; the production line data includes but is not limited to: the production capacity of the production line, current production status, and equipment failure rate.

[0022] The acquired data is preprocessed, including data cleaning, format conversion, normalization, etc., to ensure the quality and consistency of the data.

[0023] S102: Construction of demand forecasting model: Based on historical sales data and market demand data, a demand forecasting model is constructed to predict the demand for components in the future; Specifically, the demand forecasting model is constructed based on historical sales data and market demand data to predict the demand for components in the future. Specifically: Construct a demand forecasting model, use historical sales data as input of the demand forecasting model, use market demand data as output of the demand forecasting model, train the demand forecasting model, and obtain a trained demand forecasting model; Input the current sales data into the trained demand forecasting model to obtain the market demand data for a period of time in the future, and then obtain the component demand for a period of time in the future.

[0024] Among them, the demand forecasting models are common models such as convolutional neural networks (CNN), recurrent neural networks (RNN) or deep reinforcement learning models.

[0025] S103: Obtaining the dynamic priority of each production task according to the component demand and component supply data in the future period; Dynamic priority refers to the priority given when creating a process or task, which can change as the process progresses or the waiting time increases in order to obtain better scheduling performance. In production scheduling, the priority of each production task is not fixed, but adjusted according to real-time information such as production demand and supply.

[0026] Specifically, according to the component demand and component supply data in the future, the dynamic priority of each production task is obtained, which is as follows: Dynamic priority = initial priority + α * demand urgency + β * component demand + γ * component supply data Among them, initial priority: the priority determined according to the initial setting of the production task or historical data; Demand urgency is the demand urgency in the market demand data; the demand urgency is quantitatively evaluated based on the delivery period. In this formula, the initial priority, demand urgency, component demand, and component supply data are all quantified as 0-1, and the closer to 1, the higher the priority.

[0027] For example, the more urgent the demand, the closer it is to 1; the greater the component demand, the closer it is to 1; and the greater the component supply data, the closer it is to 1.

[0028] α, β, γ: They are weight coefficients of demand urgency, component demand, and component supply data, respectively. Each weight coefficient can be adjusted according to actual conditions.

[0029] Dynamic priority adjustment strategy is a flexible and effective production scheduling method that can dynamically adjust the priority of each production task according to the real-time situation. Through reasonable implementation strategies and optimization measures, it can significantly improve production efficiency, reduce inventory backlogs, enhance market responsiveness, and provide strong support for the sustainable development of enterprises.

[0030] S104: For each production task, based on the production line data, a production line optimization configuration algorithm based on deep learning is used to adjust the production line configuration; After determining the priority of each production task, the configuration of the production line needs to be determined for each production task.

[0031] Specifically, for each production task, based on the production line data, a production line optimization configuration algorithm based on deep learning is used to adjust the production line configuration, specifically: Obtain historical equipment scheduling parameters, historical worker allocation data, and historical production efficiency; Build a production line optimization configuration model based on deep learning, use historical equipment scheduling parameters and historical worker allocation data as inputs to the production line optimization configuration model, use historical production efficiency as the production line optimization configuration model, train the production line optimization configuration model, and obtain a trained production line optimization configuration model; Adjust the input of the trained production line optimization configuration model, namely the equipment scheduling parameters and worker allocation data, until the production efficiency reaches the set standard, thereby determining the equipment scheduling parameters and worker allocation data.

[0032] Among them, deep learning production line optimization configuration models, such as convolutional neural network (CNN), recurrent neural network (RNN) or deep reinforcement learning models, etc. Design the architecture of the model, including the input layer, hidden layer and output layer, and determine the parameters and activation functions of each layer.

[0033] Use optimization algorithms (such as gradient descent, Adam, etc.) to optimize the model to improve the prediction accuracy and generalization ability of the model.

[0034] The production line optimization configuration model based on deep learning aims to maximize production efficiency by intelligently adjusting production resource configuration through real-time analysis of production line data.

[0035] S105: Acquire data from the production process in real time, update the data to the production line data, and adjust the production line configuration in real time based on the production line optimization configuration algorithm. The data from the production process includes but is limited to the quality pass rate.

[0036] Acquire data from the production process in real time, update it to the production line data, and adjust the production line configuration in real time based on the production line optimization configuration algorithm. Specifically: Calculate the effective production efficiency based on the data obtained in real time during the production process to obtain the effective production efficiency; When the effective production efficiency is lower than the set standard; Adjust the trained production line optimization configuration model input, i.e., equipment scheduling parameters and worker allocation data, until the effective production efficiency reaches the set standard, and determine the adjusted equipment scheduling parameters and worker allocation data.

[0037] Specifically, the real-time production efficiency is calculated based on the data obtained in the real-time production process to obtain the adjusted production efficiency, which is specifically: Effective production efficiency = output * quality pass rate / time.

[0038] like Figure 2 The embodiment of the present invention also provides a system for real-time production scheduling of mobile phone components, including: Data acquisition unit 201: acquires data and pre-processes the data, wherein the data includes historical sales data and current sales data, market demand data, component supply data and production line data; Specifically, the market demand data includes but is not limited to: the demand quantity and urgency of each mobile phone component; the component supply data includes but is not limited to: supplier inventory, transportation time, and supply stability; the production line data includes but is not limited to: the production capacity of the production line, current production status, and equipment failure rate.

[0039] The acquired data is preprocessed, including data cleaning, format conversion, normalization, etc., to ensure the quality and consistency of the data.

[0040] Demand forecasting model building unit 202: building a demand forecasting model, based on historical sales data and market demand data, building a demand forecasting model to forecast component demand in the future; Specifically, the demand forecasting model is constructed based on historical sales data and market demand data to predict the demand for components in the future. Specifically: Construct a demand forecasting model, use historical sales data as input of the demand forecasting model, use market demand data as output of the demand forecasting model, train the demand forecasting model, and obtain a trained demand forecasting model; Input the current sales data into the trained demand forecasting model to obtain the market demand data for a period of time in the future, and then obtain the component demand for a period of time in the future.

[0041] Among them, the demand forecasting models are common models such as convolutional neural networks (CNN), recurrent neural networks (RNN) or deep reinforcement learning models.

[0042] Dynamic priority calculation unit 203: obtains the dynamic priority of each production task according to the component demand and component supply data in a future period of time; Dynamic priority refers to the priority given when creating a process or task, which can change as the process progresses or the waiting time increases in order to obtain better scheduling performance. In production scheduling, the priority of each production task is not fixed, but adjusted according to real-time information such as production demand and supply.

[0043] Specifically, according to the component demand and component supply data in the future, the dynamic priority of each production task is obtained, which is as follows: Dynamic priority = initial priority + α * demand urgency + β * component demand + γ * component supply data Among them, initial priority: the priority determined according to the initial setting of the production task or historical data; Demand urgency is the demand urgency in the market demand data; the demand urgency is quantitatively evaluated based on the delivery period. In this formula, the initial priority, demand urgency, component demand, and component supply data are all quantified as 0-1, and the closer to 1, the higher the priority.

[0044] For example, the more urgent the demand, the closer it is to 1; the greater the component demand, the closer it is to 1; and the greater the component supply data, the closer it is to 1.

[0045] α, β, γ: They are weight coefficients of demand urgency, component demand, and component supply data, respectively. Each weight coefficient can be adjusted according to actual conditions.

[0046] Dynamic priority adjustment strategy is a flexible and effective production scheduling method that can dynamically adjust the priority of each production task according to the real-time situation. Through reasonable implementation strategies and optimization measures, it can significantly improve production efficiency, reduce inventory backlogs, enhance market responsiveness, and provide strong support for the sustainable development of enterprises.

[0047] Production line configuration unit 204: for each production task, based on the production line data, using a production line optimization configuration algorithm based on deep learning to adjust the production line configuration; After determining the priority of each production task, the configuration of the production line needs to be determined for each production task.

[0048] Specifically, for each production task, based on the production line data, a production line optimization configuration algorithm based on deep learning is used to adjust the production line configuration, specifically: Obtain historical equipment scheduling parameters, historical worker allocation data, and historical production efficiency; Build a production line optimization configuration model based on deep learning, use historical equipment scheduling parameters and historical worker allocation data as inputs to the production line optimization configuration model, use historical production efficiency as the production line optimization configuration model, train the production line optimization configuration model, and obtain a trained production line optimization configuration model; Adjust the input of the trained production line optimization configuration model, namely the equipment scheduling parameters and worker allocation data, until the production efficiency reaches the set standard, thereby determining the equipment scheduling parameters and worker allocation data.

[0049] Among them, deep learning production line optimization configuration models, such as convolutional neural network (CNN), recurrent neural network (RNN) or deep reinforcement learning models, etc. Design the architecture of the model, including the input layer, hidden layer and output layer, and determine the parameters and activation functions of each layer.

[0050] Use optimization algorithms (such as gradient descent, Adam, etc.) to optimize the model to improve the prediction accuracy and generalization ability of the model.

[0051] The production line optimization configuration model based on deep learning aims to maximize production efficiency by intelligently adjusting production resource configuration through real-time analysis of production line data.

[0052] Feedback adjustment unit 205: acquires data in the production process in real time, updates the data to the production line data, and adjusts the production line configuration in real time based on the production line optimization configuration algorithm. The data in the production process includes but is limited to the quality pass rate.

[0053] like Figure 3 As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, a method for just-in-time production scheduling of mobile phone components provided in an embodiment of the present invention is implemented.

[0054] In the specific implementation process, when the processor 320 executes the computer program 311, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0055] Since the electronic device introduced in this embodiment is a device used to implement a data processing device in the embodiment of the present invention, based on the method introduced in the embodiment of the present invention, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present invention is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present invention falls within the scope of protection of the present invention.

[0056] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention.

[0057] like Figure 4 As shown, this embodiment provides a computer-readable storage medium 400, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, a method for real-time production scheduling of mobile phone components provided in an embodiment of the present invention is implemented; In a specific implementation process, when the computer program 411 is executed by a processor, Figure 1 Any implementation manner in the corresponding embodiments.

[0058] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0059] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] 1) The present invention proposes a method for real-time production scheduling of mobile phone components, comprising the following steps: acquiring data and preprocessing the data, the data including historical sales data and current sales data, market demand data, component supply data and production line data; constructing a demand forecasting model based on historical sales data and market demand data to predict the demand for components in the future; obtaining the dynamic priority of each production task based on the component demand and component supply data in the future; for each production task, based on the production line data, using a production line optimization configuration algorithm based on deep learning to adjust the production line configuration; acquiring data in the production process in real time, updating it to the production line data, and adjusting the production line configuration in real time based on the production line optimization configuration algorithm, the data in the production process includes but is limited to the quality pass rate. The present invention proposes a method for real-time production scheduling of mobile phone components, which can comprehensively consider multiple factors such as market demand, component supply, and production line capacity, realize real-time adjustment and optimization of production plans, improve production efficiency, and reduce production cycles.

[0061] 2) The method of the present invention can adjust the production plan in real time according to market demand and component supply, avoiding inventory backlog and waste.

[0062] 3) The method of the present invention uses a real-time scheduling algorithm to quickly respond to changes in market demand and improve market response speed and flexibility.

[0063] 4) The method of the present invention can reduce production costs by optimizing production plans and reducing inventory backlogs.

[0064] The above is only a specific implementation of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial changes to the present invention using this concept shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. A method for real-time production scheduling of mobile phone components, characterized in that: The following steps are involved: Acquire and pre-process data, including historical sales data and current sales data, market demand data, component supply data, and production line data; Construction of demand forecasting model: Based on historical sales data and market demand data, a demand forecasting model is constructed to predict the demand for components in the future; According to the component demand and component supply data in the future, the dynamic priority of each production task is obtained; For each production task, based on the production line data, a production line optimization configuration algorithm based on deep learning is used to adjust the production line configuration; The data in the production process is acquired in real time, updated to the production line data, and the production line configuration is adjusted in real time based on the production line optimization configuration algorithm. The data in the production process includes but is limited to the quality pass rate.

2. A method for real-time production scheduling of mobile phone components according to claim 1, characterized in that: The market demand data includes but is not limited to: the demand quantity and urgency of each mobile phone component; the component supply data includes but is not limited to: supplier inventory, transportation time, and supply stability; the production line data includes but is not limited to: the production capacity of the production line, current production status, and equipment failure rate.

3. A method for real-time production scheduling of mobile phone components according to claim 1, characterized in that: The construction of the demand forecasting model is based on historical sales data and market demand data to build a demand forecasting model to predict the demand for components in the future. Specifically: Construct a demand forecasting model, use historical sales data as input of the demand forecasting model, use market demand data as output of the demand forecasting model, train the demand forecasting model, and obtain a trained demand forecasting model; Input the current sales data into the trained demand forecasting model to obtain the market demand data for a period of time in the future, and then obtain the component demand for a period of time in the future.

4. A method for real-time production scheduling of mobile phone components according to claim 1, characterized in that: According to the component demand and component supply data in the future, the dynamic priority of each production task is obtained, which is as follows: Dynamic priority = initial priority + α * demand urgency + β * component demand + γ * component supply data Among them, initial priority: the priority determined according to the initial setting of the production task or historical data; Demand urgency is the demand urgency in the market demand data. α, β, γ: They are weight coefficients of demand urgency, component demand, and component supply data, respectively. Each weight coefficient can be adjusted according to actual conditions.

5. The method for real-time production scheduling of mobile phone components according to claim 1, characterized in that: For each production task, based on the production line data, the production line optimization configuration algorithm based on deep learning is used to adjust the production line configuration, specifically: Obtain historical equipment scheduling parameters, historical worker allocation data, and historical production efficiency; Build a production line optimization configuration model based on deep learning, use historical equipment scheduling parameters and historical worker allocation data as inputs of the production line optimization configuration model, use historical production efficiency as the production line optimization configuration model, train the production line optimization configuration model, and obtain a trained production line optimization configuration model; Adjust the input of the trained production line optimization configuration model, namely the equipment scheduling parameters and worker allocation data, until the production efficiency reaches the set standard, thereby determining the equipment scheduling parameters and worker allocation data.

6. A method for real-time production scheduling of mobile phone components according to claim 1, characterized in that: Acquire data from the production process in real time, update it to the production line data, and adjust the production line configuration in real time based on the production line optimization configuration algorithm. Specifically: Calculate the effective production efficiency based on the data obtained in real time during the production process to obtain the effective production efficiency; When the effective production efficiency is lower than the set standard; Adjust the trained production line optimization configuration model input, i.e., equipment scheduling parameters and worker allocation data, until the effective production efficiency reaches the set standard, and determine the adjusted equipment scheduling parameters and worker allocation data.

7. A method for real-time production scheduling of mobile phone components according to claim 6, characterized in that: The real-time production efficiency is calculated based on the data obtained in the real-time production process, and the adjusted production efficiency is obtained, which is specifically: Effective production efficiency = output * quality pass rate / time.

8. A mobile phone component real-time production scheduling system, characterized in that: include: Data acquisition unit: acquires data and pre-processes the data, wherein the data includes historical sales data and current sales data, market demand data, component supply data and production line data; Demand forecasting model building unit: Demand forecasting model building, based on historical sales data and market demand data, building a demand forecasting model to predict component demand in the future; Dynamic priority calculation unit: obtains the dynamic priority of each production task based on the component demand and component supply data in the future; Production line configuration unit: For each production task, based on the production line data, the production line optimization configuration algorithm based on deep learning is used to adjust the production line configuration; Feedback adjustment unit: acquires data from the production process in real time, updates the data to the production line data, and adjusts the production line configuration in real time based on the production line optimization configuration algorithm. The data from the production process includes but is not limited to the quality pass rate.

9. An electronic device, characterized in that: include: A memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method steps of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps of any one of claims 1 to 7 are implemented.

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