Task scheduling method and device based on intelligent MRP operation, medium and program product

Abstract

The invention discloses a task scheduling method and device based on intelligent MRP operation, a medium and a program product. The task scheduling method comprises the steps that first real-time production data in the production process is obtained; performing data preprocessing on the first real-time production data to obtain second real-time production data; carrying out stream data analysis processing on the second real-time production data based on an Flink framework to obtain a real-time data analysis result; the real-time data analysis result is input into a trained intelligent MRP operation model, an MRP production scheduling plan is generated by the intelligent MRP operation model according to real-time productivity and user order demands, and the intelligent MRP operation model is constructed based on a machine learning algorithm and is trained by using historical MRP data; and finally, carrying out production task scheduling according to the MRP production scheduling plan. According to the invention, the accuracy of MRP operation is effectively improved, and the instantaneity and responsiveness of generating the MRP production scheduling plan are further improved, so that the production efficiency is improved, and the resource waste is reduced.

Description

Technical Field

[0001] The present invention relates to the field of machine learning technology, and in particular to a task scheduling method, device, medium and program product based on intelligent MRP calculation. Background Art

[0002] In the modern supply chain, coordination and collaboration between suppliers, manufacturers and distributors are becoming increasingly important. In traditional ERP systems, the MRP calculation method is indeed difficult to meet the actual needs of production and manufacturing in some cases, and often fails to achieve efficient integration of production and supply chain, resulting in resource fragmentation and low production efficiency.

[0003] The data sources of production workshops, equipment, and raw materials in the manufacturing industry are numerous and complex, and traditional manual MRP operations cannot handle large-scale real-time data flows. Traditional production planning management is often based on static demand forecasts and manual experience, and cannot be adjusted in real time according to market demand and resource changes. The MRP operation decision-making process in the manufacturing industry often relies on experience and cannot make full use of real-time data and complex production environment information. Traditional MRP operation systems are often batch processing, which cannot meet the needs of real-time scheduling and feedback.

[0004] Therefore, it is urgent to invent an MRP task scheduling method to solve the problems existing in the prior art that the MRP operation cannot handle large-scale real-time data streams, the MRP accuracy is low, and the real-time scheduling feedback cannot be achieved. Summary of the invention

[0005] In view of this, embodiments of the present invention provide a task scheduling method, device, medium and program product based on intelligent MRP calculation, which at least partially solve the problems existing in the prior art.

[0006] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

[0007] In order to achieve the above purpose, the embodiment of the present invention provides the following technical solutions:

[0008] According to a first aspect of an embodiment of the present invention, a task scheduling method based on intelligent MRP calculation is provided, the method comprising:

[0009] Get the first real-time production data during the production process;

[0010] Performing data preprocessing on the first real-time production data to obtain second real-time production data;

[0011] Perform stream data analysis on the second real-time production data based on the Flink framework to obtain real-time data analysis results;

[0012] The real-time data analysis results are input into a trained intelligent MRP operation model, and the intelligent MRP operation model generates an MRP production schedule according to the real-time production capacity and user order requirements. The intelligent MRP operation model is an intelligent MRP operation model built based on a machine learning algorithm and trained using historical MRP data;

[0013] The production tasks are scheduled according to the MRP production schedule.

[0014] Furthermore, obtaining first real-time production data in the production process includes:

[0015] Initialize the current MRP data;

[0016] First real-time production data is collected during the production process, wherein the first real-time production data includes material data, BOM structure data, demand resource data, supply resource data, and production equipment status.

[0017] Furthermore, the data preprocessing includes data cleaning, data deduplication and data formatting.

[0018] Furthermore, based on the Flink framework, stream data analysis is performed on the second real-time production data to obtain real-time data analysis results, including:

[0019] Based on the Flink framework, a machine learning algorithm is used to perform stream data analysis on the second real-time production data to obtain real-time data analysis results;

[0020] The stream data analysis and processing includes real-time feature extraction, abnormal equipment detection, production scheduling pattern recognition and real-time capacity analysis.

[0021] Furthermore, the intelligent MRP operation model is used to predict the production equipment capacity, evaluate the urgency of user orders and optimize the production sequence.

[0022] Furthermore, the intelligent MRP operation model generates an MRP production schedule according to real-time production capacity and user order requirements, and further includes:

[0023] According to the model optimization strategy of the intelligent MRP operation model, the intelligent MRP operation model is optimized and iterated using the MRP production scheduling plan.

[0024] Furthermore, the production task is scheduled according to the MRP production scheduling plan, including:

[0025] Based on the Flink framework, production task scheduling, production resources and production equipment allocation are performed according to the MRP production scheduling plan;

[0026] Use the Flink framework to monitor production progress and production equipment status in real time and obtain real-time monitoring results;

[0027] The intelligent MRP operation model is optimized and adjusted according to the real-time monitoring results, and the adjustment results are fed back to the production equipment and production department.

[0028] According to a second aspect of an embodiment of the present invention, there is provided a task scheduling device based on intelligent MRP operation, the device comprising: a processor and a memory;

[0029] The memory is used to store one or more program instructions;

[0030] The processor is used to run one or more program instructions to execute the steps of a task scheduling method based on intelligent MRP calculation as described in any one of the above items.

[0031] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of a task scheduling method based on intelligent MRP calculation as described in any one of the above items are implemented.

[0032] According to a fourth aspect of an embodiment of the present invention, a computer program product is provided, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the computer implements the steps of a task scheduling method based on intelligent MRP operation as described in any one of the above items.

[0033] The embodiments of the present invention have the following advantages:

[0034] The embodiment of the present invention is based on the stream processing capability of the Flink framework and combines the optimization algorithm of artificial intelligence to realize the rapid processing and analysis of real-time data, and dynamically adjust the production plan and task scheduling according to the real-time situation. Compared with the traditional manual MRP calculation that consumes a lot of time and manpower and cannot realize real-time data processing and scheduling decision-making, the present invention effectively improves the real-time and responsiveness of production scheduling.

[0035] The artificial intelligence-based technology of the embodiment of the present invention can accurately predict production demand by analyzing historical data, building models, and combining real-time data and business rules, thereby more accurately formulating and adjusting MRP operation strategies. Compared with traditional MRP operations that rely on static demand forecasts and empirical judgments and are difficult to accurately predict production demand and resource utilization, the present invention effectively improves the effect and accuracy of MRP operations.

[0036] The artificial intelligence-based technology of the embodiment of the present invention can realize intelligent scheduling and allocation of production resources by optimizing algorithms and models, analyzing real-time data and business rules, and combining information such as the status and capabilities of equipment. Compared with the traditional manual scheduling method that cannot fully optimize resource utilization and is prone to resource waste and imbalance, the present invention optimizes resource utilization and intelligent MRP calculation to generate MRP scheduling plans for task scheduling, which can improve production efficiency and reduce resource waste.

[0037] The artificial intelligence-based technology of the embodiment of the present invention can be combined with data analysis and optimization algorithms to improve the scientificity and accuracy of MRP operation decisions by establishing models and automated decision inference. Compared with the traditional manual MRP operation method, which relies on experience and subjective judgment and is easily affected by human factors, the present invention automatically generates and adjusts production plans based on models and algorithms, which can effectively improve the quality and accuracy of decisions.

[0038] In summary, the embodiment of the present invention combines intelligent MRP operation with the Flink framework for production task scheduling, effectively improving the real-time, accuracy, resource utilization efficiency and decision-making quality of production scheduling. By optimizing the MRP operation process and decision-making scheme, the accuracy and efficiency of MRP plan generation are improved, resource costs and energy consumption are reduced, and the optimization and improvement of production scheduling and scheduling are promoted, thereby enhancing the competitiveness of enterprises and production operation effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0040] Figure 1 A flowchart of a task scheduling method based on intelligent MRP calculation provided by an embodiment of the present invention;

[0041] Figure 2 A schematic block diagram of the principles of a task scheduling method based on intelligent MRP calculation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0043] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0044] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations:

[0045] ERP system: ERP system (Enterprise Resource Planning) is an integrated software system used to manage various business processes within an enterprise, including product design, procurement, production, sales, finance, human resources, etc. The ERP system integrates the data and processes of various departments of the enterprise, allowing information to flow smoothly between different departments, thereby improving the company's operational efficiency and management decision-making capabilities.

[0046] BOM structure: BOM (Bill of Materials) structure data is a document or database that describes the detailed information of all materials, components and raw materials required for a product or component. BOM is a key component in the product design and manufacturing process and is commonly used in production, procurement and inventory management.

[0047] MRP calculation: Material Requirements Planning (MRP) is a planning system used in the manufacturing industry to determine the demand for raw materials, parts and finished products and when these materials are needed for production. The goal of MRP is to ensure that materials are in sufficient supply to meet the requirements of the production plan. During the MRP calculation process, multiple factors need to be considered, such as the priority of production tasks, the dependencies between processes, the required equipment, manpower and material resources, etc. The goal of MRP calculation is to arrange the production sequence reasonably, maximize production efficiency, meet delivery requirements, reduce costs, and ensure the rational use of resources.

[0048] Flink: Flink (Apache Flink) is an open source stream processing and batch processing framework with high performance, fault tolerance and flexibility. The Flink framework can process large-scale real-time data streams and also supports batch data processing. It provides a rich set of stream processing operations and functions to achieve complex data stream conversion, calculation and analysis.

[0049] Artificial Intelligence: Artificial Intelligence (AI) is a branch of computer science that aims to simulate and emulate the theories, methods, technologies and applications of human intelligence. It is dedicated to the research and development of algorithms and systems that enable computers to think, learn, reason and make decisions like humans.

[0050] Figure 1 The figure shows a flow chart of a task scheduling method based on intelligent MRP calculation according to an embodiment of the present invention.

[0051] like Figure 1 As shown, the task scheduling method based on intelligent MRP calculation according to an embodiment of the present invention may include step S100, step S200, step S300, step S400 and step S500.

[0052] refer to Figure 2 In step S100, first real-time production data in the production process is obtained.

[0053] Specifically, the above steps include:

[0054] First, the current MRP data is initialized, and then the first real-time production data in the production process is collected. The first real-time production data includes material data, BOM structure data, demand resource data, supply resource data and production equipment status.

[0055] Next, in step S200, data preprocessing is performed on the first real-time production data to obtain second real-time production data.

[0056] Specifically, the above data preprocessing includes data cleaning, data deduplication and data formatting, such as removing equipment anomalies and insufficient workshop capacity to ensure the accuracy of the data.

[0057] In step S300, stream data analysis is performed on the second real-time production data based on the Flink framework to obtain real-time data analysis results.

[0058] Specifically, the above steps include:

[0059] Based on the stream processing capabilities of the Flink framework, machine learning and deep learning algorithms are used to perform stream data analysis and processing on the second real-time production data to obtain real-time data analysis results. The stream data analysis and processing includes real-time feature extraction, abnormal equipment detection, production scheduling pattern recognition, and real-time capacity analysis.

[0060] Next, in step S400, the above real-time data analysis results are input into the trained intelligent MRP operation model, and the intelligent MRP operation model generates an MRP production schedule according to the real-time production capacity and user order requirements.

[0061] Specifically, the above steps include:

[0062] Firstly, an intelligent MRP operation model to be trained is constructed based on the machine learning algorithm, and then the intelligent MRP operation model is trained using historical MRP data to obtain a trained intelligent MRP operation model to improve the model accuracy and prediction ability. The intelligent MRP operation model is used to predict the production equipment capacity, evaluate the urgency of user orders, and optimize the production sequence.

[0063] The above real-time data analysis results are input into the trained intelligent MRP operation model, which generates a matching MRP production schedule based on real-time production capacity and user order requirements.

[0064] According to the model optimization strategy of the intelligent MRP operation model, the intelligent MRP operation model is further optimized and iterated using the MRP production scheduling plan to meet the constraints and improve the model efficiency.

[0065] Finally, in step S500, production tasks are scheduled according to the above MRP production scheduling plan.

[0066] Specifically, the above steps include:

[0067] Based on the Flink framework, production tasks are scheduled according to the MRP production schedule, and production resources and production tasks are allocated to various production equipment. At the same time, the Flink framework is used to monitor the production progress and production equipment status in real time, obtain real-time monitoring results, and handle abnormal situations in real time.

[0068] The intelligent MRP operation model is then automatically optimized and adjusted based on real-time monitoring results and abnormal situation feedback information, the operation strategy is dynamically adjusted, and the adjustment results are fed back to production equipment and production departments to adapt to changing production environments and needs.

[0069] The embodiment of the present invention solves key technical problems such as complex production environment, multiple constraints, high real-time and reliability requirements, high-dimensional decision space, multi-objective optimization and collaborative management through the Flink framework and artificial intelligence technology, providing enterprises with a more intelligent and efficient MRP operation solution.

[0070] The stream processing technology based on Flink in the embodiment of the present invention can process a large amount of data streams in real time and provide flexible window operation and timing analysis capabilities, thus solving the difficulty that the existing technology cannot process large-scale real-time data.

[0071] The embodiment of the present invention is based on artificial intelligence technology, uses real-time data for feature extraction and modeling, performs intelligent MRP operation decisions through machine learning and optimization algorithms, and combines Flink's stream processing capabilities to achieve data-driven optimization decisions, so as to solve the problem that the MRP operation decision-making process in the prior art often relies on experience and cannot make full use of real-time data and complex production environment information.

[0072] The embodiment of the present invention is based on artificial intelligence technology, uses real-time data for intelligent prediction and optimization decision-making, and combines the real-time processing capabilities of the Flink framework to achieve efficient production plan management, so as to solve the problem that traditional production plan management cannot be adjusted in real time according to market demand and resource changes.

[0073] The embodiment of the present invention uses Flink's stream processing technology to schedule production tasks and monitor production status, equipment efficiency, production plan and other information in real time, adjusts MRP operations according to real-time data, and feeds back the adjustment results to relevant departments and equipment to achieve real-time scheduling and feedback, thereby solving the problem that traditional MRP operation systems are often batch processing and cannot meet the needs of real-time scheduling and feedback.

[0074] In summary, the embodiments of the present invention combine the Flink framework with the intelligent MRP operation of artificial intelligence to solve the technical problems of data processing complexity and inefficient production plan management faced in the traditional production scheduling process, and realize real-time data processing, intelligent optimization decision-making, real-time scheduling and feedback functions in complex production environments, thereby improving the accuracy, efficiency and responsiveness of production plans, and further promoting the improvement of production efficiency in the manufacturing industry.

[0075] In addition, an embodiment of the present invention also provides a task scheduling device based on intelligent MRP operation, which includes: a processor and a memory; the memory is used to store one or more program instructions; the processor is used to run one or more program instructions to execute the steps of a task scheduling method based on intelligent MRP operation as described above.

[0076] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the task scheduling method based on intelligent MRP calculation are implemented as described above.

[0077] In addition, an embodiment of the present invention further provides a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor, the steps of the task scheduling method based on intelligent MRP calculation are implemented as described above.

[0078] The embodiments of the present invention can be widely applied to various manufacturing fields, so that enterprises can optimize resource allocation, improve production efficiency and supply chain coordination, reduce costs, improve service quality, and ultimately enhance the competitiveness of enterprises. The present invention can achieve more intelligent, efficient and sustainable production management.

[0079] The embodiments of the present invention have the following advantages:

[0080] The embodiment of the present invention is based on the stream processing capability of the Flink framework and combines the optimization algorithm of artificial intelligence to realize the rapid processing and analysis of real-time data, and dynamically adjust the production plan and task scheduling according to the real-time situation. Compared with the traditional manual MRP calculation that consumes a lot of time and manpower and cannot realize real-time data processing and scheduling decision-making, the present invention effectively improves the real-time and responsiveness of production scheduling.

[0081] The artificial intelligence-based technology of the embodiment of the present invention can accurately predict production demand by analyzing historical data, building models, and combining real-time data and business rules, thereby more accurately formulating and adjusting MRP operation strategies. Compared with traditional MRP operations that rely on static demand forecasts and empirical judgments and are difficult to accurately predict production demand and resource utilization, the present invention effectively improves the effect and accuracy of MRP operations.

[0082] The artificial intelligence-based technology of the embodiment of the present invention can realize intelligent scheduling and allocation of production resources by optimizing algorithms and models, analyzing real-time data and business rules, and combining information such as the status and capabilities of equipment. Compared with the traditional manual scheduling method that cannot fully optimize resource utilization and is prone to resource waste and imbalance, the present invention optimizes resource utilization and intelligent MRP calculation to generate MRP scheduling plans for task scheduling, which can improve production efficiency and reduce resource waste.

[0083] The artificial intelligence-based technology of the embodiment of the present invention can be combined with data analysis and optimization algorithms to improve the scientificity and accuracy of MRP operation decisions by establishing models and automated decision inference. Compared with the traditional manual MRP operation method, which relies on experience and subjective judgment and is easily affected by human factors, the present invention automatically generates and adjusts production plans based on models and algorithms, which can effectively improve the quality and accuracy of decisions.

[0084] In summary, the embodiment of the present invention combines intelligent MRP calculation with Flink framework for production task scheduling, effectively improving the real-time, accuracy, resource utilization efficiency and decision-making quality of production scheduling. By optimizing the MRP calculation process and decision-making scheme, the accuracy and efficiency of production plan generation are improved, resource costs and energy consumption are reduced, and the optimization and improvement of production scheduling and scheduling are promoted, thereby enhancing the competitiveness of enterprises and production operation effects.

[0085] In the embodiment of the present invention, the processor may be an integrated circuit chip with signal processing capability. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, or discrete hardware components. The methods, steps, and logic block diagrams disclosed in the embodiments of the present invention may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The processor reads the information in the storage medium and completes the steps of the above method in combination with its hardware. The storage medium may be a memory, for example, a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM).The storage medium described in the embodiment of the present invention is intended to include but is not limited to these and any other suitable types of memory. Those skilled in the art should be aware that in one or more of the above examples, the functions described in the present invention can be implemented by a combination of hardware and software. When the software is applied, the corresponding function can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein the communication medium includes any medium that is convenient for transmitting a computer program from one place to another. The storage medium can be any available medium that a general or special-purpose computer can access. Although the present invention has been described in detail above with general descriptions and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to it on the basis of the present invention. Therefore, these modifications or improvements made on the basis of not departing from the spirit of the present invention all belong to the scope of protection claimed in the present invention.

Claims

1. A task scheduling method based on intelligent MRP calculation, characterized in that: The method comprises: Get the first real-time production data during the production process; Performing data preprocessing on the first real-time production data to obtain second real-time production data; Perform stream data analysis on the second real-time production data based on the Flink framework to obtain real-time data analysis results; The real-time data analysis results are input into a trained intelligent MRP operation model, and the intelligent MRP operation model generates an MRP production schedule according to the real-time production capacity and user order requirements. The intelligent MRP operation model is an intelligent MRP operation model built based on a machine learning algorithm and trained using historical MRP data; The production tasks are scheduled according to the MRP production schedule.

2. A task scheduling method based on intelligent MRP calculation as claimed in claim 1, characterized in that: Get first-hand real-time production data during production, including: Initialize the current MRP data; First real-time production data is collected during the production process, wherein the first real-time production data includes material data, BOM structure data, demand resource data, supply resource data, and production equipment status.

3. The task scheduling method based on intelligent MRP calculation as claimed in claim 1, characterized in that: The data preprocessing includes data cleaning, data deduplication and data formatting.

4. The task scheduling method based on intelligent MRP calculation as claimed in claim 1, characterized in that: Based on the Flink framework, stream data analysis and processing is performed on the second real-time production data to obtain real-time data analysis results, including: Based on the Flink framework, a machine learning algorithm is used to perform stream data analysis on the second real-time production data to obtain real-time data analysis results; The stream data analysis and processing includes real-time feature extraction, abnormal equipment detection, production scheduling pattern recognition and real-time capacity analysis.

5. The task scheduling method based on intelligent MRP calculation as claimed in claim 1, characterized in that: The intelligent MRP operation model is used to predict the production equipment capacity, evaluate the urgency of user orders and optimize the production sequence.

6. The task scheduling method based on intelligent MRP calculation as claimed in claim 1, characterized in that: The intelligent MRP operation model generates an MRP production schedule according to real-time production capacity and user order requirements, and also includes: According to the model optimization strategy of the intelligent MRP operation model, the intelligent MRP operation model is optimized and iterated using the MRP production scheduling plan.

7. The task scheduling method based on intelligent MRP calculation as claimed in claim 1, characterized in that: Scheduling production tasks according to the MRP production schedule includes: Based on the Flink framework, production task scheduling, production resources and production equipment allocation are performed according to the MRP production scheduling plan; Use the Flink framework to monitor production progress and production equipment status in real time and obtain real-time monitoring results; The intelligent MRP operation model is optimized and adjusted according to the real-time monitoring results, and the adjustment results are fed back to the production equipment and production department.

8. A task scheduling device based on intelligent MRP calculation, characterized in that: The device comprises: a processor and a memory; The memory is used to store one or more program instructions; The processor is used to run one or more program instructions to execute the steps of a task scheduling method based on intelligent MRP calculation as described in any one of claims 1 to 7.

9. 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 the processor, the steps of the task scheduling method based on intelligent MRP calculation as described in any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises computer program instructions, which, when executed by a processor, implement the steps of a task scheduling method based on intelligent MRP calculation as claimed in any one of claims 1 to 7.

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