A production task scheduling method and system based on big data

Abstract

The purpose of the present invention is to provide a production task scheduling method and system based on big data, the method comprising: obtaining production task data to be scheduled; obtaining processing data of production equipment; classifying the production tasks to be scheduled according to the equipment for production; and planning the production task sequence of the same production equipment. When facing multiple production tasks that do not have a sequential relationship, the present invention uniformly schedules production tasks in a centralized environment, allocates and schedules production tasks through resource constraints and cost constraints, and considers the net present value of each production task, sets a delay penalty and early completion reward for each production task, which can help enterprises intelligently complete the scheduling of production tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a production task scheduling method and system based on big data. Background Art

[0002] Production task scheduling is a key link to ensure the smooth operation of the production process. It involves regular, rapid, and centralized accounting and supervision of the execution of production operation plans, as well as the handling and prevention of deviations. This process not only includes the preparation of production operation plans, but also includes the detailed and specific management of daily production activities to ensure that production can proceed smoothly according to the plan. Production scheduling is an important part of the entire management work of departments, places, and enterprises, covering capital construction scheduling, enterprise production scheduling, vehicle transportation scheduling, material scheduling, construction scheduling and other aspects. Its basic task is to supervise and inspect the implementation of the plan, make a comprehensive balance of manpower and material resources, especially logistics supply, and promptly resolve various contradictions on the construction site and coordination and cooperation problems between various construction units to promote the completion of the project image progress on schedule. The implementation of production scheduling depends on the management of the planning, implementation, inspection, and summary cycle of production scheduling, which is the central link of enterprise production and operation management. As the functional department of production scheduling management, the production management department is the command center of the company's production. Production scheduling in a narrow sense refers to the technical work of production scheduling management, including the understanding, mastering, prevention, and handling of production and operation dynamics, as well as the control of key parts and coordination between departments.

[0003] Most of the existing technologies rely on priority to schedule production tasks, without considering the parallel processing of multiple production tasks of the same priority. In the process of scheduling multiple production tasks, the problem model is more complex and the calculation complexity is also higher. At present, there is no accurate scheduling solution to deal with the problem of scheduling multiple production tasks. Summary of the invention

[0004] The purpose of the present invention is to provide a production task scheduling method and system based on big data. When faced with multiple production tasks that have no sequence relationship, the method uniformly schedules production tasks in a centralized environment, allocates and schedules production tasks through resource constraints and cost constraints, and considers the net present value of each production task, sets delay penalties and early completion rewards for each production task, which can help enterprises intelligently complete the scheduling of production tasks.

[0005] A production task scheduling method based on big data, comprising: Obtain the production task data to be scheduled; Obtain processing data of production equipment; Classify the production tasks to be scheduled according to the equipment used for production; Plan the production task sequence of the same production equipment.

[0006] Preferably, the obtaining of production task data to be scheduled includes: Generate a production task library to be scheduled. The data of a single production task to be scheduled is represented as follows: ; in, Time for issuing production tasks, Deadline, The production equipment corresponding to the production task, The production process corresponding to the production task.

[0007] Preferably, the obtaining of processing data of production equipment includes: Generate a database of production equipment to be processed. The processing data of a single production equipment is expressed as: ; in, Represents the production tasks queued for the nth production process.

[0008] Preferably, the planning of the production task sequence of the same production equipment includes: Construct a multi-production task constraint model, expressed as: ; Among them, f is the duration of the production task, As a penalty for delay, The amount of resources that the production equipment supplies to the current production task. Indicates the resource demand of the current production task, i represents the i-th production task, and N is a positive integer; When multiple production tasks of the same priority are assigned for scheduling, a multi-production task constraint model is used to plan the production task sequence of the same production equipment.

[0009] Preferably, the planning of the production task sequence of the same production equipment includes: Set the objective function according to the multi-production task scheduling requirements: ; Among them, NCF is the production task Net present value at completion, is the discount rate, CIF is the production task Cash inflow at completion, when production tasks are delayed, i.e. f> , then U=1, otherwise U=0, B is the unit time reward when the production task is completed ahead of schedule, P is the unit time penalty when the project is delayed, the first term in the formula calculates the discounted cash flow for each production task and sums them up, the second term in the formula counts the net present value of each production task, the third term is the reward for multiple production tasks in advance, and the fourth term is the penalty for multiple production tasks being delayed.

[0010] Preferably, the classifying the production tasks to be scheduled according to the equipment for production includes: When there are multiple devices that meet the production requirements of a production task, select one production device to perform the production task: ; in, The utilization rate required for production equipment to perform production tasks, The energy consumption required for production equipment to perform production tasks, and is the weight coefficient of current production equipment utilization and energy consumption, is the i-th production equipment selected.

[0011] Preferably, the planning of the production task sequence of the same production equipment includes: If there is an urgent production task, the production task is scheduled manually and the urgent production task is inserted into the current production task scheduling list; Find a gap to cut in line: ; in, Allocate time for urgent production tasks, The deadline for urgent production tasks, The processing position corresponding to the production task on the production equipment, is the operating power of the production equipment, is the processing position corresponding to the previous production task, The operating speed of the production equipment; When the queue-jumping gap meets the queue-jumping conditions, the urgent production task is inserted into the task queue of the current production equipment.

[0012] Preferably, the planning of the production task sequence of the same production equipment includes: Check the completion time of each production task on each production equipment; If the completion time of a production task exceeds the threshold, it is determined that the current production equipment has a fault; Send out an alarm message to notify the staff to repair the production equipment.

[0013] A production task scheduling system based on big data, comprising: A production task data acquisition module is used to acquire the production task data to be scheduled; A production equipment data acquisition module is used to acquire processing data of production equipment; A data processing module is used to classify the production tasks to be scheduled according to the equipment used for production; The production task scheduling module is used to plan the production task sequence of the same production equipment.

[0014] An electronic device includes: a chip, a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the chip executes the computer instructions, the electronic device executes a production task scheduling method based on big data.

[0015] The beneficial effects of the present invention are: 1. The present invention sets multiple constraints for multiple production tasks that have no sequence relationship, including resource constraints and cost constraints, and schedules multiple parallel production tasks through the constraints, and also sets penalties for delayed production tasks and rewards for early completion, providing an excellent production task scheduling method for enterprises; 2. The present invention can select the optimal production equipment to perform production tasks based on the energy consumption and utilization rate required for each production equipment to complete the same production task, which can reduce energy consumption and help enterprises save production costs; 3. The present invention can also calculate which production equipment can jump the queue. When encountering sudden emergency production tasks, it calculates which production equipment can meet the conditions for jumping the queue, so as to arrange the sudden emergency production tasks to jump the queue in an orderly manner; 4. The present invention can also intelligently detect whether the production equipment has a fault. When the production task exceeds the preset time, it means that the current production equipment has a fault and cannot complete the production task regularly, so please ask the staff to troubleshoot and repair the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 This is a flow chart of a production task scheduling method based on big data of the present invention; Figure 2 It is a schematic diagram of the production equipment fault detection process of the present invention; Figure 3 This is a schematic diagram of the structure of a production task scheduling system based on big data of the present invention; Figure 4 The figure is a schematic diagram of the hardware structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0020] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0021] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0022] Most of the existing technologies rely on priority to schedule production tasks, without considering the parallel processing of multiple production tasks of the same priority. In the process of scheduling multiple production tasks, the problem model is more complex and the calculation complexity is also higher. At present, there is no accurate scheduling solution to deal with the problem of scheduling multiple production tasks.

[0023] The present invention sets multiple constraints for multiple production tasks that have no sequence relationship, including resource constraints and cost constraints, and schedules multiple parallel production tasks through the constraints, and also sets penalties for delayed production tasks and rewards for early completion, thereby providing an excellent production task scheduling method for enterprises; the present invention can select the optimal production equipment to perform production tasks based on the energy consumption and utilization rate required for each production equipment to complete the same production task, which can reduce energy consumption and help enterprises save production costs; the present invention can also calculate which production equipment can jump the queue, and when encountering sudden emergency production tasks, calculate which production equipment can meet the conditions for jumping the queue, thereby arranging the sudden emergency production tasks to jump the queue in an orderly manner; the present invention can also intelligently detect whether the production equipment has a fault. When the production task exceeds the preset time, it means that the current production equipment has a fault and cannot complete the production task regularly, so the staff is asked to perform troubleshooting and maintenance.

[0024] Example 1 A production task scheduling method based on big data, reference Figure 1 , including the steps of: Step S100, obtaining production task data to be scheduled; Production tasks mainly include fully completing the tasks specified in the production plan, improving the effectiveness and efficiency of the production system, and improving the flexibility of the production system to enhance the adaptability of product production. These tasks ensure that the production process can efficiently and flexibly meet market demand while ensuring product quality and delivery time. Ensure that the products produced meet the needs of users, including variety, quality, quantity and delivery time.

[0025] Step S200, obtaining processing data of production equipment; Production equipment refers to the labor materials or tools that are operated by production workers during the production process and are necessary to directly change the properties, performance, form or enhance the appearance value of raw materials. These equipment include but are not limited to blast furnaces, machine tools, reactors, printing and dyeing machines, etc. Their quality and technological advancement directly affect the quality, precision, output and production efficiency of products. As fixed assets, the value consumption of production equipment in the production process is gradually transferred to its products. In addition, production equipment can be divided into many types according to its function, such as power equipment, conduction equipment, transportation equipment, etc. Production equipment is an indispensable part of industrial production and plays an important role in the normal operation of enterprises and the guarantee of product quality.

[0026] Step S300, classifying the production tasks to be scheduled according to the equipment for production; The selection of production equipment is a comprehensive decision-making process, which requires consideration of multiple factors to ensure technological advancement, economic rationality, and compliance with the current and future development needs of the enterprise. When selecting production equipment, the following basic principles should be followed: Technological advancement: The selected technology should remain advanced, support efficient production, and meet product quality requirements. Economic rationality: While considering technological advancement, the selection of equipment should also take into account cost-effectiveness to ensure a reasonable return on investment. Meet the current and future development needs of the enterprise: The equipment should be able to adapt to the current production scale of the enterprise, and should also have a certain degree of scalability to meet the needs of possible future expansion of production scale or technological upgrading. When selecting production equipment, the following factors are also considered: The degree to which the equipment guarantees product quality: The equipment should be able to stably produce products that meet quality standards. The level of equipment production efficiency: Highly efficient production equipment can improve the overall efficiency of the production line and reduce production costs. The level of energy and raw material consumption of the equipment: Energy-saving and consumption-reducing equipment helps to reduce production costs and is also beneficial to environmental protection. Reliability and durability of the equipment: The stability and durability of the equipment are the key to ensuring long-term stable production. Safety assurance level of equipment operation: The safety performance of the equipment is an important aspect of ensuring the safety of production personnel. Difficulty of equipment maintenance: Equipment that is easy to repair and maintain can reduce downtime and improve production efficiency. Completeness of equipment: A complete set of equipment can reduce the complexity of spare parts and maintenance, and improve production efficiency. Adaptability of equipment to changes in product varieties and operating conditions: The ability to adapt to changes in different products or processes increases production flexibility. Environmental friendliness of equipment: Comply with environmental protection standards and reduce negative impacts on the environment. Economic benefits after the equipment is put into production: Consider the return on investment of the equipment to ensure the economic rationality of the investment decision.

[0027] The selection of production equipment is a complex decision-making process that takes into account multiple factors such as technology, economy, and environmental protection. It needs to be evaluated and selected from multiple angles to ensure the long-term development of the company and the improvement of its market competitiveness. Step S400, planning the production task sequence of the same production equipment.

[0028] The selection of production sequence involves multiple factors and principles to ensure production efficiency, cost control and meet customer needs. Workpiece arrival situation: According to whether all workpieces have arrived or arrive one after another, the production sequence can be divided into static sorting and dynamic sorting. Static sorting is applicable to the situation where all workpieces have arrived, while dynamic sorting is applicable to the situation where workpieces arrive one after another, and the processing sequence needs to be arranged at any time. Machine type and quantity: According to the type and quantity of machines, the production sequence can be divided into single machine sorting and multi-machine sorting. Multi-machine sorting is further subdivided into single-piece workshop sorting and flow workshop sorting, the latter of which requires all workpieces to follow the same processing route. Sorting rules: Including FCFS (First Come First Served) rule, SPT (Shortest Processing Time) rule, SCR (Minimum Critical Ratio) rule, EDD (Earliest Delivery Date) rule, SST (Shortest Slack Time) rule and MWKR (Maximum Unfinished Task) rule, etc. These rules determine the processing order of workpieces according to different optimization goals (such as processing time, delivery date, etc.). Production planning and scheduling: Based on limited production capacity, comprehensive consideration of market, materials, production capacity, process flow, funds, management system, employee behavior and other factors are used to optimize reasonable and effective production plans through APS (Advanced Planning and Scheduling). This includes multiple levels such as master production plan (MPS), schedule planning, and production plan management. Principle conflict handling: When multiple sorting principles conflict, such as the conflict between the delivery priority principle and the key customer priority principle, or the conflict between the delivery principle and the production-favoring principle, it is necessary to handle it flexibly according to the actual situation, giving priority to not affecting the timely delivery of customers, and flexibly adjusting the sorting plan based on experience. Preparation of product production schedule: The principles to be followed include ensuring the variety and production quantity specified in the order contract, reasonably arranging new product trial production tasks, and coordinating the supply time and quantity of raw materials, external components, and outsourced parts to ensure that the production schedule is consistent with the plan. The production sequence needs to consider a variety of factors and principles. Through reasonable sorting rules and flexible processing principles, it is possible to meet customer needs and cost control requirements while ensuring production efficiency and quality.

[0029] Preferably, step S100, obtaining the production task data to be scheduled includes: Generate a production task library to be scheduled. The data of a single production task to be scheduled is represented as follows: ; in, Time for issuing production tasks, Deadline, The production equipment corresponding to the production task, The production process corresponding to the production task.

[0030] Production task data is one of the most core and largest data in the manufacturing process. It includes: Production plan and execution data, production work orders for each workstation, production start time, intermediate pause time, production end time, time consumption information for each processing step, completion volume of the production plan, etc. Converting production task data into a unified form is conducive to improving the efficiency of subsequent data processing.

[0031] Preferably, step S200, obtaining processing data of production equipment includes: Generate a database of production equipment to be processed. The processing data of a single production equipment is expressed as: ; in, Represents the production tasks queued for the nth production process.

[0032] If equipment management is not done well, the hidden cost of production will increase. If a piece of equipment is damaged and not repaired in time, the progress of the entire workshop production will be affected. Production equipment data also includes equipment status and tooling data, equipment power on and off status, alarm data, equipment operating pressure, speed, temperature and other real-time data, as well as the time data corresponding to these equipment status data, tooling mold model, life and other usage information. Converting the processing data of production equipment into a unified form is conducive to improving the efficiency of subsequent data processing.

[0033] Preferably, step S400, planning the production task sequence of the same production equipment includes: Construct a multi-production task constraint model, expressed as: ; Among them, f is the duration of the production task, As a penalty for delay, The amount of resources that the production equipment supplies to the current production task. Indicates the resource demand of the current production task, i represents the i-th production task, and N is a positive integer; When multiple production tasks of the same priority are assigned for scheduling, a multi-production task constraint model is used to plan the production task sequence of the same production equipment.

[0034] Constraint model is a concept involving multiple fields and disciplines, and its definition and application scope vary from field to field. In physics and engineering, constraint model usually refers to a mechanical model, in which ideal constraint is a core concept. An ideal constraint means that the sum of the work done by the force (including constraint reaction force) acting on a particle in any virtual displacement is zero, and this constraint is considered to be an ideal constraint. For example, constraints such as smooth fixed surface constraints, smooth hinges, weightless rigid rods, inextensible flexible cables, fixed hinge supports, rolling supports, and fixed ends are all ideal constraints. The constraint relation model is an extended relational data model, which involves a given constraint relationship between the value ranges of certain attributes of the original database or query results. This shows that in data processing and database design, constraint models are used to ensure the integrity and consistency of data. Constraints are defined as conditions that the solution to the optimization problem needs to meet, which can be equality constraints or inequality constraints. The set of solutions that meet all constraints is called a feasible set or candidate solution. Although the specific applications and forms of expression of "constraint models" are different, they all involve restrictions on certain variables or conditions to ensure the stability and efficient operation of the system or to achieve optimization goals.

[0035] Preferably, step S400, planning the production task sequence of the same production equipment includes: Set the objective function according to the multi-production task scheduling requirements: ; Among them, NCF is the production task Net present value at completion, is the discount rate, CIF is the production task Cash inflow at completion, when production tasks are delayed, i.e. f> , then U=1, otherwise U=0, B is the unit time reward when the production task is completed ahead of schedule, P is the unit time penalty when the project is delayed, the first term in the formula calculates the discounted cash flow for each production task and sums them up, the second term in the formula counts the net present value of each production task, the third term is the reward for multiple production tasks in advance, and the fourth term is the penalty for multiple production tasks being delayed.

[0036] The objective function is one of the evaluation criteria for the quality of an algorithm. It is used to maximize or minimize to find the optimal solution. This is particularly important in machine learning and data science. In machine learning, we usually use optimization algorithms to optimize the objective function and find the optimal model parameters. Gradient descent and its variants (such as stochastic gradient descent) are commonly used optimization algorithms that minimize the objective function by iteratively updating model parameters. The objective function can be a loss function, a cost function, or any function that can measure the difference between the model prediction and the true value. Specifically: Loss function: measures the prediction error of the model on a single sample, and is usually used to calculate the performance of the model on the training data. Cost function: is used to evaluate the overall performance of the model, and is usually adjusted by the optimization algorithm during the training process. The form and specific choice of the objective function depends on the nature of the problem and the specific task to be solved. For example, in neural network training, the objective function is to minimize the cost function by adjusting the network parameters to achieve the purpose of improving the accuracy of the model prediction. The objective function provides a criterion for evaluating model performance and guides the optimization algorithm on how to adjust the model parameters to achieve the best performance. In the embodiment of the present invention, the objective function takes into account the net present value of a single production task and multiple production tasks at the same time, and adds a penalty for delay and a reward for advance for each production task, and has distinct characteristics of multiple production tasks.

[0037] Preferably, step S300, classifying the production tasks to be scheduled according to the equipment for production includes: When there are multiple devices that meet the production requirements of a production task, select one production device to perform the production task: ; in, The utilization rate required for production equipment to perform production tasks, The energy consumption required for production equipment to perform production tasks, and is the weight coefficient of current production equipment utilization and energy consumption, is the i-th production equipment selected.

[0038] Production equipment energy consumption refers to the amount of energy consumed by production equipment during the production process. This includes energy in the form of electricity, natural gas, coal, oil, etc., which are used to drive production equipment to operate, heat, cool, etc. to support production activities. The level of production equipment energy consumption directly affects production costs and the economic benefits of the enterprise. Therefore, reducing production equipment energy consumption and improving energy utilization efficiency are important research directions in industrial production.

[0039] In order to reduce the energy consumption of production equipment, enterprises can take a variety of measures, such as optimizing production processes, improving equipment design, adopting energy-saving technologies, and improving equipment maintenance and management levels. In addition, the government and relevant agencies can also promote enterprises to improve energy efficiency and reduce energy consumption of production equipment by formulating energy-saving standards, providing energy-saving technologies and financial support. When calculating the energy consumption of production equipment, factors such as the rated power of the equipment, the actual operating time, and the energy conversion efficiency are usually taken into account. For example, the number of air conditioners is related to their cooling capacity, and the size of the cooling capacity directly affects the energy consumption of air conditioners. In addition, for industrial enterprises, the energy consumption index per unit product is also an important consideration, which reflects the efficiency of energy utilization of enterprises in the production process. By calculating and analyzing these indicators, enterprises can better understand their own energy consumption status and take corresponding measures to reduce energy consumption.

[0040] The utilization rate of production equipment refers to the percentage of the number of production equipment actually in operation to the number of production equipment available for use. This indicator reflects the utilization status of the registered production equipment available to the enterprise. By calculating the ratio of the number of production equipment actually in operation to the number of production equipment available for use, the value of the utilization rate of production equipment can be obtained. Subtracting this ratio from 1 is the unused rate or idle rate of production equipment. For enterprises with assembly line production processes, the utilization rate of production equipment should be calculated from the ratio of the actual working time of production equipment to the institutional working time, that is, the utilization rate of production equipment is equal to the actual working time of the equipment divided by the institutional working time of the equipment multiplied by 100%, where the working time refers to the accumulated working time within a certain period of time. The utilization rate of production equipment is an important parameter for measuring the production efficiency and technical and economic indicators of an enterprise. It is directly related to the production capacity and resource utilization efficiency of the enterprise.

[0041] Sometimes there are many idle production equipment that can perform the same production task, so how to find the optimal idle production equipment is a technical problem that needs to be solved urgently. In an embodiment of the present invention, by calculating the energy consumption and utilization rate of idle production equipment that perform the same production task to determine which is the optimal production equipment to perform the production task, production costs can be saved for the enterprise.

[0042] Preferably, step S400, planning the production task sequence of the same production equipment includes: If there is an urgent production task, the production task is scheduled manually and the urgent production task is inserted into the current production task scheduling list; Find a gap to cut in line: ; in, Allocate time for urgent production tasks, The deadline for urgent production tasks, The processing position corresponding to the production task on the production equipment, is the operating power of the production equipment, is the processing position corresponding to the previous production task, The operating speed of the production equipment; When the queue-jumping gap meets the queue-jumping conditions, the urgent production task is inserted into the task queue of the current production equipment.

[0043] Emergency production task queue jumping refers to the process of jumping the queue when faced with an urgent production task, that is, skipping the normal queue order and giving priority to the urgent task to ensure that the production progress is not affected. This practice usually occurs on the production line. When an emergency occurs in a certain link, in order to ensure the smooth progress of the entire production process, the urgent task will be handled by jumping the queue. For example, on the production line, if a key component needs to be completed urgently, the production link of this component may be given priority, and the production order of other non-critical components may be temporarily ignored to ensure that the efficiency of the entire production line and product quality are not affected.

[0044] In the production process of an enterprise, temporary urgent production tasks are often encountered. Then, how to insert the temporary urgent production tasks into the original production queue without affecting the completion of the original production tasks is a technical problem that needs to be solved urgently. In an embodiment of the present invention, by setting a queue insertion algorithm, calculating which production equipment meets the queue insertion gap, and then inserting the urgent production tasks into the production equipment, the urgent production tasks can be completed first without affecting the completion of the original production tasks.

[0045] Preferably, step S400, planning the production task sequence of the same production equipment includes: Step S401, detecting the completion time of each production task on each production equipment; Install a timer on the output and input ports of the production equipment. When the production equipment receives a production task, the timer starts timing, and when the output port outputs the completed production task, the timer stops timing. Calculating the time required to complete each production task can not only detect whether the production equipment has a fault, but also evaluate the production capacity of the production equipment and optimize the selection of subsequent production equipment. For example, it takes one hour for production equipment A to perform production task B, and half an hour for production equipment C to perform production task B. Then, it is necessary to adjust different time thresholds according to different production equipment, and comprehensively consider energy consumption and production equipment utilization to select production equipment to perform production tasks.

[0046] Step S402: if the completion time of a production task exceeds a threshold, it is determined that a current production device fails; When the completed production task is not received within the expected time, it means that the production equipment has not completed the production task within the preset time. In this case, it is preliminarily determined that the production equipment has a fault, and intelligent fault detection and repair are performed.

[0047] Step S403: Send out an alarm message to notify the staff to repair the production equipment.

[0048] The methods for judging the failure of production equipment mainly include observing the appearance of the equipment, abnormal functions, and data or information prompts. Observe the appearance of the equipment: By observing the appearance of the equipment, some obvious signs of failure can be found, such as abnormal noise, vibration or smoke from the equipment, which are obvious signals of equipment failure. In addition, you can also visually observe whether the appearance of the equipment is normal, such as checking for leaks, loose pipe joints, pipeline damage, etc., as well as whether the oil level in the oil tank is normal, whether the filter is blocked, and whether the oil is deteriorated. These observations can help to timely discover and judge whether the equipment has a fault. Abnormal function: Check the operating status of the equipment. If some parts of the equipment lose their original accuracy or performance, resulting in the equipment not being able to operate normally, reduced technical performance, or causing the equipment to interrupt production or reduce efficiency, these are all manifestations of equipment failure. For example, if the vibration or noise of the equipment is abnormal, or the working speed or working pressure is abnormal, these may be signs of failure, and the cause needs to be found out and dealt with immediately. ‌Data or information prompts:‌ Some equipment or machines will display the current temperature or voltage values.‌ If these values ​​exceed the normal range,‌ it often means that the equipment may have a fault. ‌ Through these data or information prompts,‌ it is possible to more accurately determine whether the equipment has a fault and the nature and location of the fault. ‌ To determine the failure of production equipment, it is necessary to use a variety of diagnostic methods such as observation method, maintenance manual method, and fault analysis method, and make a comprehensive judgment based on the appearance of the equipment, ‌ functional abnormalities, and data or information prompts. ‌ Timely discovery and handling of equipment failures is crucial to ensure the smooth progress of production.

[0049] Example 2 A production task scheduling system based on big data, reference Figure 3 ,include: A production task data acquisition module is used to acquire the production task data to be scheduled; A production equipment data acquisition module is used to acquire processing data of production equipment; A data processing module is used to classify the production tasks to be scheduled according to the equipment used for production; The production task scheduling module is used to plan the production task sequence of the same production equipment.

[0050] Example 3 An electronic device includes: a chip, a processor and a memory, the memory is used to store computer program code, the computer program code includes computer instructions, and when the chip executes the computer instructions, the electronic device executes a production task scheduling method based on big data.

[0051] refer to Figure 4 , the electronic device 2 includes a processor 21, a memory 22, an input device 23, and an output device 24. The processor 21, the memory 22, the input device 23, and the output device 24 are coupled via a connector, and the connector includes various interfaces, transmission lines, or buses, etc., which are not limited in the embodiments of the present invention. It should be understood that in various embodiments of the present invention, coupling refers to mutual connection in a specific manner, including direct connection or indirect connection through other devices, for example, through various interfaces, transmission lines, buses, etc.

[0052] The processor 21 may be one or more graphics processing units (GPUs). When the processor 21 is a GPU, the GPU may be a single-core GPU or a multi-core GPU. Optionally, the processor 21 may be a processor group consisting of multiple GPUs, and the multiple processors are coupled to each other via one or more buses. Optionally, the processor may also be other types of processors, etc., which are not limited in the embodiments of the present invention.

[0053] The memory 22 can be used to store computer program instructions and various computer program codes including program codes for executing the scheme of the present invention. Optionally, the memory includes but is not limited to random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM), or portable read only memory (CD-ROM), which is used for related instructions and data.

[0054] The input device 23 is used to input data and / or signals, and the output device 24 is used to output data and / or signals. The output device 24 and the input device 23 may be independent devices or an integrated device.

[0055] The present invention sets multiple constraints for multiple production tasks that have no sequence relationship, including resource constraints and cost constraints, and schedules multiple parallel production tasks through the constraints, and also sets penalties for delayed production tasks and rewards for early completion, thereby providing an excellent production task scheduling method for enterprises; the present invention can select the optimal production equipment to perform production tasks based on the energy consumption and utilization rate required for each production equipment to complete the same production task, which can reduce energy consumption and help enterprises save production costs; the present invention can also calculate which production equipment can jump the queue, and when encountering sudden emergency production tasks, calculate which production equipment can meet the conditions for jumping the queue, thereby arranging the sudden emergency production tasks to jump the queue in an orderly manner; the present invention can also intelligently detect whether the production equipment has a fault. When the production task exceeds the preset time, it means that the current production equipment has a fault and cannot complete the production task regularly, so the staff is asked to perform troubleshooting and maintenance.

[0056] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A production task scheduling method based on big data, characterized in that: include: Obtain the production task data to be scheduled; Obtain processing data of production equipment; Classify the production tasks to be scheduled according to the equipment used for production; Plan the production task sequence of the same production equipment.

2. The method for scheduling production tasks based on big data according to claim 1, characterized in that: The obtaining of the production task data to be scheduled includes: Generate a production task library to be scheduled. The data of a single production task to be scheduled is represented as follows: ； in, Time for issuing production tasks, Deadline, The production equipment corresponding to the production task, The production process corresponding to the production task.

3. The method for scheduling production tasks based on big data according to claim 1, characterized in that: The obtaining of processing data of production equipment comprises: Generate a database of production equipment to be processed. The processing data of a single production equipment is expressed as: ； in, Represents the production tasks queued for the nth production process.

4. The method for scheduling production tasks based on big data according to claim 2, characterized in that: The production task sequence of planning the same production equipment includes: Construct a multi-production task constraint model, expressed as: ； Among them, f is the duration of the production task, As a penalty for delay, The amount of resources that the production equipment supplies to the current production task. Indicates the resource demand of the current production task, i represents the i-th production task, and N is a positive integer; When multiple production tasks of the same priority are assigned for scheduling, a multi-production task constraint model is used to plan the production task sequence of the same production equipment.

5. A production task scheduling method based on big data according to claim 4, characterized in that: The production task sequence of planning the same production equipment includes: Set the objective function according to the multi-production task scheduling requirements: ； Among them, NCF is the production task Net present value at completion, is the discount rate, CIF is the production task Cash inflow at completion, when production tasks are delayed, i.e. f> , then U=1, otherwise U=0, B is the unit time reward when the production task is completed ahead of schedule, P is the unit time penalty when the project is delayed, the first term in the formula calculates the discounted cash flow for each production task and sums them up, the second term in the formula counts the net present value of each production task, the third term is the reward for multiple production tasks in advance, and the fourth term is the penalty for multiple production tasks being delayed.

6. The method for scheduling production tasks based on big data according to claim 1, characterized in that: The classification of the production tasks to be scheduled according to the equipment for production includes: When there are multiple devices that meet the production requirements of a production task, select one production device to perform the production task: ； in, The utilization rate required for production equipment to perform production tasks, The energy consumption required for production equipment to perform production tasks, and is the weight coefficient of current production equipment utilization and energy consumption, is the i-th production equipment selected.

7. The method for scheduling production tasks based on big data according to claim 5, characterized in that: The production task sequence of planning the same production equipment includes: If there is an urgent production task, the production task is scheduled manually and the urgent production task is inserted into the current production task scheduling list; Find a gap to cut in line: ； in, Allocate time for urgent production tasks, The deadline for urgent production tasks, The processing position corresponding to the production task on the production equipment, is the operating power of the production equipment, is the processing position corresponding to the previous production task, The operating speed of the production equipment; When the queue-jumping gap meets the queue-jumping conditions, the urgent production task is inserted into the task queue of the current production equipment.

8. The method for scheduling production tasks based on big data according to claim 5, characterized in that: The production task sequence of planning the same production equipment includes: Check the completion time of each production task on each production equipment; If the completion time of a production task exceeds the threshold, it is determined that the current production equipment has a fault; Send out an alarm message to notify the staff to repair the production equipment.

9. A production task scheduling system based on big data, characterized in that: include: A production task data acquisition module is used to acquire the production task data to be scheduled; A production equipment data acquisition module is used to acquire processing data of production equipment; A data processing module is used to classify the production tasks to be scheduled according to the equipment used for production; The production task scheduling module is used to plan the production task sequence of the same production equipment.

10. An electronic device, characterized in that: include: A chip, a processor and a memory, wherein the memory is used to store computer program code, wherein the computer program code includes computer instructions. When the chip executes the computer instructions, the electronic device executes a production task scheduling method based on big data as described in any one of claims 1 to 8.

Images