CN116562566A - MES-based intelligent scheduling system for production workshop - Google Patents
MES-based intelligent scheduling system for production workshop Download PDFInfo
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Abstract
The invention discloses an intelligent scheduling system of a production workshop based on MES, in particular to the technical field of production scheduling, which is used for solving the problems that the existing production situation prediction is inaccurate and the production task cannot be completed in time; the system comprises a data processing module, and an information acquisition module, a sequencing module, a comparison module and a prediction module which are in communication connection with the data processing module; the method comprises the steps of calculating an evaluation coefficient of mechanical processing equipment, accurately judging the state of the mechanical processing equipment, calculating a production efficiency coefficient of the mechanical processing equipment, timely finding out the problem of production efficiency reduction, taking corresponding measures for maintenance and maintenance, and calculating a predicted production time by acquiring historical production time and the production efficiency coefficient, so that a manager can take measures more pertinently to improve the production efficiency, and perform scheduling analysis and resource scheduling to ensure the efficient execution of a production plan and the improvement of product quality.
Description
Technical Field
The invention relates to the technical field of production scheduling, in particular to an intelligent scheduling system of a production workshop based on MES.
Background
The MES system is a production informatization management system facing the workshop execution layer of a manufacturing enterprise, and can provide a solid, reliable, comprehensive and feasible manufacturing collaborative management platform for the enterprise, wherein the management module comprises manufacturing data management, planning scheduling management, production scheduling management, inventory management, quality management, manpower resource management, work center/equipment management, tool management, purchase management, cost management, project billboard management, production process control, bottom data integration analysis, upper layer data integration decomposition and the like for the enterprise.
In the existing machining production workshops, most of the management of planning scheduling management and production scheduling management relies on the actual conditions of the production workshops and experiences of management staff to predict the production conditions of the machining production workshops, and the situation that the production conditions are not predicted accurately enough easily causes low production efficiency and cannot complete production tasks in time.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides an intelligent scheduling system for a MES-based manufacturing shop to solve the above-mentioned problems set forth in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the MES-based intelligent scheduling system for the production workshop comprises a data processing module, and an information acquisition module, a sequencing module, a comparison module and a prediction module which are in communication connection with the data processing module;
the information acquisition module acquires machining equipment information, production raw material information and energy consumption information, sends the machining equipment information to the data processing module, and calculates to obtain a machining equipment evaluation coefficient; the method comprises the steps that a machining equipment evaluation coefficient, production raw material information and energy consumption information are sent to a data processing module, and the data processing module processes and calculates the machining equipment evaluation coefficient, the production raw material information and the energy consumption information to obtain a production efficiency coefficient;
the sequencing module receives the evaluation coefficients of the mechanical processing equipment and sequences the mechanical processing equipment according to the evaluation coefficients of the mechanical processing equipment;
the comparison module compares the machining equipment evaluation coefficient with a critical threshold value of the machining equipment evaluation coefficient, and generates different signals according to the comparison system; comparing the production efficiency coefficient with a critical threshold of the production efficiency coefficient, and generating different signals according to a comparison system;
the prediction module obtains the predicted production time and adjusts the production schedule according to the calculation of the historical production time and the production efficiency coefficient by the data processing module.
In a preferred embodiment, the information acquisition module acquires machining equipment information; the machining equipment information comprises equipment reliability, a lubricating oil pressure deviation value and equipment vibration frequency deviation value;
calculating a machining equipment evaluation coefficient by normalizing the equipment reliability, the lubricating oil pressure deviation value and the equipment vibration frequency deviation value, wherein the expression is as follows:
wherein S is an evaluation coefficient of machining equipment, er, dp and Df are respectively equipment reliability, lubricating oil pressure deviation value and equipment vibration frequency deviation value, and alpha 1 、α 2 、α 3 Preset proportional coefficients of equipment reliability, lubricating oil pressure deviation value and equipment vibration frequency deviation value respectively, and alpha 3 ＞α 1 ＞α 2 ＞0。
In a preferred embodiment, a machining equipment evaluation coefficient critical threshold is set, labeled S 0 The method comprises the steps of carrying out a first treatment on the surface of the When the mechanical processing equipment evaluation coefficient is larger than the mechanical processing equipment evaluation coefficient critical threshold, the system generates an equipment early warning signal, and a manager stops the operation of the equipment in a production workshop according to the equipment early warning signal generated by the system, and a technician is arranged to overhaul the equipment;
when the evaluation coefficient of the mechanical processing equipment is smaller than or equal to the critical threshold value of the evaluation coefficient of the mechanical processing equipment, the system generates an equipment normal signal;
and screening out the machining equipment if the machining equipment evaluation coefficient of the machining equipment is larger than the machining equipment evaluation coefficient critical threshold, and sorting the machining equipment with the machining equipment evaluation coefficient smaller than or equal to the machining equipment evaluation coefficient critical threshold according to the machining equipment evaluation coefficient from small to large.
In a preferred embodiment, the information acquisition module also acquires production raw material information and energy consumption information; the production raw material information comprises a raw material purity value and a raw material surface Ra value; the energy consumption information includes an actual energy consumption ratio;
raw material surface Ra value: the Ra value of the raw material surface is the average roughness of the raw material surface;
calculating a production efficiency coefficient by normalizing a machining equipment evaluation coefficient, a raw material purity value, a raw material surface Ra value and an actual energy consumption ratio, wherein the expression is as follows:
wherein X is the production efficiency coefficient, rv, rs and Ar are the raw material purity value, the raw material surface Ra value and the actual energy consumption ratio, beta 1 、β 2 、β 3 、β 4 Preset proportional coefficients of the machining equipment evaluation coefficient and the machining equipment evaluation coefficient critical threshold, the raw material purity value, the raw material surface Ra value and the actual energy consumption ratio are respectively, and beta 1 ＞β 4 ＞β 3 ＞β 2 ＞0；
Setting a production efficiency coefficient critical threshold, generating an efficiency early warning signal by a system when the production efficiency coefficient is larger than the production efficiency coefficient critical threshold, and generating the efficiency early warning signal by a manager according to the system.
In a preferred embodiment, the historical production time is obtained, the historical production time is corrected by the production efficiency coefficient, and the predicted production time is calculated, wherein the expression is as follows:
t is the predicted production time, H is the historical production time;
if the predicted production time is greater than the historical production time, the system generates a scheduling early warning signal;
if the predicted production time is less than or equal to the historical production time, the system generates a scheduling normal signal.
The intelligent scheduling system for the production workshop based on the MES has the technical effects and advantages that:
1. the state of the machining equipment can be rapidly and accurately judged by calculating the evaluation coefficient of the machining equipment and setting the critical threshold value of the evaluation coefficient of the machining equipment, so that production accidents and losses caused by poor state of the machining equipment are avoided, faults of the machining equipment are timely found and processed, and the high efficiency and the stability of production are ensured; meanwhile, the sorting is carried out according to the evaluation coefficients of the machining equipment, so that the stability and reliability of the performance of the selected machining equipment can be ensured, and the production benefit is improved.
2. The production efficiency coefficient of the machining equipment is calculated by collecting the production raw material information and the energy consumption information, so that the problem of production efficiency reduction can be found in time, and corresponding measures are taken for maintenance.
3. The production time is calculated by acquiring the historical production time and the production efficiency coefficient, so that a production workshop can be helped to better know the production process and the production efficiency, and the production efficiency is improved by taking measures more pertinently by using the predicted production time, and scheduling analysis and resource scheduling are performed to ensure efficient execution of a production plan and improvement of product quality.
Drawings
FIG. 1 is a schematic diagram of a MES-based intelligent scheduling system for manufacturing plants according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
FIG. 1 shows a schematic structure diagram of an intelligent scheduling system of a production plant based on MES, which comprises a data processing module, an information acquisition module, a sequencing module, a comparison module and a prediction module, wherein the information acquisition module, the sequencing module, the comparison module and the prediction module are in communication connection with the data processing module.
The information acquisition module acquires machining equipment information, production raw material information and energy consumption information, sends the machining equipment information to the data processing module, and calculates to obtain a machining equipment evaluation coefficient; and the machining equipment evaluation coefficient, the production raw material information and the energy consumption information are sent to a data processing module, and the data processing module processes and calculates the machining equipment evaluation coefficient, the production raw material information and the energy consumption information to obtain a production efficiency coefficient.
The ranking module receives the machining equipment evaluation coefficients and ranks the machining equipment according to the machining equipment evaluation coefficients.
The comparison module compares the machining equipment evaluation coefficient with a critical threshold value of the machining equipment evaluation coefficient, and generates different signals according to the comparison system; and comparing the production efficiency coefficient with a critical threshold value of the production efficiency coefficient, and generating different signals according to a comparison system.
The prediction module obtains the predicted production time and adjusts the production schedule according to the calculation of the historical production time and the production efficiency coefficient by the data processing module.
Example 2
The information acquisition module acquires machining equipment information.
The information of the machining equipment is obtained to evaluate the actual production efficiency of the machining equipment, and generally in the field of machining, a production workshop generally has a plurality of same machining equipment, and the same machining equipment is analyzed, so that production workshop managers can reasonably arrange production workshop schedules according to different conditions of each machining equipment.
The machining equipment information includes equipment reliability, lubrication oil pressure deviation values, and equipment vibration frequency deviation values.
Device reliability: the reliability of the equipment is the ratio of the historical time to the times of faults of the mechanical processing equipment in the historical time, and the smaller the reliability of the equipment is, the more the times of faults of the mechanical processing equipment are, the lower the reliability of the mechanical processing equipment is, so that the smaller the reliability of the equipment is, the more the normal operation and the production efficiency improvement of the mechanical processing equipment are not facilitated.
Lubricating oil pressure deviation value: the deviation value of the lubricating oil pressure and the optimal lubricating oil pressure is the optimal lubricating oil pressure in machining, and if the lubricating oil pressure is too low, the problems of insufficient lubrication or air invasion and the like of machining equipment can be caused, so that the failure rate of the machining equipment is increased; if the pressure of the lubricating oil is too high, the lubricating oil can enter the gap inside the equipment too much, so that an oil film is broken, the lubricating effect is reduced, and the abrasion and damage of the equipment are increased.
The lubricating oil pressure is obtained by connecting a pressure joint of a sensor (such as a pressure transmitter, a piezoelectric sensor and the like) into a lubricating oil pipeline, converting the pressure into an electric signal through the sensor, and transmitting the electric signal to a control device such as a computer or a PLC (programmable logic controller) for processing.
Device vibration frequency deviation value: the deviation value of the vibration frequency of the equipment refers to the deviation value of the frequency corresponding to the vibration generated by the mechanical processing equipment when the mechanical processing equipment works and the optimal vibration frequency of the equipment, the vibration frequency of the equipment refers to the frequency of the vibration when the mechanical processing equipment works, and due to the specificity of a mechanical structure, certain vibration can occur, and the vibration has certain frequency and amplitude; the optimal equipment vibration frequency refers to the optimal vibration frequency of equipment in a normal running state; when the mechanical vibration frequency is smaller than the vibration frequency of the optimal equipment, the problems of reduced machining precision, reduced machining speed and the like are caused; when the mechanical vibration frequency exceeds the optimal equipment vibration frequency, the mechanical parts of the mechanical processing equipment are unbalanced, eccentric or loose.
Wherein the device vibration frequency is typically measured using a vibration sensor that can be mounted on the body of the machining device or a nearby structure, capable of measuring the amplitude and frequency of the device vibration.
Calculating a machining equipment evaluation coefficient by normalizing the equipment reliability, the lubricating oil pressure deviation value and the equipment vibration frequency deviation value, wherein the expression is as follows:
wherein S is an evaluation coefficient of machining equipment, er, dp and Df are respectively equipment reliability, lubricating oil pressure deviation value and equipment vibration frequency deviation value, and alpha 1 、α 2 、α 3 Preset proportional coefficients of equipment reliability, lubricating oil pressure deviation value and equipment vibration frequency deviation value respectively, and alpha 3 ＞α 1 ＞α 2 ＞0。
Setting a critical threshold value of the evaluation coefficient of the machining equipment, and marking the critical threshold value of the evaluation coefficient of the machining equipment as S 0 The method comprises the steps of carrying out a first treatment on the surface of the When the evaluation coefficient of the mechanical processing equipment is larger than the critical threshold value of the evaluation coefficient of the mechanical processing equipment, the state of the mechanical processing equipment is poor, mechanical faults are easy to occur during mechanical processing, at the moment, equipment early warning signals are generated by a system, a manager stops the operation of the equipment in a production workshop according to the equipment early warning signals generated by the system, and technicians are arranged to overhaul the equipment.
When the evaluation coefficient of the mechanical processing equipment is smaller than or equal to the critical threshold value of the evaluation coefficient of the mechanical processing equipment, the system generates an equipment normal signal, and the mechanical processing equipment can be put into production.
If the machining equipment evaluation coefficient of the machining equipment is larger than the machining equipment evaluation coefficient critical threshold, screening out the machining equipment, and sorting the machining equipment with the machining equipment evaluation coefficient smaller than or equal to the machining equipment evaluation coefficient critical threshold according to the machining equipment evaluation coefficient from small to large, wherein the scheme is assumed to be as follows:
if the production workshop has six identical machining devices, namely A1, A2, A3, A4, A5 and A6, respectively calculating machining device evaluation coefficients of A1, A2, A3, A4, A5 and A6, wherein the machining device evaluation coefficients of A2 are larger than a machining device evaluation coefficient critical threshold value, and the machining device evaluation coefficients of A1, A3, A4, A5 and A6 are smaller than or equal to the machining device evaluation coefficient critical threshold value; and (3) removing the A2, and selecting the A4 and the A1 if two machining equipment are required to be put into production at the moment according to the sizes of the evaluation coefficients of the machining equipment from small to large in sequence, wherein the A4, the A1, the A5, the A6 and the A3 are respectively selected.
It is noted that the production plants referred to in this invention are all machining production plants.
The state of the machining equipment can be rapidly and accurately judged by calculating the evaluation coefficient of the machining equipment and setting the critical threshold value of the evaluation coefficient of the machining equipment, so that production accidents and losses caused by poor state of the machining equipment are avoided, normal operation of the machining equipment is ensured, faults of the machining equipment are timely found and processed, and high efficiency and stability of production are ensured; meanwhile, when the machining equipment put into production is selected, the machining equipment with the machining equipment evaluation coefficient smaller than or equal to the critical threshold value of the machining equipment evaluation coefficient is sequentially selected from small to large, and the machining equipment is ordered according to the magnitude of the machining equipment evaluation coefficient, so that the stability and reliability of the performance of the selected machining equipment can be ensured, the production efficiency is high, and the production benefit is improved.
Example 3
The information acquisition module also acquires production raw material information and energy consumption information.
Wherein the production raw material information includes a raw material purity value and a raw material surface Ra value.
In the invention, the energy consumption information is embodied as an actual energy consumption ratio.
Raw material surface Ra value: the Ra value of the raw material surface is the average roughness of the raw material surface, and represents the average value of the sum of the surface height values perpendicular to the reference line in micrometers (mum) in the detection length; the larger the Ra value of the raw material surface, the rougher the raw material surface; when the Ra value of the raw material surface is larger, this means that the friction force between the machining tool and the raw material surface increases during the machining, resulting in problems such as an increase in cutting force, an increase in cutting temperature, an increase in tool wear, a decrease in workpiece accuracy, and a deterioration in surface quality; therefore, the larger the raw material surface Ra value, the more adversely affects the machining.
Notably, the raw material surface Ra value is calculated by sampling the raw material.
Raw material purity value: the higher the raw material purity value, the more advantageous it is generally for machining; the raw materials with higher purity have more uniform and stable structure and chemical composition and fewer impurities and defects, so that the raw materials can respond to the acting force and temperature change of mechanical processing more consistently, thereby being beneficial to improving the processing precision and efficiency and reducing faults and loss in the processing process; the purity value of the raw material is measured by a chemical analysis method, wherein a common chemical analysis method includes atomic absorption spectrum, fluorescence spectrum, inductively coupled plasma emission spectrum, etc., and a common value representing the purity value of the raw material is a percentage, such as 80.99%.
Notably, the raw material purity value is calculated by sampling the raw material.
Actual energy consumption ratio: the actual energy consumption ratio is the ratio of actual energy consumption to theoretical energy consumption, and when the actual energy consumption of the machining equipment is larger than the theoretical energy consumption, the problems of performance reduction, damage, aging and the like of the machining equipment possibly exist, and the machining equipment needs to be timely maintained, maintained and replaced.
The larger the actual energy consumption ratio is, the more energy is consumed by the machining equipment in the production process, and accordingly the production cost is increased, so that the economic benefit of enterprises is directly affected, in addition, the energy consumption is large, the waste of energy resources is caused, and the environment is adversely affected.
If the actual energy consumption ratio of the mechanical processing equipment is smaller, the mechanical processing equipment consumes less energy in the production process, so that the production cost of enterprises can be reduced, the waste of energy resources can be reduced, and the environment is positively influenced. Meanwhile, low-energy-consumption equipment also generally shows better energy utilization efficiency and production efficiency, and can improve the competitiveness and market share of enterprises.
Determining theoretical energy consumption: calculating theoretical energy consumption of the machining equipment according to rated power and running time of the machining equipment; for example, a machining apparatus rated for 5 kw and operating for 10 hours would have a theoretical power consumption of 50 kwh.
Determining actual energy consumption: the actual energy consumption comprises, but is not limited to, calculating the actual energy consumption of the mechanical processing equipment by measuring parameters such as electricity consumption, gas consumption or water consumption of the mechanical processing equipment; for example, a machining apparatus actually consumes 55 kilowatt-hours of electrical energy in 10 hours, and then its actual energy consumption is 55 kilowatt-hours.
The raw material purity value, the raw material surface Ra value and the actual energy consumption ratio have strong influence on the actual production of a production workshop, and the raw material purity value, the raw material surface Ra value and the actual energy consumption ratio are combined with the evaluation coefficient of the mechanical processing equipment so as to better realize the actual production condition of the production workshop.
Calculating a production efficiency coefficient by normalizing a machining equipment evaluation coefficient, a raw material purity value, a raw material surface Ra value and an actual energy consumption ratio, wherein the expression is as follows:
wherein X is the production efficiency coefficient, rv, rs and Ar are the raw material purity value, the raw material surface Ra value and the actual energy consumption ratio, beta 1 、β 2 、β 3 、β 4 Preset proportional coefficients of the machining equipment evaluation coefficient and the machining equipment evaluation coefficient critical threshold, the raw material purity value, the raw material surface Ra value and the actual energy consumption ratio are respectively, and beta 1 ＞β 4 ＞β 3 ＞β 2 ＞0。
The production efficiency coefficient expresses the actual production efficiency condition of the machining equipment, and the larger the production efficiency coefficient is, the lower the production efficiency of the machining equipment is, the more likely the problems of delivery delay and the like caused by untimely production and machining are caused.
Setting a critical threshold of the production efficiency coefficient, when the production efficiency coefficient is larger than the critical threshold of the production efficiency coefficient, the production efficiency of the mechanical processing equipment is lower at the moment, and the production of a normal production workshop is influenced, generating an efficiency early warning signal by a system, and moderately distributing tasks of the mechanical processing equipment to other idle mechanical processing equipment by a manager according to the efficiency early warning signal generated by the system so as to ensure that the production tasks are finished on time and in quantity to evaluate raw materials; ensuring the qualification of raw materials, checking machining equipment, finding out reasons for the reduction of production efficiency, such as equipment abrasion, loosening of parts and poor lubrication, and carrying out equipment maintenance: the mechanical processing equipment is maintained, such as replacement of worn parts, enhanced lubrication and maintenance, and the like, so that the performance and the service life of the mechanical processing equipment are improved, and the stable and efficient operation of the mechanical processing equipment is ensured.
When the production efficiency coefficient is smaller than or equal to the production efficiency coefficient critical threshold, the production efficiency of the mechanical processing equipment is normal, the system does not need to send out signals, and management staff does not need to take measures.
The production efficiency coefficient of the machining equipment is calculated by collecting and normalizing the production raw material information and the energy consumption information, the problem of production efficiency reduction can be found timely, corresponding measures are taken for maintenance, the machining equipment can be guaranteed to finish production tasks on time according to quantity, the production quality is improved, the performance and the service life of the machining equipment are improved, the stable and efficient operation of the machining equipment is guaranteed, and therefore the production efficiency is improved.
Example 4
And acquiring historical production time, analyzing the historical production time and the production efficiency coefficient, and performing scheduling analysis on a production workshop.
The historical production time is the time required by the equipment for processing quantitative certain processed products, the historical production time is corrected through the production efficiency coefficient, the predicted production time is obtained through calculation, and the expression is as follows:
wherein T is the predicted production time, and H is the historical production time.
To better explain the calculated predicted production time, an example is illustrated:
setting the historical production time to be 2.4 hours, and calculating the production efficiency coefficient to be 1.6452I.e. the predicted production time was 2.73h.
Setting the historical production time to be 2.4 hours, and calculating the production efficiency coefficient to be 1.0315I.e. the predicted production time was 2.42h.
If the predicted production time is greater than the historical production time, the system generates a scheduling early warning signal, and the manager takes the following measures:
the personnel management of the production workshop is enhanced, the personnel is ensured to complete the work task in a reasonable time, and the production progress lag caused by improper personnel scheduling is avoided.
Ensure the supply and quality of the production raw materials and avoid the blockage of the production progress caused by untimely raw material supply or unqualified raw material quality.
The maintenance and the maintenance of the equipment are enhanced, the machining equipment can be ensured to stably and efficiently run, and the production progress is prevented from being blocked due to equipment faults.
And the production schedule is adjusted, the production tasks and the production time are reasonably arranged, and the production progress of a production workshop can be ensured to be completed on time.
If the predicted production time is less than or equal to the historical production time, the system generates a normal scheduling signal, and then a manager can take measures for increasing the task amount of the machining equipment according to the predicted production time, so that the production efficiency of a production workshop is improved, meanwhile, the management and the monitoring of the production workshop are also enhanced, problems and barriers in the production process are timely found and treated, and the production progress of the production workshop is ensured to be completed on time.
The production efficiency is improved by taking measures more pertinently by using the predicted production time, so that a manager can carry out scheduling analysis and resource scheduling to ensure efficient execution of a production plan and improvement of product quality, and a scheduling early warning signal is generated when the predicted production time is greater than the historical production time, so that the manager can take corresponding measures in time to avoid production delay; by improving the production efficiency and optimizing the production schedule, enterprises can improve the production benefits and quality, improve the enterprise competitiveness and obtain more market shares.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and module may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (5)
1. The MES-based intelligent scheduling system for the production workshop is characterized by comprising a data processing module, and an information acquisition module, a sequencing module, a comparison module and a prediction module which are in communication connection with the data processing module;
the information acquisition module acquires machining equipment information, production raw material information and energy consumption information, sends the machining equipment information to the data processing module, and calculates to obtain a machining equipment evaluation coefficient; the method comprises the steps that a machining equipment evaluation coefficient, production raw material information and energy consumption information are sent to a data processing module, and the data processing module processes and calculates the machining equipment evaluation coefficient, the production raw material information and the energy consumption information to obtain a production efficiency coefficient;
the sequencing module receives the evaluation coefficients of the mechanical processing equipment and sequences the mechanical processing equipment according to the evaluation coefficients of the mechanical processing equipment;
the comparison module compares the machining equipment evaluation coefficient with a critical threshold value of the machining equipment evaluation coefficient, and generates different signals according to the comparison system; comparing the production efficiency coefficient with a critical threshold of the production efficiency coefficient, and generating different signals according to a comparison system;
the prediction module obtains the predicted production time and adjusts the production schedule according to the calculation of the historical production time and the production efficiency coefficient by the data processing module.
2. The MES-based intelligent scheduling system for manufacturing plants as set forth in claim 1, wherein: the information acquisition module acquires information of machining equipment; the machining equipment information comprises equipment reliability, a lubricating oil pressure deviation value and equipment vibration frequency deviation value;
calculating a machining equipment evaluation coefficient by normalizing the equipment reliability, the lubricating oil pressure deviation value and the equipment vibration frequency deviation value, wherein the expression is as follows:
wherein S is an evaluation coefficient of machining equipment, er, dp and Df are respectively equipment reliability, lubricating oil pressure deviation value and equipment vibration frequency deviation value, and alpha 1 、α 2 、α 3 Preset proportional coefficients of equipment reliability, lubricating oil pressure deviation value and equipment vibration frequency deviation value respectively, and alpha 3 ＞α 1 ＞α 2 ＞0。
3. The MES-based intelligent scheduling system for manufacturing plants as set forth in claim 2, wherein: setting a critical threshold value of the evaluation coefficient of the machining equipment, and marking the critical threshold value of the evaluation coefficient of the machining equipment as S 0 The method comprises the steps of carrying out a first treatment on the surface of the When the mechanical processing equipment evaluation coefficient is larger than the mechanical processing equipment evaluation coefficient critical threshold, the system generates an equipment early warning signal, and a manager stops the operation of the equipment in a production workshop according to the equipment early warning signal generated by the system, and a technician is arranged to overhaul the equipment;
when the evaluation coefficient of the mechanical processing equipment is smaller than or equal to the critical threshold value of the evaluation coefficient of the mechanical processing equipment, the system generates an equipment normal signal;
and screening out the machining equipment if the machining equipment evaluation coefficient of the machining equipment is larger than the machining equipment evaluation coefficient critical threshold, and sorting the machining equipment with the machining equipment evaluation coefficient smaller than or equal to the machining equipment evaluation coefficient critical threshold according to the machining equipment evaluation coefficient from small to large.
4. A MES-based intelligent scheduling system for a production facility as claimed in claim 3 wherein: the information acquisition module also acquires production raw material information and energy consumption information; the production raw material information comprises a raw material purity value and a raw material surface Ra value; the energy consumption information includes an actual energy consumption ratio;
raw material surface Ra value: the Ra value of the raw material surface is the average roughness of the raw material surface;
calculating a production efficiency coefficient by normalizing a machining equipment evaluation coefficient, a raw material purity value, a raw material surface Ra value and an actual energy consumption ratio, wherein the expression is as follows:
wherein X is the production efficiency coefficient, rv, rs and Ar are the raw material purity value, the raw material surface Ra value and the actual energy consumption ratio, beta 1 、β 2 、β 3 、β 4 Respectively machining equipmentPreset ratio of evaluation coefficient to machining equipment evaluation coefficient critical threshold value, raw material purity value, raw material surface Ra value, and actual energy consumption ratio, and beta 1 ＞β 4 ＞β 3 ＞β 2 ＞0；
Setting a production efficiency coefficient critical threshold, generating an efficiency early warning signal by a system when the production efficiency coefficient is larger than the production efficiency coefficient critical threshold, and generating the efficiency early warning signal by a manager according to the system.
5. The MES-based intelligent scheduling system for manufacturing plants as set forth in claim 4, wherein: acquiring historical production time, correcting the historical production time through a production efficiency coefficient, and calculating and predicting the production time, wherein the expression is as follows:
t is the predicted production time, H is the historical production time;
if the predicted production time is greater than the historical production time, the system generates a scheduling early warning signal;
if the predicted production time is less than or equal to the historical production time, the system generates a scheduling normal signal.
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| CN117057630A (en) * | 2023-10-10 | 2023-11-14 | 山东丰香园食品股份有限公司 | Intelligent management method and system for production line of food packaging workshop |
| CN117092974A (en) * | 2023-09-12 | 2023-11-21 | 温州天瑞化纤有限公司 | Intelligent switch control system for spunbonded non-woven fabric workshop |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117092974A (en) * | 2023-09-12 | 2023-11-21 | 温州天瑞化纤有限公司 | Intelligent switch control system for spunbonded non-woven fabric workshop |
| CN117057630A (en) * | 2023-10-10 | 2023-11-14 | 山东丰香园食品股份有限公司 | Intelligent management method and system for production line of food packaging workshop |
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