CN115879757A - Management method based on MES intelligent manufacturing and related equipment - Google Patents
Management method based on MES intelligent manufacturing and related equipment Download PDFInfo
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- CN115879757A CN115879757A CN202211155063.5A CN202211155063A CN115879757A CN 115879757 A CN115879757 A CN 115879757A CN 202211155063 A CN202211155063 A CN 202211155063A CN 115879757 A CN115879757 A CN 115879757A
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Abstract
The application provides a management method based on MES intelligent manufacturing and related equipment. In the method, the server acquires the target product information based on the preset acquisition frequency and the acquisition type; processing the operation data of the industrial equipment and outputting a target parameter set; processing according to the target parameter set, and outputting an evaluation result of a target product; processing the operation of the staff according to the evaluation result, and outputting the operation evaluation result of the staff; determining a processing priority corresponding to the abnormal information according to the abnormal information of the target product; determining a processing terminal for processing the abnormal information according to the processing priority; generating abnormal state reminding information based on the abnormal information; and sending abnormal state reminding information to the processing terminal. The production and equipment state information of the industrial equipment is observed in real time through continuous monitoring and intelligent analysis on the industrial equipment, and managers at all levels are assisted to discover the abnormal state of the equipment in time.
Description
Technical Field
The disclosure relates to the technical field of data processing, in particular to a management method based on MES intelligent manufacturing and related equipment.
Background
The manufacturing industry is used as the national pillar industry and keeps a good development situation all the time, but with the increase of labor cost, the traditional manufacturing industry is changing continuously, and the production mode of combining intelligent manufacturing and manpower is colliding and changing continuously.
The MES management System management Execution System is a System for collecting and integrating various production information of a factory site, and provides a set of solution for collecting, integrating and analyzing factory Manufacturing information. The MES has obvious effect of improving production efficiency in the fields of semiconductors, electronics and the like. In the aspect of enterprise production management, the MES management system has the characteristics of intelligent management, informatization, traceable source and the like, is very effective in monitoring the product quality in the production process, can obviously improve the yield of products and eliminate uncontrollable factors in the production process, and can complete the production of the products with high efficiency and high quality to the maximum extent. Enterprises in different product fields actively research MES management systems suitable for the products and apply the MES management systems to the products so as to improve the production efficiency.
However, in the prior art, the MES system only collects data of each part in the process of the flow operation, and cannot perform all-around management, and cannot classify, process and report various kinds of abnormal information to the user, so that the overall efficiency is reduced, and the operation and management by the user are inconvenient. In addition, the traditional MES system cannot meet the higher and higher use requirements of modern industry, and the equipments cannot be effectively coordinated with each other, and the equipments in the production process are not effectively monitored and managed.
In the process of production and manufacturing in the prior art, the technical problems that production management information circulation is not timely, station information cannot be deeply analyzed in real time, and intelligent production management is carried out exist.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The application aims to provide a management method based on MES intelligent manufacturing and related equipment, which at least overcome the problems in the prior art to a certain extent, and can conveniently find the abnormal state of the equipment in time by classifying and processing abnormal information occurring in the production process, thereby achieving the purpose of improving the production efficiency of a factory and the adaptability of an MES system.
Additional features and advantages of the present application will be set forth in the detailed description which follows, or may be learned by practice of the invention.
According to one aspect of the application, a management method based on MES intelligent manufacturing is provided, which comprises the following steps: acquiring target product information based on a preset acquisition frequency and an acquisition type, wherein the target product information comprises operation data of corresponding industrial equipment; processing the operation data of the industrial equipment and outputting a target parameter set; processing according to the target parameter set, and outputting an evaluation result of a target product, wherein the evaluation result of the target product comprises abnormal information of the target product; processing the operation of the staff according to the evaluation result, and outputting the operation evaluation result of the staff; determining a processing priority corresponding to the abnormal information according to the abnormal information of the target product; determining a processing terminal for processing the abnormal information according to the processing priority; generating abnormal state reminding information based on the abnormal information; and sending abnormal state reminding information to the processing terminal, wherein the abnormal state reminding information comprises the abnormal information of the target product, the priority of the abnormal information of the target product and the operation evaluation result of the worker.
In an embodiment of the application, the processing the operation data of the industrial equipment and outputting a target parameter set includes: acquiring early warning data; obtaining classification criteria based on the early warning data; preprocessing the operation data based on the classification standard, and outputting a plurality of classes of classification data; respectively processing the classified data and respectively outputting production state data corresponding to the classified data; and taking a plurality of production state data as a target parameter set.
In an embodiment of the present application, the acquiring early warning data includes: acquiring task information and task timeliness information; and generating early warning data based on the task information and the task timeliness information.
In an embodiment of the application, the processing according to the target parameter set and outputting an evaluation result of a target product includes: constructing a product operation characteristic set according to the target product information; constructing a product operation abnormity detection model based on the product operation characteristic set; performing feature traversal matching on the target image set based on the product operation abnormity detection model to obtain a first feature traversal matching result; and obtaining an operation evaluation result of the target product according to the first feature traversal matching result.
In an embodiment of the application, the processing the operation of the worker according to the evaluation result, and outputting the operation evaluation result of the worker include: acquiring an operation record of a corresponding operator according to the operation data; and processing the operation record to obtain the working efficiency data of the working personnel.
In an embodiment of the present application, the determining, according to the exception information of the target product, a processing priority corresponding to the exception information includes: processing the abnormal information of the target product and outputting the abnormal type of the abnormal information; and outputting corresponding processing priority based on the exception type of the exception information.
In an embodiment of the application, the generating abnormal state reminding information based on the abnormal state data further includes: issuing an alert if at least one of the following is detected as abnormal: real-time energy consumption data of the industrial equipment exceeds a first proportion of a first threshold; a second proportion that the total amount of energy consumption data of the industrial equipment in the first time period exceeds a second threshold value; the energy consumption data of the industrial equipment is continuously increased in the second time period; the energy consumption data of the industrial equipment in the third time period is continuously reduced; the industrial device has energy consumption data fluctuating beyond a predetermined range during a fourth time period.
In another aspect of the present application, an apparatus for MES-based smart manufacturing, comprising: the system comprises a receiving module, a processing module and a display module, wherein the receiving module is configured to acquire target product information based on a preset acquisition frequency and an acquisition type, and the target product information comprises operation data of corresponding industrial equipment; the processing module is configured to process the operation data of the industrial equipment and output a target parameter set; processing according to the target parameter set, and outputting an evaluation result of a target product, wherein the evaluation result of the target product comprises abnormal information of the target product; processing the operation of the staff according to the evaluation result, and outputting the operation evaluation result of the staff; determining a processing priority corresponding to the abnormal information according to the abnormal information of the target product; determining a processing terminal for processing the abnormal information according to the processing priority; generating abnormal state reminding information based on the abnormal information; the sending module is configured to send abnormal state reminding information to the processing terminal, wherein the abnormal state reminding information comprises the abnormal information of the target product, the priority of the abnormal information of the target product and the operation evaluation result of the worker.
According to still another aspect of the present application, an electronic apparatus, characterized by comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute, via execution of the executable instructions, a management method that implements MES-based intelligent manufacturing as described above.
According to a further aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the above-mentioned MES intelligent manufacturing based management method.
According to yet another aspect of the present application, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned MES intelligence manufacturing based management method.
The application provides a management method based on MES intelligent manufacturing, which comprises the following steps: acquiring target product information based on a preset acquisition frequency and an acquisition type, wherein the target product information comprises operation data of corresponding industrial equipment; processing the operation data of the industrial equipment and outputting a target parameter set; processing according to the target parameter set, and outputting an evaluation result of a target product, wherein the evaluation result of the target product comprises abnormal information of the target product; processing the operation of the staff according to the evaluation result, and outputting the operation evaluation result of the staff; determining a processing priority corresponding to the abnormal information according to the abnormal information of the target product; determining a processing terminal for processing the abnormal information according to the processing priority; generating abnormal state reminding information based on the abnormal information; and sending abnormal state reminding information to the processing terminal, wherein the abnormal state reminding information comprises the abnormal information of the target product, the priority of the abnormal information of the target product and the operation evaluation result of the worker. By continuously monitoring and intelligently analyzing the operation data, the production, quality, energy consumption and equipment state information of a factory are observed in real time, a multistage exception handling mechanism is established, and exception information occurring in the production process is classified and handled, so that the exception state of the equipment can be conveniently found in time, and field exception fluctuation can be responded in time, thus the production efficiency is improved, the advance prevention of production work is realized, and the unplanned shutdown is avoided by in-process control. In addition, the industrial Internet of things data is visualized, the state of a production field is conveniently observed, and management personnel at all levels are assisted to make correct decisions.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure. It should be apparent that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived by those of ordinary skill in the art without inventive effort.
FIG. 1 is a flow chart illustrating a MES-based intelligent manufacturing management method according to an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating a MES-based intelligent manufacturing apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 4 is a schematic diagram of a storage medium provided in an embodiment of the present application.
Detailed Description
Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
In addition, technical solutions in the embodiments of the present application may be combined with each other, but it is necessary to be based on the realization of the technical solutions by a person skilled in the art, and when the technical solutions are contradictory to each other or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope claimed in the present application.
It is noted that other embodiments of the present application will become readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise construction and arrangements of parts which have been described below and illustrated in the accompanying drawings, and that various modifications and changes can be made therein without departing from the scope thereof. The scope of the application is limited only by the appended claims.
A management method based on MES intelligent manufacturing according to an exemplary embodiment of the present application is described below with reference to fig. 1. It should be noted that the following application scenarios are merely illustrated for the convenience of understanding the spirit and principles of the present application, and the embodiments of the present application are not limited in this respect. Rather, embodiments of the present application may be applied to any scenario where applicable.
In one embodiment, the application further provides a management method based on MES intelligent manufacturing. Fig. 1 schematically shows a flow diagram of a management method based on MES intelligent manufacturing according to an embodiment of the present application. As shown in fig. 1, the method is applied to a server, and includes:
s101, target product information is obtained based on a preset collection frequency and a collection type, wherein the target product information comprises operation data of corresponding industrial equipment.
In one embodiment, the preset collection frequency may be set according to actual conditions, for example, different collection frequencies are set according to different production lines, including but not limited to collecting data of yarn changing every 5 minutes, collecting work data of industrial equipment every hour, and the like. The collection type is determined based on a 4M1E method and comprises a personnel type, an equipment type, a material type, a method type and an environment type. Person (Man): the system comprises designers, technologists, assemblers, detection personnel and the like, and the data of education degree, professional skill level, physical health condition, psychological diathesis condition and the like of the personnel. Device (Machine): the service life, operation condition, maintenance condition, failure frequency and other data of the equipment can be used as the assembly data content of statistical analysis. Materials (Material): the manufacturing precision, material, overall dimension and other data of the material are all used as basic data content of assembly. Process (Method): the technological method comprises data such as shoe garment matching technological plans, shoe garment detection technological plans and the like. Environment (Environment) environmental factors at the assembly site, such as humidity, temperature, lighting, air pressure, noise, vibration, etc., may contribute to personnel, equipment, materials, etc. data.
In another embodiment, the industrial equipment includes, but is not limited to, industrial production equipment and various machine tools, such as lathes, milling machines, grinding machines, planing machines, and the like. Through set up collection system in advance on industrial equipment, and then acquire industrial equipment's various operational data, wherein, collection system includes but not limited to the equipment that can gather factory production data such as camera, binocular camera, flowmeter, pressure gauge, galvanometer, sensor. The server receives various operation data sent by the industrial equipment, wherein the operation data comprises but is not limited to the running state of the industrial equipment, the rotating speed of the industrial equipment, the starting-up time, the shutdown times and the like of the industrial equipment. The server monitors the working state of the equipment by acquiring the data, so that managers can master the operation condition of the warp knitting machine in real time.
And S102, processing the operation data of the industrial equipment and outputting a target parameter set.
In one embodiment, a server obtains early warning data; obtaining a classification standard based on the early warning data; preprocessing the operation data based on the classification standard, and outputting a plurality of classes of classification data; respectively processing the classified data and respectively outputting production state data corresponding to the classified data; and taking a plurality of production state data as a target parameter set.
In another embodiment, the server outputs an early warning standard rule according to the early warning data, classifies the operation data of the industrial equipment according to a preset early warning standard rule, calculates each type of data to obtain production state data corresponding to each type of data, and obtains a plurality of target parameters of the production process of the industrial equipment. The target parameters include, but are not limited to, a factory area name, a machine number, a machine type, a current all machine state, a time utilization rate, a performance utilization rate, a qualification rate, a product name, an OEE value, a machine repair rate, a shutdown type time ratio, inefficient machine early warning information and the like.
S103, processing is carried out according to the target parameter set, and an evaluation result of the target product is output, wherein the evaluation result of the target product comprises abnormal information of the target product.
In one embodiment, the server constructs a product operation characteristic set according to the target product information; constructing a product operation abnormity detection model based on the product operation characteristic set; performing feature traversal matching on a target image set based on a product operation abnormity detection model to obtain a first feature traversal matching result; and traversing the matching result according to the first characteristics to obtain an operation evaluation result of the target product.
In another embodiment, when the equipment information, the quality information, the material information, the process information and the like do not meet the preset conditions, the server identifies the corresponding production attribute information as abnormal information. The server can monitor and collect information such as the running state, abnormal quality data, pressure, rotating speed and the like of the equipment; manual work can report information such as point inspection data, material shortage, material quality problems and the like through an MES system; meanwhile, the MES system can automatically report and collect quality information and material information according to the set abnormal judgment rule.
And S104, processing the operation of the staff according to the evaluation result, and outputting the operation evaluation result of the staff.
In one embodiment, the server acquires the corresponding operation records of the operators according to the operation data; and processing the operation records to obtain the working efficiency data of the working personnel. The server checks the working efficiency value of each employee in the period, evaluates, analyzes and reports the working efficiency of the employees according to the working efficiency value of the employees in the period, realizes transparentization and publicity of working contents, working links and working methods, is convenient for managers to know the working efficiency of each employee in time, and is convenient for arrangement of subsequent work.
In another embodiment, the server analyzes the overall production status information to determine whether the whole production process has a deviation from an expected target, for example, the production progress is lower than the expected progress, the failure rate of the production equipment is high, the failure rate of the staff operation is high, and the like. The server corresponds to the operation information of the abnormal production state according to the reminding information of the abnormal state, for example, if the failure rate of the production equipment is high, the failed production equipment is maintained in time; if the error rate of the operation of the staff is high, the training of the staff should be strengthened so that the staff can be familiar with the operation flow of the post as soon as possible.
S105, determining a processing priority corresponding to the abnormal information according to the abnormal information of the target product.
In one embodiment, the server processes the abnormal information of the target product and outputs the abnormal type of the abnormal information; and outputting the corresponding processing priority based on the exception type of the exception information. The priority of the abnormal information can be three abnormal levels of atmosphere, namely a first-level abnormality, a second-level abnormality and a third-level abnormality, wherein the first-level abnormality is a production fluctuation abnormality, the second-level abnormality is a production efficiency abnormality, and the third-level abnormality is an equipment fault, a batch quality problem and a major accident abnormality.
In another embodiment, when the collected production attribute information is identified as abnormal information, the abnormal information is sent to a display device which can be observed by a user, such as a mobile phone of a related person in charge, a workshop display signboard, a production operation center screen and the like, so that the user can respond to the field abnormal problem in time to carry out the next processing.
And S106, determining a processing terminal for processing the abnormal information according to the processing priority.
In one embodiment, each level of priority of the abnormal information corresponds to one or more receiving account numbers, and in actual production, a direct person in charge of production work, a procedure or a department associated with upstream and downstream, a leader of a superior level, a leader of a company and the like all have different receiving account numbers. The primary exception is a production fluctuation exception, has small influence on the whole production, and usually only informs a direct responsible person of the production work; the second-level abnormity relates to production efficiency reduction abnormity, such as untimely material distribution and the like, which affects the production progress, and besides the direct responsible person of the production work needs to be informed, the processes or departments and superior leaders related to the upstream and downstream affected by the abnormity need to be informed; the third-level anomaly relates to equipment failure, batch quality problems and major accidents, has great influence on production safety, finished product quality and the like, and can cause great loss to the whole production work, so the third-level anomaly needs to inform a production work direct responsible person, a process or a department related to upstream and downstream, a superior leader and even a company leader. Aiming at the second-level and third-level abnormalities, the upstream and downstream linkage response processing abnormalities are more efficient and timely by informing a direct person in charge of production work and associated processes or departments of the upstream and downstream.
And S107, generating abnormal state reminding information based on the abnormal information.
In one embodiment, the server issues an alert if it detects an anomaly in at least one of: the real-time energy consumption data of the industrial equipment exceeds a first proportion of a first threshold; a second proportion that the total amount of energy consumption data of the industrial equipment in the first time period exceeds a second threshold value; the energy consumption data of the industrial equipment is continuously increased in the second time period; the energy consumption data of the industrial equipment in the third time period is continuously reduced; the industrial device has energy consumption data fluctuating beyond a predetermined range during a fourth time period.
In another embodiment, the early warning personnel can configure (associate the nail ID) on the server in advance, and the timeout time can be configured; and (4) early warning notification in overtime level (for example, if the upper shaft is overtime for 5 minutes, a first-level responder is triggered, the upper shaft group length is informed by nailing, if the upper shaft is overtime for 20 minutes, a second-level responder is triggered, and the factory length is informed by nailing). By sending the abnormal state reminding information and the abnormal state operation information to corresponding managers, the corresponding managers check corresponding machine halt details, so that the managers can know factors generated by current abnormal data, and the abnormal problem is avoided or solved.
In another embodiment, the server analyzes the overall production status information to determine whether something deviating from the expected goal occurs in the whole production process, such as a production schedule lower than the expected schedule, a high failure rate of production equipment, a high failure rate of staff operation, etc. The server corresponds to the operation information of the abnormal production state according to the reminding information of the abnormal state, for example, if the failure rate of the production equipment is high, the failed production equipment is maintained in time; if the error rate of the operation of the staff is high, the training of the staff should be strengthened so that the staff can become familiar with the operation flow of the post and the like as soon as possible.
And S108, sending abnormal state reminding information to the processing terminal, wherein the abnormal state reminding information comprises the abnormal information of the target product, the priority of the abnormal information of the target product and the operation evaluation result of the worker.
In one implementation mode, the abnormal information of the target product comprises the use environment of industrial equipment and the like, when the production attribute information meets preset conditions, the abnormal information is marked as the abnormal information and is sent to display equipment, the priority of the abnormal information is determined according to a preset correspondence table in an MES (manufacturing execution system), the abnormal information is sent to a corresponding receiving account according to the priority of the abnormal information, and through an established multi-stage abnormal processing mechanism, the abnormal information can be classified and processed, the field abnormal fluctuation can be responded in time, so that the production efficiency is improved, and the beforehand prevention and the in-process control of production work are realized.
This application is gathered the key data of factory production operation in-process through thing networking device, guarantees data acquisition's promptness and accuracy, through network transmission, guarantees data transmission's stability and promptness, through carrying out the visualization with industry thing networking data, conveniently observes the state of production scene, and the management personnel of supplementary at different levels make the correct decision.
The method comprises the steps that target product information is obtained by a server based on a preset acquisition frequency and an acquisition type, wherein the target product information comprises operation data of corresponding industrial equipment; processing the operation data of the industrial equipment and outputting a target parameter set; processing according to the target parameter set, and outputting an evaluation result of a target product, wherein the evaluation result of the target product comprises abnormal information of the target product; processing the operation of the staff according to the evaluation result, and outputting the operation evaluation result of the staff; determining a processing priority corresponding to the abnormal information according to the abnormal information of the target product; determining a processing terminal for processing the abnormal information according to the processing priority; generating abnormal state reminding information based on the abnormal information; and sending abnormal state reminding information to the processing terminal, wherein the abnormal state reminding information comprises the abnormal information of the target product, the priority of the abnormal information of the target product and the operation evaluation result of the worker. By continuously monitoring and intelligently analyzing the operation data, the production, quality, energy consumption and equipment state information of a factory are observed in real time, a multistage exception handling mechanism is established, and exception information occurring in the production process is classified and handled, so that the exception state of the equipment can be conveniently found in time, and field exception fluctuation can be responded in time, thus the production efficiency is improved, the advance prevention of production work is realized, and the unplanned shutdown is avoided by in-process control. In addition, the industrial Internet of things data is visualized, the state of a production field is conveniently observed, and management personnel at all levels are assisted to make correct decisions.
Optionally, in another embodiment based on the foregoing method of the present application, after the processing according to the target parameter set and outputting an evaluation result of the target product, the method further includes:
receiving task carrying information sent by terminal equipment according to operation and maintenance task information, wherein the task carrying information comprises one or more abnormal identifiers of the industrial equipment;
sending a task binding relationship to each terminal device according to the task carrying information, wherein the task binding relationship comprises: an identifier of a terminal device and an identifier of the industrial device subject to the anomaly.
In one embodiment, the terminal device may input and generate corresponding task carrying information according to one or more operation and maintenance task information, that is, in the task carrying information, the identifier of one or more abnormal working devices included in the task carrying information is the identifier of the abnormal working device in each corresponding operation and maintenance task information. Wherein, the task binding relationship comprises: the identifier of the terminal equipment and the identifier of the abnormal working equipment which is accepted; that is, after the task binding relationship is sent to the terminal device, the terminal device can obtain the matching relationship between the identifier of the terminal device and the identifier of the abnormal working device. It should be noted that all terminal devices that establish connection with the server, for example, terminal devices of maintenance personnel for the working devices, may obtain a matching relationship between an identifier of any one terminal device and an identifier of an abnormal working device that is accepted by the terminal device. The maintenance condition of the working equipment can be synchronously displayed to all maintenance personnel through the server, and the maintenance personnel can see which tasks are carried by other maintenance personnel after logging in through the terminal equipment of the maintenance personnel.
Optionally, in another embodiment of the method according to the present application, the acquiring early warning data includes:
acquiring task information and task timeliness information;
and generating early warning data based on the task information and the task timeliness information.
In one embodiment, the server generates an early warning management mechanism by acquiring the time for normal operation of each business operation input by a manager in advance or in real time, so that the manager can manage and control the production process conveniently.
In one embodiment, the server associates preset card control time according to types of different processes, and triggers an alarm when the duration time exceeds the card control standard time, wherein the early warning information includes, but is not limited to, a machine number, a machine type, an early warning process name, a start time, a duration, standard time, and timeout time.
The method comprises the steps that target product information is obtained by a server based on preset collection frequency and collection types, wherein the target product information comprises operation data of corresponding industrial equipment; acquiring task information and task timeliness information; generating early warning data based on the task information and the task timeliness information; obtaining classification criteria based on the early warning data; preprocessing the operation data based on the classification standard, and outputting a plurality of classes of classification data; respectively processing the classified data and respectively outputting production state data corresponding to the classified data; taking a plurality of production state data as a target parameter set; constructing a product operation characteristic set according to the target product information; constructing a product operation abnormity detection model based on the product operation characteristic set; performing feature traversal matching on the target image set based on the product operation abnormity detection model to obtain a first feature traversal matching result; and obtaining an operation evaluation result of the target product according to the first feature traversal matching result, wherein the evaluation result of the target product comprises abnormal information of the target product.
In addition, the server acquires the operation records of corresponding operators according to the operation data; processing the operation records to obtain the working efficiency data of the workers; processing the abnormal information of the target product and outputting the abnormal type of the abnormal information; outputting a corresponding processing priority based on the exception type of the exception information; determining a processing terminal for processing the abnormal information according to the processing priority; issuing an alert if at least one of the following is detected as abnormal: real-time energy consumption data of the industrial equipment exceeds a first proportion of a first threshold; a second proportion that the total amount of energy consumption data of the industrial equipment in the first time period exceeds a second threshold value; the energy consumption data of the industrial equipment is continuously increased in the second time period; the energy consumption data of the industrial equipment in the third time period is continuously reduced; the fluctuation of the energy consumption data of the industrial equipment in the fourth time period exceeds a preset range; and sending abnormal state reminding information to the processing terminal, wherein the abnormal state reminding information comprises the abnormal information of the target product, the priority of the abnormal information of the target product and the operation evaluation result of the worker. The operation data is continuously monitored and intelligently analyzed, the production, quality, energy consumption and equipment state information of a factory are known in real time, a multi-stage exception handling mechanism is established, exception information occurring in the production process is classified and handled, the exception state of the equipment is conveniently found in time, and field exception fluctuation is responded in time, so that the production efficiency is improved, the production work is prevented in advance, and unplanned shutdown is avoided by control in advance. In addition, the industrial Internet of things data is visualized, the state of a production field is conveniently observed, and managers at all levels are assisted to make correct decisions.
In one embodiment, as shown in fig. 2, the present application further provides an MES-based intelligent manufacturing apparatus, comprising:
the receiving module 201 is configured to obtain target product information based on a preset acquisition frequency and an acquisition type, wherein the target product information includes operation data of corresponding industrial equipment;
a processing module 202 configured to process the operation data of the industrial equipment and output a target parameter set; processing according to the target parameter set, and outputting an evaluation result of a target product, wherein the evaluation result of the target product comprises abnormal information of the target product; processing the operation of the staff according to the evaluation result, and outputting the operation evaluation result of the staff; determining a processing priority corresponding to the abnormal information according to the abnormal information of the target product; determining a processing terminal for processing the abnormal information according to the processing priority; generating abnormal state reminding information based on the abnormal information;
a sending module 203, configured to send abnormal state reminding information to the processing terminal, where the abnormal state reminding information includes abnormal information of the target product, priority of the abnormal information of the target product, and operation evaluation result of the worker.
The method comprises the steps that target product information is obtained by a server based on preset collection frequency and collection types, wherein the target product information comprises operation data of corresponding industrial equipment; processing the operation data of the industrial equipment and outputting a target parameter set; processing according to the target parameter set, and outputting an evaluation result of a target product, wherein the evaluation result of the target product comprises abnormal information of the target product; processing the operation of the staff according to the evaluation result, and outputting the operation evaluation result of the staff; determining a processing priority corresponding to the abnormal information according to the abnormal information of the target product; determining a processing terminal for processing the abnormal information according to the processing priority; generating abnormal state reminding information based on the abnormal information; and sending abnormal state reminding information to the processing terminal, wherein the abnormal state reminding information comprises the abnormal information of the target product, the priority of the abnormal information of the target product and the operation evaluation result of the worker. By continuously monitoring and intelligently analyzing the operation data, the production, quality, energy consumption and equipment state information of a factory are observed in real time, a multistage exception handling mechanism is established, and exception information occurring in the production process is classified and handled, so that the exception state of the equipment can be conveniently found in time, and field exception fluctuation can be responded in time, thus the production efficiency is improved, the advance prevention of production work is realized, and the unplanned shutdown is avoided by in-process control. In addition, the industrial Internet of things data is visualized, the state of a production field is conveniently observed, and management personnel at all levels are assisted to make correct decisions.
In another embodiment of the application, the processing module 302 configured to process the operation data of the industrial device and output a target parameter set includes:
acquiring early warning data;
obtaining classification criteria based on the early warning data;
preprocessing the operation data based on the classification standard, and outputting a plurality of types of classification data;
respectively processing the classified data and respectively outputting production state data corresponding to the classified data;
and taking a plurality of production state data as a target parameter set.
In another embodiment of the application, the processing module 202 is configured to obtain the early warning data, and includes:
acquiring task information and task timeliness information;
and generating early warning data based on the task information and the task timeliness information.
In another embodiment of the application, the processing module 202, configured to perform the processing according to the target parameter set and output an evaluation result of the target product, includes:
constructing a product operation characteristic set according to the target product information;
constructing a product operation abnormity detection model based on the product operation characteristic set;
performing feature traversal matching on the target image set based on the product operation abnormity detection model to obtain a first feature traversal matching result;
and obtaining an operation evaluation result of the target product according to the first characteristic traversal matching result.
In another embodiment of the application, the processing module 202 is configured to process the operation of the worker according to the evaluation result, and output the evaluation result of the operation of the worker, and includes:
acquiring an operation record of a corresponding operator according to the operation data;
and processing the operation record to obtain the working efficiency data of the working personnel.
In another embodiment of the present application, the determining, by the processing module 202, a processing priority corresponding to the exception information according to the exception information of the target product includes:
processing the abnormal information of the target product and outputting the abnormal type of the abnormal information;
and outputting corresponding processing priority based on the exception type of the exception information.
In another embodiment of the application, the processing module 202 is configured to generate the abnormal state reminding information based on the abnormal state data, and further includes:
issuing an alert if at least one of the following is detected as abnormal:
real-time energy consumption data of the industrial equipment exceeds a first proportion of a first threshold;
a second proportion of the total amount of energy consumption data of the industrial equipment in the first time period exceeds a second threshold;
the energy consumption data of the industrial equipment is continuously increased in the second time period;
the energy consumption data of the industrial equipment in the third time period is continuously reduced;
the fluctuation of the energy consumption data of the industrial equipment in the fourth time period exceeds a predetermined range.
The method comprises the steps that target product information is obtained by a server based on preset collection frequency and collection types, wherein the target product information comprises operation data of corresponding industrial equipment; acquiring task information and task timeliness information; generating early warning data based on the task information and the task timeliness information; obtaining a classification standard based on the early warning data; preprocessing the operation data based on the classification standard, and outputting a plurality of classes of classification data; respectively processing the classified data and respectively outputting production state data corresponding to the classified data; taking a plurality of production state data as a target parameter set; constructing a product operation characteristic set according to the target product information; constructing a product operation abnormity detection model based on the product operation characteristic set; performing feature traversal matching on the target image set based on the product operation abnormity detection model to obtain a first feature traversal matching result; and obtaining an operation evaluation result of the target product according to the first feature traversal matching result, wherein the evaluation result of the target product comprises abnormal information of the target product.
In addition, the server acquires the operation records of corresponding operators according to the operation data; processing the operation records to obtain the working efficiency data of the workers; processing the abnormal information of the target product and outputting the abnormal type of the abnormal information; outputting corresponding processing priority based on the exception type of the exception information; determining a processing terminal for processing the abnormal information according to the processing priority; issuing an alert if at least one of the following is detected as abnormal: real-time energy consumption data of the industrial equipment exceeds a first proportion of a first threshold; a second proportion of the total amount of energy consumption data of the industrial equipment in the first time period exceeds a second threshold; the energy consumption data of the industrial equipment is continuously increased in the second time period; the energy consumption data of the industrial equipment in the third time period is continuously reduced; the fluctuation of the energy consumption data of the industrial equipment in the fourth time period exceeds a preset range; and sending abnormal state reminding information to the processing terminal, wherein the abnormal state reminding information comprises the abnormal information of the target product, the priority of the abnormal information of the target product and the operation evaluation result of the worker. The operation data is continuously monitored and intelligently analyzed, the production, quality, energy consumption and equipment state information of a factory are known in real time, a multi-stage exception handling mechanism is established, exception information occurring in the production process is classified and handled, the exception state of the equipment is conveniently found in time, and field exception fluctuation is responded in time, so that the production efficiency is improved, the production work is prevented in advance, and unplanned shutdown is avoided by control in advance. In addition, the industrial Internet of things data is visualized, the state of a production field is conveniently observed, and managers at all levels are assisted to make correct decisions.
The embodiment of the present application provides an electronic device, as shown in fig. 3, which includes a processor 300, a memory 301, a bus 302 and a communication interface 303, where the processor 300, the communication interface 303 and the memory 301 are connected through the bus 302; the memory 301 stores a computer program operable on the processor 300, and the processor 300 executes the management method based on MES intelligent manufacturing provided by any of the foregoing embodiments when executing the computer program.
The Memory 301 may include a Random Access Memory (RAM) and a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is implemented through at least one communication interface 303 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like may be used.
The electronic equipment provided by the above embodiment of the present application and the management method based on MES intelligent manufacturing provided by the embodiment of the present application have the same beneficial effects as the method adopted, run or implemented by the application program stored in the electronic equipment.
An embodiment of the present application provides a computer-readable storage medium, as shown in fig. 4, where the computer-readable storage medium stores 401 a computer program, and when the computer program is read and executed by a processor 402, the management method based on MES intelligent manufacturing as described above is implemented.
The technical solutions of the embodiments of the present application may be substantially implemented as those contributing to the prior art, or all or part of the technical solutions may be implemented in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be an air conditioner, a refrigeration device, a personal computer, a server, or a network device, etc.) or a processor (which is a processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various media capable of storing program codes.
The computer-readable storage medium provided by the above embodiment of the present application and the management method based on MES intelligent manufacturing provided by the embodiment of the present application have the same inventive concept and have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
The present application provides a computer program product, including a computer program, which is executed by a processor to implement the method as described above.
The computer program product provided by the above embodiment of the present application and the management method based on MES intelligent manufacturing provided by the embodiment of the present application have the same beneficial effects as the method adopted, run or implemented by the application program stored in the computer program product.
It is noted that, in the present application, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the application are described in a relevant manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the management method, the electronic device, the electronic equipment and the readable storage medium based on the MES intelligent manufacturing, since they are substantially similar to the embodiments of the management method based on the MES intelligent manufacturing described above, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the embodiments of the management method based on the MES intelligent manufacturing described above.
Although the present application is disclosed above, the present application is not limited thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present disclosure, and it is intended that the scope of the present disclosure be defined by the appended claims.
Claims (10)
1. A management method based on MES intelligent manufacturing is characterized by comprising the following steps:
acquiring target product information based on a preset acquisition frequency and an acquisition type, wherein the target product information comprises operation data of corresponding industrial equipment;
processing the operation data of the industrial equipment and outputting a target parameter set;
processing according to the target parameter set, and outputting an evaluation result of a target product, wherein the evaluation result of the target product comprises abnormal information of the target product;
processing the operation of the staff according to the evaluation result, and outputting the operation evaluation result of the staff;
determining a processing priority corresponding to the abnormal information according to the abnormal information of the target product;
determining a processing terminal for processing the abnormal information according to the processing priority;
generating abnormal state reminding information based on the abnormal information;
and sending abnormal state reminding information to the processing terminal, wherein the abnormal state reminding information comprises the abnormal information of the target product, the priority of the abnormal information of the target product and the operation evaluation result of the worker.
2. The intelligent MES-based manufacturing management method of claim 1, wherein the processing the operational data of the industrial equipment to output a target parameter set comprises:
acquiring early warning data;
obtaining a classification standard based on the early warning data;
preprocessing the operation data based on the classification standard, and outputting a plurality of types of classification data;
respectively processing the classified data and respectively outputting production state data corresponding to the classified data;
and taking a plurality of production state data as a target parameter set.
3. The MES intelligent manufacturing-based management method of claim 2, wherein the obtaining of the warning data comprises:
acquiring task information and task timeliness information;
and generating early warning data based on the task information and the task timeliness information.
4. The MES-based intelligent manufacturing management method according to claim 1, wherein the processing according to the target parameter set and outputting the evaluation result of the target product comprises:
constructing a product operation characteristic set according to the target product information;
constructing a product operation abnormity detection model based on the product operation characteristic set;
performing feature traversal matching on the target image set based on the product operation abnormity detection model to obtain a first feature traversal matching result;
and obtaining an operation evaluation result of the target product according to the first feature traversal matching result.
5. The MES-based intelligent manufacturing management method according to claim 1, wherein the processing the operation of the worker according to the evaluation result and outputting the evaluation result of the operation of the worker comprises:
acquiring operation records of corresponding operators according to the operation data;
and processing the operation record to obtain the working efficiency data of the working personnel.
6. The intelligent MES-based manufacturing management method according to claim 1, wherein the determining a processing priority corresponding to the exception information according to the exception information of the target product comprises:
processing the abnormal information of the target product and outputting the abnormal type of the abnormal information;
and outputting corresponding processing priority based on the exception type of the exception information.
7. The MES-based intelligent manufacturing management method of claim 1, wherein generating exception status notification information based on the exception status data further comprises:
issuing an alert if at least one of the following is detected as abnormal:
the real-time energy consumption data of the industrial equipment exceeds a first proportion of a first threshold;
a second proportion that the total amount of energy consumption data of the industrial equipment in the first time period exceeds a second threshold value;
the energy consumption data of the industrial equipment is continuously increased in the second time period;
the energy consumption data of the industrial equipment in the third time period is continuously reduced;
the fluctuation of the energy consumption data of the industrial equipment in the fourth time period exceeds a predetermined range.
8. An MES-based intelligent manufacturing apparatus, comprising:
the system comprises a receiving module, a processing module and a display module, wherein the receiving module is configured to acquire target product information based on a preset acquisition frequency and an acquisition type, and the target product information comprises operation data of corresponding industrial equipment;
the processing module is configured to process the operation data of the industrial equipment and output a target parameter set; processing according to the target parameter set, and outputting an evaluation result of a target product, wherein the evaluation result of the target product comprises abnormal information of the target product; processing the operation of the staff according to the evaluation result, and outputting the operation evaluation result of the staff; determining a processing priority corresponding to the abnormal information according to the abnormal information of the target product; determining a processing terminal for processing the abnormal information according to the processing priority; generating abnormal state reminding information based on the abnormal information;
the sending module is configured to send abnormal state reminding information to the processing terminal, wherein the abnormal state reminding information comprises the abnormal information of the target product, the priority of the abnormal information of the target product and the operation evaluation result of the worker.
9. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the MES Intelligent manufacturing-based management method of any one of claims 1-7 via execution of the executable instructions.
10. A computer readable storage medium storing computer readable instructions which when executed perform the operations of the MES smart manufacturing based management method of any of claims 1 to 7.
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| CN117910985A (en) * | 2024-03-20 | 2024-04-19 | 通亿（泉州）轻工有限公司 | Intelligent dyeing material management method based on ERP system and related equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117910985A (en) * | 2024-03-20 | 2024-04-19 | 通亿（泉州）轻工有限公司 | Intelligent dyeing material management method based on ERP system and related equipment |
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