Method and device for optimizing production equipment in glass production process

Abstract

The invention discloses a method and a device for optimizing production equipment in a glass production process, relates to the technical field of glass production, and mainly aims to identify and repair the production equipment in a risk interval to be pre-warned so as to prevent interruption of the production process. According to the main technical scheme, the method comprises the following steps: acquiring actual operation state data of each production device in a preset glass production process; performing simulation according to preset simulation software to obtain normal simulation operation state data, upper limit abnormal simulation operation state data and lower limit abnormal simulation operation state data of each production device; determining an upper limit to-be-early-warned risk interval and a lower limit to-be-early-warned risk interval of each production device according to the normal simulation operation state data, the upper limit abnormal simulation operation state data and the lower limit abnormal simulation operation state data; according to the actual operation state data, the upper limit risk interval to be pre-warned and the lower limit risk interval to be pre-warned, whether production equipment to be pre-warned exists or not is judged; and if yes, optimizing the production equipment to be pre-warned according to a preset optimization method.

Description

Technical Field

[0001] The present invention relates to the technical field of glass production, and in particular to a method and device for optimizing production equipment in a glass production process. Background Art

[0002] With the continuous development of society and the continuous advancement of technology, the intelligent and automated glass production is gradually being realized. In this process, most steps are completed by intelligent production equipment and automated transmission tools. Therefore, the health of production equipment is directly related to the efficiency of the entire glass production process.

[0003] Based on this, production equipment maintenance is required. The commonly used production equipment maintenance method is to install a fault alarm system on the production equipment. Once the production equipment triggers a fault alarm, the relevant personnel will be notified immediately and quickly carry out fault repair work to ensure that the production line can resume normal operation as soon as possible.

[0004] However, this approach is essentially an immediate repair strategy after a fault alarm, which focuses on the equipment that has triggered the alarm. When the fault of the alarm equipment is serious, it may cause the production process to be interrupted, resulting in reduced production line efficiency and may affect the on-time delivery of orders. Summary of the invention

[0005] In view of the above problems, the present invention provides a method and device for optimizing production equipment in a glass production process. The main purpose is to quickly identify production equipment in a risk interval to be warned, thereby avoiding the situation where adjustments are made after the equipment reports a fault, preventing interruptions in the production process, and effectively improving the stability and production efficiency of the production line.

[0006] In order to solve the above technical problems, the present invention proposes the following solutions:

[0007] In a first aspect, the present invention provides a method for optimizing production equipment in a glass production process, the method comprising:

[0008] Obtain the actual operating status data corresponding to the production equipment in each production link in the preset glass production process;

[0009] According to the preset simulation software, the normal simulation operation status data, the upper limit abnormal simulation operation status data and the lower limit abnormal simulation operation status data of each production equipment are obtained;

[0010] Determine the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment according to the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment;

[0011] Determine whether there is a production equipment to be warned among the production equipment according to the actual operation status data corresponding to each production equipment, the upper limit risk interval to be warned and the lower limit risk interval to be warned;

[0012] If so, the production equipment to be warned is optimized according to a preset optimization method to prevent the production equipment to be warned from shutting down due to an alarm.

[0013] In a second aspect, the present invention provides an optimization device for production equipment in a glass production process, the device comprising:

[0014] An actual data acquisition unit is used to acquire actual operation status data corresponding to production equipment in each production link in a preset glass production process;

[0015] The simulation operation unit is used to obtain the normal simulation operation status data, the upper limit abnormal simulation operation status data and the lower limit abnormal simulation operation status data of each production equipment according to the actual data acquisition unit of the preset simulation software;

[0016] An interval determination unit, used to determine the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment according to the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment obtained by the simulated operation unit;

[0017] An early warning determination unit, used to determine whether there is a production equipment to be warned among the production equipment according to the actual operation status data corresponding to each production equipment, the upper limit risk interval to be warned and the lower limit risk interval to be warned determined by the interval determination unit;

[0018] The equipment optimization unit is used to optimize the production equipment to be warned according to a preset optimization method if the warning determination unit determines that it exists, so as to prevent the production equipment to be warned from shutting down due to an alarm.

[0019] In order to achieve the above-mentioned purpose, according to the third aspect of the present invention, a storage medium is provided, wherein the storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the optimization method of production equipment in the glass production process of the first aspect mentioned above.

[0020] In order to achieve the above object, according to a fourth aspect of the present invention, a processor is provided, the processor being used to run a program, wherein when the program is run, the method for optimizing production equipment in a glass production process according to the first aspect is executed.

[0021] By means of the above technical scheme, the present invention provides a method and device for optimizing production equipment in a glass production process, which can obtain the actual operation status data of the production equipment in each production link in the preset glass production process. Next, the preset simulation software is used to simulate the operation of each production equipment to obtain the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of the equipment. Based on these data, the corresponding upper limit risk interval to be warned and the lower limit risk interval to be warned can be set for each production equipment. Subsequently, through the actual operation status data of each production equipment and these risk intervals, it can be determined whether there is any production equipment in the state to be warned. Once the production equipment to be warned is found, it is immediately adjusted by the preset optimization method to prevent the equipment from shutting down due to the alarm. In summary, the present invention realizes the rapid identification of potential risk equipment by pre-setting the warning risk interval of each production equipment, avoids emergency adjustments after equipment failure, thereby preventing the interruption of the production process and significantly improving the stability and efficiency of the production line. In addition, the present invention takes into account the risks of exceeding and falling below the normal standard range respectively. By setting the upper and lower limits of the warning risk interval, it can more accurately and comprehensively identify production equipment with warning risks, thereby ensuring the smooth progress of the production process.

[0022] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0024] Figure 1 A flow chart of a method for optimizing production equipment in a glass production process provided by an embodiment of the present invention is shown;

[0025] Figure 2 A flow chart of another method for optimizing production equipment in a glass production process provided by an embodiment of the present invention is shown;

[0026] Figure 3 A block diagram showing a composition of an optimization device for production equipment in a glass production process provided by an embodiment of the present invention;

[0027] Figure 4A block diagram showing the composition of another device for optimizing production equipment in a glass production process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0029] The instant repair strategy after the fault alarm is adopted for the production equipment in the current glass production process may cause the production process to be interrupted, thereby causing the problem of reduced production line efficiency. Through in-depth research, the inventor has proposed a new optimization method for production equipment in the glass production process. The method simulates the simulated operation conditions corresponding to each production equipment according to the preset simulation software, and then obtains the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment according to the simulated operation conditions. Subsequently, the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment are determined according to the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment. In this way, if it is found that a certain production equipment meets its corresponding upper limit risk interval to be warned or the lower limit risk interval to be warned according to the actual operation status data corresponding to each production equipment obtained, the equipment can be determined as a production equipment to be warned. At this time, the production equipment to be warned can be optimized immediately according to the preset optimization method to prevent the equipment from generating an alarm shutdown. This method can effectively avoid the interruption of the production process, thereby avoiding affecting the on-time delivery of orders.

[0030] The executor of the present invention is an equipment maintenance system, and its working principle is as follows: sensors (such as temperature sensors, pressure sensors, flow meters, etc.) are arranged at key positions of each production equipment to monitor the operating status of the production equipment in real time.

[0031] The data collected by these sensors are transmitted to the Programmable Logic Controller (PLC) via wired or wireless means. The PLC system is responsible for collecting this data and performing preliminary processing on it, such as data filtering and format conversion. Afterwards, the PLC system uploads the processed data to the equipment maintenance system via industrial Ethernet, fieldbus or other communication protocols. The maintenance system can be a dedicated server, cloud platform or other form of central management system.

[0032] The equipment maintenance system further processes and analyzes the received data, using tools such as data mining and machine learning to identify whether the equipment has potential risks or anomalies. Based on these analysis results, the equipment maintenance system can generate control commands or optimization strategies, such as adjusting equipment parameters, starting maintenance procedures, or triggering alarms. These commands are then sent to the PLC system through the communication protocol.

[0033] The PLC system controls the production equipment to perform corresponding actions according to the control commands received. For example, if the equipment maintenance system detects that the temperature of a certain production equipment is too high, it will generate a command to reduce the power of the heating element; after receiving the command, the PLC system adjusts the power of the heating element. After the production equipment performs the control action, its new operating status will be captured by the sensor and fed back to the PLC system again. The PLC system uploads these updated data to the equipment maintenance system again, forming a closed-loop control system to ensure that the production equipment is always in the best operating state.

[0034] It should be noted that the type and quantity of sensors are not specifically limited, as long as the number can realize the present invention.

[0035] 101. Obtain the actual operating status data corresponding to the production equipment in each production link in the preset glass production process.

[0036] It needs to be explained that the actual operating status data corresponding to each production equipment includes unique data items related to the specific work content of each production equipment and common data items related to the operating efficiency of each production equipment, among which the common data items refer to the data items commonly owned by all production equipment.

[0037] The process of obtaining the data value in the unique data item by the equipment maintenance system is as follows:

[0038] In this embodiment, a unique identifier (ID) needs to be assigned to the sensor deployed at each production device in advance. At the same time, a configuration file or a database table is created to store the information of each sensor, including but not limited to: sensor ID, sensor type (such as temperature sensor), specific location of the sensor (such as inside the furnace, annealing furnace entrance, etc.), and production link monitored by the sensor (such as melting stage, molding stage, etc.).

[0039] In addition, it is also necessary to maintain a glass production process, which includes multiple production links connected in sequence: batching → melting → forming (calendering / float) → annealing → cooling → cutting → taking out → packaging. And each production link is associated with the above-maintained sensor information.

[0040] The sensor transmits the actual operating status data collected to the programmable logic controller (PLC) via wired or wireless means. When the PLC system receives the data from the sensor, it can parse the sensor ID in the data packet. Subsequently, the PLC system can query the configuration file / database to determine the sensor type, location, and production link corresponding to the ID, and mark the actual operating status data, attaching the sensor location information and production link information.

[0041] In this way, when the equipment maintenance system obtains the data value in the unique data item, it can clearly know which production link in the glass production process the data value in the unique data item corresponds to, thereby clarifying which production equipment it corresponds to.

[0042] The process of obtaining the data value in the common data item by the equipment maintenance system is as follows:

[0043] For each production equipment, the common data items may include production speed, historical failure times or downtime. The production speed can be calculated from the data values ​​in the unique data items obtained above, for example, by calculating the output per unit time by monitoring the output and the corresponding time when the output is obtained. The historical failure times or downtime can be the production equipment records maintained by the staff in the equipment maintenance system in advance. Whenever the equipment encounters a failure or downtime, the staff will update the production equipment records maintained in the equipment maintenance system to ensure the accuracy of these data.

[0044] At this point, the equipment maintenance system can obtain the complete actual operating status data corresponding to the production equipment in each production link in the preset glass production process.

[0045] 102. According to the preset simulation software, the normal simulation operation status data, the upper limit abnormal simulation operation status data and the lower limit abnormal simulation operation status data of each production equipment are obtained.

[0046] 103. Determine the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment according to the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment.

[0047] In step 102 to step 103, since parameters in the equipment that are too high or too low may cause equipment failure, the risk interval to be warned in the present invention should cover both the upper limit risk interval to be warned corresponding to the parameters that are too high and the lower limit risk interval to be warned corresponding to the parameters that are too low.

[0048] And because for each production equipment, unique data items and common data items are distinguished in the actual operating status data, the upper limit risk intervals to be warned corresponding to each production equipment set after this step should also be divided into the first upper limit risk interval to be warned corresponding to the common data items corresponding to each production equipment and the second upper limit risk interval to be warned corresponding to the unique data items corresponding to each production equipment; the first lower limit risk interval to be warned corresponding to the common data items corresponding to each production equipment and the second lower limit risk interval to be warned corresponding to the unique data items corresponding to each production equipment.

[0049] It should be noted that each production link in the glass production process has different and significant differences in work content. Therefore, the production equipment used in each production link has its own specific risk range to be warned.

[0050] Specifically, the simulated operation status of each production equipment can be determined according to the preset simulation software. Then, by gradually adjusting the operation parameters of each production equipment in the simulated operation status, the normal simulated operation status data of each production equipment in the normal state and the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data in the fault state are obtained.

[0051] It should be noted that the preset simulation software can be any simulation software, as long as the preset simulation software has the ability to establish virtual models of various equipment in the glass production process and can simulate the operating status of these equipment. In addition, the software must also realize the adjustability of equipment parameters so as to facilitate detailed adjustment and optimization of the operating status of any equipment.

[0052] The upper limit abnormal simulation operation state data can be obtained by increasing the operation parameters in the preset simulation software. Correspondingly, the lower limit abnormal simulation operation state data is obtained by reducing the operation parameters in the same simulation software.

[0053] It should be noted that the normal simulated operation status data and the upper / lower limit abnormal simulated operation status data here only include data values ​​in unique data items. As for the data in the common data items, such as the production speed, the data value of the production speed in the common data item in the normal simulated operation status data can be obtained according to the data value of the unique data item in the normal simulated operation status data, and similarly, the data value of the production speed in the common data item in the upper / lower limit abnormal simulated operation status data can be obtained according to the data value of the unique data item in the upper / lower limit abnormal simulated operation status data.

[0054] As for other common data items in the normal simulation operation status data and the upper / lower limit abnormal simulation operation status data, such as the number of historical failures or downtime, the data values ​​in the production equipment records maintained in advance by the staff in the equipment maintenance system can be referred to. So far, the normal simulation operation status data and the upper / lower limit abnormal simulation operation status data with all data items have been obtained.

[0055] It should be noted that the data values ​​in the production equipment records maintained in advance by the staff in the equipment maintenance system may be the number of historical failures or downtime of each equipment in a unit time, and this unit time may be multiple times, such as the past year, the past month, or the past week. In this embodiment, the data selected may be the data in the past week, because compared with other data, it can better reflect the recent situation of each production equipment.

[0056] After obtaining the normal simulation operation status data and abnormal simulation operation status data with all data items, the risk interval to be warned can be determined by following the steps below:

[0057] For the first upper / lower limit risk interval to be warned corresponding to the common data items:

[0058] Calculate the efficiency score:

[0059] For both normal simulation operation status data and upper / lower limit abnormal simulation operation status data, the weighted sum of data values ​​corresponding to multiple common data items can be calculated first to obtain the normal simulation efficiency score value and the upper / lower limit abnormal simulation efficiency score value.

[0060] Determine the first upper / lower limit risk interval to be warned:

[0061] The value between the normal simulation efficiency score and the upper / lower limit abnormal simulation efficiency score is taken to obtain the first upper / lower limit warning risk interval corresponding to the common data item. That is, according to the normal simulation efficiency score and the upper limit abnormal simulation efficiency score, the first upper limit warning risk interval corresponding to the common data item is obtained; according to the normal simulation efficiency score and the lower limit abnormal simulation efficiency score, the first lower limit warning risk interval corresponding to the common data item is obtained.

[0062] The second upper / lower limit risk interval to be warned corresponding to the unique data item:

[0063] Calculate the work quality value:

[0064] For normal simulation operation status data and upper / lower limit abnormal simulation operation status data, the weighted sum value between the data values ​​corresponding to multiple unique data items can be calculated first to obtain the normal simulation work quality value and the upper / lower limit abnormal simulation work quality value.

[0065] Determine the second upper / lower limit risk interval to be warned:

[0066] The value between the normal simulated work quality value and the upper / lower limit abnormal simulated work quality value is taken to obtain the second upper / lower limit warning risk interval corresponding to the unique data item. That is, the second upper limit warning risk interval corresponding to the unique data item is obtained according to the normal simulated work quality value and the upper limit abnormal simulated work quality value; the second lower limit warning risk interval corresponding to the unique data item is obtained according to the normal simulated work quality value and the lower limit abnormal simulated work quality value.

[0067] It should be noted that when calculating, the properties of the unique data items and common data items of multiple dimensions need to be transformed to ensure that the relationship between these data items and the final score is consistent. This means that when calculating the efficiency score and work quality value, data items of different nature need to be properly standardized or normalized to ensure that they can be compared on the same scale and have a consistent relationship with the final score.

[0068] When assigning weights to the unique data items and common data items in the operating status data corresponding to each production equipment, the following points can be considered:

[0069] Importance: Consider the degree of impact of the data item on the production process. Generally speaking, data items that have a greater impact on production efficiency should be given a higher weight.

[0070] Controllability: Consider whether the data item can be controlled by adjusting device parameters or operations. Data items with strong adjustability should generally be given a higher weight.

[0071] Stability: Consider the volatility of data items. Data items with poor stability and easily affected by external factors may need to be given lower weights to reduce their impact on the overall score.

[0072] Historical data: Refer to past data analysis results to understand which data items have had the greatest impact on production efficiency in history.

[0073] In addition, when setting weights, it is necessary to avoid the problem of weight overflow, that is, the sum of the weights of multiple common data items does not exceed 1, and the sum of the weights of multiple unique data items does not exceed 1.

[0074] 104. Determine whether there is any production equipment to be warned among the production equipment based on the actual operating status data, the upper limit risk range to be warned and the lower limit risk range to be warned corresponding to each production equipment.

[0075] In this step, you can use the following methods to make judgments:

[0076] Consider common data items and unique data items at the same time: determine whether each production equipment simultaneously meets the first upper / lower limit warning risk interval and the second upper / lower limit warning risk interval based on the data values ​​corresponding to the common data items and the data values ​​of the unique data items in the actual operating status data corresponding to each production equipment.

[0077] Only unique data items are considered: only the data values ​​corresponding to the unique data items are used to determine whether each production equipment meets the second upper / lower limit risk interval for warning; or only common data items are considered: only the data values ​​corresponding to the common data items are used to determine whether each production equipment meets the first upper / lower limit risk interval for warning.

[0078] For example, the unique data items of the batching production stage are: the time and weight of each raw material addition recorded by the weighing sensor. The unique data items of the melting production stage are: the temperature inside the furnace monitored by thermocouples or other temperature sensors and the flow rate of the molten glass measured by the flowmeter.

[0079] Production speed in the common data items: the proportioning time and speed of each batch of raw materials are calculated based on the addition amount and time of raw materials monitored by the weighing sensor in the batching process.

[0080] The production speed in the common data items is the melting speed calculated based on the temperature inside the furnace and the flow rate of the molten glass monitored during the melting process, combined with the fuel consumption rate and the melting rate.

[0081] Regardless of which method is used, as long as it is determined that there is a production device to be warned among the production devices, step 105 can be executed.

[0082] 105. Optimize the production equipment to be warned according to the preset optimization method to prevent the production equipment to be warned from shutting down due to alarm.

[0083] In this step, the normal simulated operation status data of the production equipment to be warned obtained according to the preset simulation software can be introduced. Specifically, it is determined whether there are target unique data items with different data values ​​in the actual operation status data corresponding to the production equipment to be warned and the normal simulated operation status data, that is, it is determined which unique data items in the actual operation status data of the production equipment to be warned have data values ​​deviated from the data values ​​under normal operation.

[0084] If such target-unique data items exist, the operating parameters of the production equipment to be warned can be adjusted in a targeted manner based on these target-unique data items so that the data values ​​of the target-unique data items corresponding to the actual operating status data of each production equipment and the normal simulated operating status data are the same, thereby preventing the warning from causing an alarm shutdown.

[0085] For example, in the glass production process, the production equipment to be warned is a glass bottle forming machine, and one of its unique data items is the mold pressure. The normal simulation operation status data obtained by the preset simulation software shows that the mold pressure should be between 100-120 bar. However, the actual operation status data shows that the mold pressure is 125 bar, which exceeds the normal simulation operation status data range, but does not reach the fault alarm range.

[0086] At this time, the hydraulic system settings in the molding machine can be adjusted through the PLC control system to adjust the mold pressure down to 115 bar, back to the range of normal simulated operating status data. This ensures that the actual operating status data of the production equipment is consistent with the normal simulated operating status data, thereby avoiding alarm shutdowns caused by deviations from the normal range and improving production efficiency and product quality.

[0087] Based on the above Figure 1 It can be seen from the implementation method that the present invention provides a method for optimizing production equipment in a glass production process. In the preset glass production process, the actual operating status data of the production equipment in each production link can be obtained. Next, the preset simulation software is used to simulate the operation of each production equipment to obtain the normal simulated operating status data, the upper limit abnormal simulated operating status data and the lower limit abnormal simulated operating status data of the equipment. Based on these data, a corresponding upper limit risk interval to be warned and a lower limit risk interval to be warned can be set for each production equipment. Subsequently, through the actual operating status data of each production equipment and these risk intervals, it can be determined whether there is any production equipment in a state to be warned. Once the production equipment to be warned is found, it is immediately adjusted using the preset optimization method to prevent the equipment from shutting down due to an alarm. In summary, the present invention realizes the rapid identification of potential risk equipment by pre-setting the warning risk interval of each production equipment, avoids emergency adjustments after equipment failure, thereby preventing the interruption of the production process and significantly improving the stability and efficiency of the production line. In addition, the present invention takes into account the risks of exceeding and falling below the normal standard range respectively. By setting the upper and lower limits of the warning risk interval, it can more accurately and comprehensively identify production equipment with warning risks, thereby ensuring the smooth progress of the production process.

[0088] Furthermore, as a Figure 1 The embodiment shown in the figure is refined and expanded. The embodiment of the present invention also provides another method for optimizing production equipment in a glass production process, such as Figure 2 As shown, the specific steps are as follows:

[0089] 201. Obtain actual operation status data corresponding to production equipment in each production link in a preset glass production process.

[0090] 202. According to the preset simulation software, normal simulation operation status data, upper limit abnormal simulation operation status data and lower limit abnormal simulation operation status data of each production equipment are obtained.

[0091] 203. Determine the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment according to the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment.

[0092] 204. Determine whether there is any production equipment to be warned among the production equipment according to the actual operation status data, the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment.

[0093] After obtaining the actual operation status data, the actual operation status data of each production device can be compared to determine the common data items between the actual operation status data of each production device. Subsequently, the unique data items in the actual operation status data of each production device are determined based on the common data items.

[0094] Calculate the efficiency score:

[0095] For each production equipment, if there are multiple common data items, the weighted sum of the data values ​​corresponding to these common data items is determined as the efficiency score value corresponding to the production equipment.

[0096] Calculate the work quality value:

[0097] For each production equipment, a weighted summation result between data values ​​corresponding to a plurality of unique data items is determined as a work quality value corresponding to the production equipment.

[0098] Determine the production equipment to be warned:

[0099] Finally, it can be determined whether there are production equipments with efficiency score values ​​in the first upper limit risk interval to be warned or the first lower limit risk interval to be warned among all production equipments; and / or, it can be determined whether there are production equipments with work quality values ​​in the second upper limit risk interval to be warned or the second lower limit risk interval to be warned among all production equipments; if so, it can be determined that there are production equipments to be warned among all production equipments; if not, it can be determined that there are no production equipments to be warned among all production equipments. In this way, the operating status of production equipments can be determined more accurately, and potential problems can be discovered in a timely manner.

[0100] It should be noted that the reason for reconfirming whether there are multiple common data items is to avoid the loss of historical failure times or downtime data of each equipment under maintenance due to certain objective factors. In the event of data loss, the efficiency score corresponding to the common data item will be directly reflected as the production speed value of each production equipment.

[0101] The efficiency indicator is introduced to detect whether there are risks to be warned for each production equipment, because the tendency of equipment to gradually fail is often reflected in its working efficiency. Specifically, when the efficiency of equipment decreases, this is usually an important signal that the equipment has a potential failure risk. Therefore, by monitoring the changes in equipment efficiency, possible problems can be discovered at an early stage, so that timely measures can be taken to avoid failures.

[0102] The reason why specific and unique data items and their data values ​​are introduced is that these data values ​​can accurately map the actual working status of each production equipment. They can not only effectively reveal whether the equipment is facing the risk of failure, but also have important guiding significance for optimizing the performance adjustment of production equipment.

[0103] In the embodiment of the present invention, the larger the efficiency score and the work quality value, the better the operating state of the equipment is, because the operating parameters of each production equipment, whether too high or too low, may cause equipment failure. Therefore, the definition of the efficiency score and the work quality value needs to be determined according to the specific production environment and equipment characteristics. In some cases, the efficiency score and the work quality value may need to be kept moderate within a certain range to ensure that the equipment can operate stably and efficiently.

[0104] It should be noted that in the actual operation status data, the weight distribution between the various data items can be the same as the weight in the simulation process.

[0105] 205. Optimize the production equipment to be warned according to the preset optimization method to prevent the production equipment to be warned from shutting down due to an alarm.

[0106] Among them, the implementation method of step 205 is the same as that of step 105, and can achieve the same technical effect and solve the same technical problem, and will not be repeated here.

[0107] 206. Adjust the production equipment in the target production link.

[0108] In this step, the failure risk of the production equipment to be warned may cause a decrease in its operating efficiency to some extent. In this case, the production link where the production equipment to be warned is located can be determined, and their previous target production link in the preset business process can be further identified. Subsequently, the production speed of the production equipment in the target production link can be adjusted through the PLC (Programmable Logic Controller) control system to avoid the problem of task accumulation in the production equipment to be warned.

[0109] Specifically, the control command from the equipment maintenance system can be processed by the corresponding logic program pre-written in the PLC, and the relevant adjustment program can be executed accordingly. The control command is issued by the equipment maintenance system to instruct the PLC to adjust the production speed of the production equipment in the target production link.

[0110] After receiving the control command, the PLC system adjusts the relevant parameters of the production equipment in the target production link, such as motor speed, valve opening, etc., according to the instruction, so as to achieve the purpose of reducing the production speed.

[0111] It is worth noting that, although the various production links in the preset glass production process are usually carried out in sequence, in the actual production process, especially in the case of multiple rounds of production, the last production link of the first round may still be processing the work of the previous round, while the first production link of the first round has already started processing the work of the next round. Therefore, there is a chance that they work at the same time, and the present invention aims to obtain the actual operating status data of these links at the same time point in real time.

[0112] Furthermore, as a response to the above Figure 1 In order to realize the method shown in the figure, the embodiment of the present invention also provides an optimization device for production equipment in a glass production process, which is used to optimize the above-mentioned Figure 1 The device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Figure 3 As shown, the device comprises:

[0113] The actual data acquisition unit 301 is used to acquire the actual operation status data corresponding to the production equipment in each production link in the preset glass production process;

[0114] The simulation operation unit 302 is used to obtain the normal simulation operation status data, the upper limit abnormal simulation operation status data and the lower limit abnormal simulation operation status data of each production equipment according to the actual data acquisition unit 301 of the preset simulation software;

[0115] An interval determination unit 303 is used to determine the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment according to the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment obtained by the simulated operation unit 302;

[0116] The warning determination unit 304 is used to determine whether there is a production equipment to be warned among the production equipment according to the actual operation status data corresponding to each production equipment, the upper limit risk interval to be warned and the lower limit risk interval to be warned determined by the interval determination unit 303;

[0117] The equipment optimization unit 305 is used to optimize the production equipment to be warned according to a preset optimization method if the warning determination unit 304 determines that it exists, so as to prevent the production equipment to be warned from shutting down due to an alarm.

[0118] Furthermore, as a response to the above Figure 2 In order to realize the method shown in the figure, the embodiment of the present invention also provides another device for optimizing production equipment in a glass production process, which is used to optimize the above-mentioned Figure 2 The device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Figure 4 As shown, the device comprises:

[0119] The actual data acquisition unit 301 is used to acquire the actual operation status data corresponding to the production equipment in each production link in the preset glass production process;

[0120] The simulation operation unit 302 is used to obtain the normal simulation operation status data, the upper limit abnormal simulation operation status data and the lower limit abnormal simulation operation status data of each production equipment according to the actual data acquisition unit 301 of the preset simulation software;

[0121] An interval determination unit 303 is used to determine the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment according to the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment obtained by the simulated operation unit 302;

[0122] The warning determination unit 304 is used to determine whether there is a production equipment to be warned among the production equipment according to the actual operation status data corresponding to each production equipment, the upper limit risk interval to be warned and the lower limit risk interval to be warned determined by the interval determination unit 303;

[0123] The equipment optimization unit 305 is used to optimize the production equipment to be warned according to a preset optimization method if the warning determination unit 304 determines that it exists, so as to prevent the production equipment to be warned from shutting down due to an alarm.

[0124] In an optional implementation, the warning determination unit 304 includes:

[0125] The data comparison module 3041 is used to compare the actual operation status data of each production equipment to determine the common data items between the actual operation status data of each production equipment, wherein the common data items are data items representing the operation efficiency of the equipment;

[0126] The data item determination module 3042 is used to determine the unique data items in the actual operation status data of each production device according to the common data items determined by the data comparison module 3041, wherein the unique data items are data items representing the work content of each device;

[0127] The early warning determination module 3043 is used to determine whether there is a production equipment to be warned in each production equipment according to the data value corresponding to the common data item in the corresponding actual operating status data determined by the data comparison module 3041, the data value corresponding to the unique data item determined by the data item determination module 3042, and the upper limit risk interval to be warned and the lower limit risk interval to be warned.

[0128] In an optional implementation manner, the early warning determination module 3043 is specifically used to:

[0129] For each production equipment, if there are multiple common data items, a weighted sum of the data values ​​corresponding to the multiple common data items is determined as the efficiency score corresponding to the production equipment;

[0130] For each production equipment, a weighted sum of data values ​​corresponding to a plurality of unique data items is determined as a work quality value corresponding to the production equipment;

[0131] It is determined whether there is a production equipment to be warned among the production equipment according to the efficiency score value, the work quality value, the upper limit risk interval to be warned, and the lower limit risk interval to be warned corresponding to each production equipment.

[0132] In an optional implementation manner, the early warning determination module 3043 is specifically used to determine whether there is a production equipment to be warned in each production equipment according to the efficiency score value, the work quality value, the upper limit risk interval to be warned, and the lower limit risk interval to be warned corresponding to each production equipment:

[0133] Obtaining, among the upper limit risk intervals to be warned corresponding to each production equipment, a first upper limit risk interval to be warned corresponding to the common data item and a second upper limit risk interval to be warned corresponding to the unique data item;

[0134] Obtaining, in the lower limit risk intervals to be warned corresponding to each production equipment, a first lower limit risk interval to be warned corresponding to the common data item and a second lower limit risk interval to be warned corresponding to the unique data item;

[0135] Determine whether there is any production equipment among the production equipment whose efficiency score is in the first upper limit risk interval to be warned or the first lower limit risk interval to be warned; and / or,

[0136] Determine whether there is any production equipment whose work quality value is in the second upper limit risk interval to be warned or the second lower limit risk interval to be warned among the production equipment;

[0137] If so, it is determined that there is a production equipment to be warned among the production equipment;

[0138] If not, it is determined that there is no production equipment to be warned among the production equipment.

[0139] In an optional implementation manner, the interval determining unit 303 includes:

[0140] The normal value determination module 3031 is used to calculate the normal simulation efficiency score value and the normal simulation work quality value corresponding to the normal simulation operation status data for each production equipment;

[0141] The first abnormal value determination module 3032 is used to calculate the upper limit abnormal simulation efficiency score value and the upper limit abnormal simulation work quality value corresponding to the upper limit abnormal simulation operation status data for each production equipment;

[0142] The second abnormal value determination module 3033 is used to calculate the lower limit abnormal simulation efficiency score value and the lower limit abnormal simulation work quality value corresponding to the lower limit abnormal simulation operation status data for each production equipment;

[0143] The first interval determination module 3034 is used to obtain a first upper limit risk interval to be warned corresponding to the common data item according to the normal simulation efficiency score value determined by the normal value determination module 3031 and the upper limit abnormal simulation efficiency score value determined by the first abnormal value determination module 3032;

[0144] The second interval determination module 3035 is used to obtain a first lower limit risk interval to be warned corresponding to the common data item according to the normal simulation efficiency score value determined by the normal value determination module 3031 and the lower limit abnormal simulation efficiency score value determined by the second abnormal value determination module 3033;

[0145] A third interval determination module 3036 is used to obtain a second upper limit risk interval to be warned corresponding to a unique data item according to the normal simulated work quality value determined by the normal value determination module 3031 and the upper limit abnormal simulated work quality value determined by the first abnormal value determination module 3032;

[0146] The fourth interval determination module 3037 is used to obtain the second lower limit warning risk interval corresponding to the unique data item according to the normal simulated work quality value determined by the normal value determination module 3031 and the lower limit abnormal simulated work quality value determined by the second abnormal value determination module 3033.

[0147] In an optional implementation manner, the device optimization unit 305 includes:

[0148] The abnormality determination module 3051 is used to determine whether there is a target unique data item with different data values ​​in the actual operation status data and the normal simulation operation status data corresponding to each production device;

[0149] The parameter adjustment module 3052 is used to adjust the operating parameters of the production equipment to be warned according to the target unique data item if the determination result of the abnormality determination module 3051 is existence, so as to make the data value of the target unique data item between the actual operating status data corresponding to each production equipment and the normal simulated operating status data the same, thereby preventing the production equipment to be warned from shutting down due to an alarm.

[0150] In an optional implementation, when the parameter adjustment module 3052 adjusts the operating parameters of the production equipment to be warned according to the target unique data item, the device further includes a process adjustment unit 306, and the process adjustment unit 306 includes:

[0151] The link determination module 3061 is used to determine the previous target production link of the production link where the production equipment to be warned is located according to a preset business process;

[0152] The equipment adjustment module 3062 is used to use the preset control system to adjust the operating parameters of the production equipment in the target production link determined by the link determination module 3061, so as to reduce the production speed of the production equipment and prevent the production equipment to be warned from generating task accumulation.

[0153] Furthermore, an embodiment of the present invention further provides a storage medium, wherein the storage medium is used to store a computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the above Figure 1-2 A method for optimizing production equipment in the glass production process described in .

[0154] Furthermore, an embodiment of the present invention further provides a processor, the processor is used to run a program, wherein the program executes the above Figure 1-2 A method for optimizing production equipment in the glass production process described in .

[0155] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0156] It is understandable that the related features in the above methods and devices can be referenced to each other. In addition, the "first", "second" and the like in the above embodiments are used to distinguish the embodiments, but do not represent the advantages and disadvantages of the embodiments.

[0157] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0158] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0159] In addition, the memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0160] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0161] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0164] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0165] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0166] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0167] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0168] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0169] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for optimizing production equipment in a glass production process, characterized in that: The method comprises: Obtain the actual operating status data corresponding to the production equipment in each production link in the preset glass production process; According to the preset simulation software, the normal simulation operation status data, the upper limit abnormal simulation operation status data and the lower limit abnormal simulation operation status data of each production equipment are obtained; Determine the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment according to the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment; Determine whether there is a production equipment to be warned among the production equipment according to the actual operation status data corresponding to each production equipment, the upper limit risk interval to be warned and the lower limit risk interval to be warned; If so, the production equipment to be warned is optimized according to a preset optimization method to prevent the production equipment to be warned from shutting down due to an alarm.

2. The method according to claim 1, characterized in that Judging whether there is a production equipment to be warned among the production equipment according to the actual operation status data corresponding to each production equipment, the upper limit risk interval to be warned, and the lower limit risk interval to be warned, includes: Comparing the actual operation status data of each production equipment to determine the common data items between the actual operation status data of each production equipment, wherein the common data items are data items representing the operation efficiency of the equipment; Determine unique data items in the actual operation status data of each production device according to the common data items, wherein the unique data items are data items representing the work content of each device; For each production equipment, determine whether there is a production equipment to be warned among the production equipment based on the data values ​​corresponding to the common data items in the corresponding actual operating status data, the data values ​​corresponding to the unique data items, the upper limit risk interval to be warned, and the lower limit risk interval to be warned.

3. The method according to claim 2, characterized in that For each production equipment, judging whether there is a production equipment to be warned in each production equipment according to the data value corresponding to the common data item in the corresponding actual operation status data, the data value corresponding to the unique data item, the upper limit risk interval to be warned, and the lower limit risk interval to be warned, including: For each production equipment, if there are multiple common data items, a weighted sum of the data values ​​corresponding to the multiple common data items is determined as the efficiency score corresponding to the production equipment; For each production equipment, a weighted sum of data values ​​corresponding to a plurality of unique data items is determined as a work quality value corresponding to the production equipment; According to the efficiency score value, the work quality value, the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment, it is judged whether there is a production equipment to be warned among the production equipment.

4. The method according to claim 3, characterized in that Judging whether there is a production equipment to be warned among the production equipment according to the efficiency score value, the work quality value, the upper limit risk interval to be warned, and the lower limit risk interval to be warned corresponding to each production equipment, includes: Obtaining, in the upper limit risk intervals to be warned corresponding to each production equipment, a first upper limit risk interval to be warned corresponding to the common data item and a second upper limit risk interval to be warned corresponding to the unique data item; Obtaining, in the lower limit risk intervals to be warned corresponding to each production equipment, a first lower limit risk interval to be warned corresponding to the common data item and a second lower limit risk interval to be warned corresponding to the unique data item; Determine whether there is any production equipment among the production equipment whose efficiency score is in the first upper limit risk interval to be warned or the first lower limit risk interval to be warned; and / or, Determine whether there is any production equipment whose work quality value is in the second upper limit risk interval to be warned or the second lower limit risk interval to be warned among the production equipment; If so, it is determined that there is a production equipment to be warned among the production equipment; If not, it is determined that there is no production equipment to be warned among the production equipment.

5. The method according to claim 1, characterized in that According to the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment, the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment are determined, including: For each production equipment, respectively calculate the normal simulation efficiency score value and the normal simulation work quality value corresponding to the normal simulation operation status data; For each production equipment, respectively calculate the upper limit abnormal simulation efficiency score value and the upper limit abnormal simulation work quality value corresponding to the upper limit abnormal simulation operation status data; For each production equipment, respectively calculate the lower limit abnormal simulation efficiency score value and the lower limit abnormal simulation work quality value corresponding to the lower limit abnormal simulation operation status data; According to the normal simulation efficiency score value and the upper limit abnormal simulation efficiency score value, a first upper limit risk interval to be warned corresponding to the common data item is obtained; According to the normal simulation efficiency score value and the lower limit abnormal simulation efficiency score value, a first lower limit risk interval to be warned corresponding to the common data item is obtained; A second upper limit risk interval to be warned corresponding to a unique data item is obtained according to the normal simulated work quality value and the upper limit abnormal simulated work quality value; A second lower limit risk interval to be warned corresponding to a unique data item is obtained according to the normal simulated work quality value and the lower limit abnormal simulated work quality value.

6. The method according to claim 1, characterized in that Optimizing the production equipment to be warned according to a preset optimization method to prevent the production equipment to be warned from shutting down due to an alarm, including: Determine whether there is a target unique data item with different data values ​​in the actual operation status data and the normal simulation operation status data corresponding to each production equipment; If it exists, the operating parameters of the production equipment to be warned are adjusted according to the target unique data item so that the data value of the target unique data item between the actual operating status data corresponding to each production equipment and the normal simulated operating status data is the same, so as to prevent the production equipment to be warned from shutting down due to an alarm.

7. The method according to claim 6, characterized in that When adjusting the operating parameters of the production equipment to be warned according to the target unique data item, the method further includes: According to the preset business process, determine the previous target production link of the production link where the production equipment to be warned is located; The preset control system is used to adjust the operating parameters of the production equipment in the target production link so as to reduce the production speed of the production equipment and prevent the production equipment to be warned from having task accumulation.

8. An optimization device for production equipment in a glass production process, characterized in that: The device comprises: An actual data acquisition unit is used to acquire actual operation status data corresponding to production equipment in each production link in a preset glass production process; The simulation operation unit is used to obtain the normal simulation operation status data, the upper limit abnormal simulation operation status data and the lower limit abnormal simulation operation status data of each production equipment according to the actual data acquisition unit of the preset simulation software; An interval determination unit, used to determine the upper limit risk interval to be warned and the lower limit risk interval to be warned corresponding to each production equipment according to the normal simulated operation status data, the upper limit abnormal simulated operation status data and the lower limit abnormal simulated operation status data of each production equipment obtained by the simulated operation unit; An early warning determination unit, used to determine whether there is a production equipment to be warned among the production equipment according to the actual operation status data corresponding to each production equipment, the upper limit risk interval to be warned and the lower limit risk interval to be warned determined by the interval determination unit; The equipment optimization unit is used to optimize the production equipment to be warned according to a preset optimization method if the warning determination unit determines that it exists, so as to prevent the production equipment to be warned from shutting down due to an alarm.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the method for optimizing production equipment in a glass production process according to any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the method for optimizing production equipment in a glass production process as described in any one of claims 1 to 7.

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