Device Efficiency Prediction Method, Electronic Device, Production Scheduling Method and Device

Abstract

The present application proposes a method for predicting equipment efficiency, an electronic device, a production scheduling method and a device. The method for predicting equipment efficiency includes obtaining historical operation parameter data and historical production parameter data of the equipment, calculating the production efficiency value and multiple different efficiency weight values of the equipment according to the historical operation parameter data and historical production parameter data of the equipment, estimating the cross-efficiency value of the equipment based on the multiple different efficiency weight values of the equipment, and determining the average efficiency value of the equipment by using the production efficiency value and cross-efficiency value of the equipment. The present application calculates the average efficiency value of each equipment by using the operation parameter data and production parameter data of the equipment, then compares the average efficiency values of each equipment, screens out the equipment that meets the expectations, reasonably allocates the production quantity generated by the equipment that meets the expectations, and improves the output and quality.

Description

Technical Field

[0001] This application relates to the technical field of production scheduling management, and particularly to a method for predicting equipment efficiency, an electronic device, a production scheduling method, and a device. Background Art

[0002] The existing production scheduling method determines the daily production quantity according to orders, and then the on-site experience is used to allocate production equipment according to the overall equipment efficiency. This scheduling method only considers a single input factor of the overall equipment efficiency. However, it does not consider that maintaining the normal operation of the equipment requires certain maintenance costs and consumable costs, and these cost data directly affect the equipment production efficiency. In addition, the output variable data of the equipment also has a certain impact on the equipment production efficiency. Therefore, the method of performing production scheduling only based on the overall equipment efficiency does not comprehensively consider the equipment production status, and there are certain deviations in the scheduling results, which will affect the output and quality of products. Summary of the Invention

[0003] In view of the above, this application proposes a method for predicting equipment efficiency, an electronic device, a production scheduling method, and a device. By using multiple operating parameters and production parameter data of the equipment and other production conditions, the average efficiency of the equipment is calculated, and production scheduling is performed based on the ranking of the average efficiency of the equipment, which can more accurately recommend high-efficiency equipment for production, reasonably allocate production quantities, and improve output and quality.

[0004] An embodiment of this application provides a method for predicting equipment efficiency, including: obtaining historical operating parameter data and historical production parameter data of the equipment; calculating a production efficiency value and multiple different efficiency weight values of the equipment according to the historical operating parameter data and historical production parameter data of the equipment; estimating a cross-efficiency value of the equipment based on the multiple different efficiency weight values of the equipment; and determining an average efficiency value of the equipment by using the production efficiency value and cross-efficiency value of the equipment.

[0005] In some embodiments, the step of calculating the production efficiency value and multiple different efficiency weight values of the equipment includes: constructing an efficiency prediction model and preprocessing the efficiency prediction model; inputting the historical operating parameter data and historical production parameter data of the equipment into the preprocessed efficiency prediction model to obtain an efficiency value range of the equipment, where the efficiency value range includes multiple different efficiency values; sorting the multiple different efficiency values in the efficiency value range to obtain a sorting result; and based on the sorting result, taking the largest efficiency value in the efficiency value range as the production efficiency value, and the remaining efficiency values as the efficiency weight values.

[0006] In some embodiments, the step of estimating the cross-efficiency value of the equipment includes: calculating an average value of the sum of the multiple different efficiency weight values of the equipment to obtain the cross-efficiency value of the equipment.

[0007] In some embodiments, the step of determining the average efficiency value of the device includes: selecting the optimal weight of the cross-efficiency value of the device; calculating the product of the cross-efficiency value of the device and the optimal weight, and obtaining the average value of the sum of the product and the production efficiency value of the device to obtain the average efficiency value of the device.

[0008] An embodiment of the present application further provides an electronic device, including a first processor and a storage medium. The storage medium includes readable instructions for being executed by the first processor to perform the device efficiency prediction method as described above.

[0009] An embodiment of the present application further provides a production scheduling method, including: obtaining operation parameter data and production parameter data of multiple devices within a set historical time period; respectively predicting the average efficiency values of the multiple devices according to the operation parameter data and production parameter data of the multiple devices; obtaining target order data, and determining the total production quantity and total production time corresponding to the target order; obtaining the average production time of the multiple devices within the set historical time period according to the operation parameter data of the multiple devices; based on the total production quantity, the total production time, the average production time of the multiple devices, and the average efficiency values of the multiple devices, screening out the devices that meet the expectations, and determining the production quantity corresponding to the devices that meet the expectations.

[0010] In some embodiments, the step of predicting the average efficiency value of the device includes: obtaining the operation parameter data and production parameter data of the device; calculating the production efficiency value and multiple different efficiency weight values of the device according to the operation parameter data and production parameter data of the device; estimating the cross-efficiency value of the device based on the multiple different efficiency weight values of the device; using the production efficiency value and cross-efficiency value of the device to determine the average efficiency value of the device.

[0011] In some embodiments, after predicting the average efficiency values of the multiple devices, further compare the magnitudes of the average efficiency values of the multiple devices, and arrange the average efficiency values of the multiple devices in a gradually decreasing order.

[0012] In some embodiments, the step of determining the total production quantity and total production time corresponding to the target order includes: parsing the target order data to extract the total production quantity corresponding to the target order; calculating the total production time corresponding to the target order according to the total production quantity and the processing time of a single quantity.

[0013] In some embodiments, the steps of screening out the devices that meet the expectations and determining the production quantity corresponding to the devices that meet the expectations include: calculating the average value of the sum of the average production times of the multiple devices within a set historical time period to obtain the average production time corresponding to all the devices within the set historical time period; calculating the number of devices required for the target order according to the total production time corresponding to the target order and the average production time corresponding to all the devices; screening out the required number of devices that meet the expectations and rank among the top from the multiple devices based on the arrangement result of the average efficiency values of the multiple devices; and calculating the production quantity of the devices that meet the expectations by using the total production quantity corresponding to the target order and the average production time corresponding to the devices that meet the expectations.

[0014] An embodiment of the present application further provides a production scheduling device, including: a communicator, configured to receive the operation parameter data and production parameter data of multiple devices within a set historical time period, and further configured to receive target order data; a second processor, coupled to the communicator, configured to predict the average efficiency values of the multiple devices respectively according to the received operation parameter data and production parameter data of the multiple devices; determine the total production quantity and total production time corresponding to the target order according to the received target order data; obtain the average production time of the multiple devices within the set historical time period according to the operation parameter data of the multiple devices; and screen out the devices that meet the expectations and determine the production quantity corresponding to the devices that meet the expectations based on the total production quantity, the total production time, the average production time of the multiple devices, and the average efficiency values of the multiple devices.

[0015] In this way, the present application uses the multiple operation parameter data and production parameter data of each device to calculate the production efficiency value and efficiency weight value of each device, and then estimates the cross-efficiency value of each device. The average efficiency value of each device is calculated through the production efficiency value and cross-efficiency value of each device, and then the average efficiency values of each device are compared. According to the efficiency comparison result, the devices that meet the expectations are screened out, so that when arranging the order production quantity, it can be specified to the device level, and the devices that meet the expectations are preferably used for production, and the production quantity of the devices is reasonably allocated according to the average production time of the devices, thereby improving the production quantity and quality.

[0016] The present application takes into account that certain cost expenditures are required for device operation in product production, which may include maintenance costs, spare parts consumption costs, etc. The consumable quantity cost and maintenance cost are used as operation parameter data, and the efficiency value of the device is calculated and the efficiency of multiple devices is compared by using multi-dimensional input factors such as the overall equipment effectiveness, consumable quantity cost, alarm frequency, maintenance cost, power consumption, and the average production time of the device, and the production quantity data. Compared with the scheduling method that only considers the overall equipment effectiveness, the scheduling result is more reasonable and accurate. Brief Description of the Drawings

[0017] Figure 1 is a schematic flowchart of a method for predicting equipment efficiency according to an embodiment of the present application.

[0018] Figure 2 is a schematic flowchart of a method for calculating equipment production efficiency values and efficiency weight values according to an embodiment of the present application.

[0019] Figure 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

[0020] Figure 4 is a schematic flowchart of a production scheduling method according to an embodiment of the present application.

[0021] Figure 5 is a schematic flowchart of a method for predicting the average efficiency value of multiple devices according to an embodiment of the present application.

[0022] Figure 6 is a schematic flowchart of a method for screening out devices that meet expectations and determining the production quantity of devices that meet expectations according to an embodiment of the present application.

[0023] Figure 7 is a schematic structural diagram of a production scheduling device according to an embodiment of the present application.

[0024] Description of Main Element Symbols

[0025] Electronic device 100

[0026] First processor 10

[0027] Storage medium 11

[0028] Production scheduling device 200

[0029] Communicator 20

[0030] Second processor 21

[0031] The following specific embodiments will further illustrate the present application in conjunction with the above-mentioned drawings. Specific Embodiments

[0032] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0033] An embodiment of the present application provides a method for predicting equipment efficiency, including: obtaining historical operation parameter data and historical production parameter data of the equipment, calculating a production efficiency value and multiple different efficiency weight values of the equipment according to the historical operation parameter data and historical production parameter data of the equipment, estimating a cross-efficiency value of the equipment based on the multiple different efficiency weight values of the equipment, and determining an average efficiency value of the equipment by using the production efficiency value and cross-efficiency value of the equipment.

[0034] An embodiment of the present application further provides an electronic device, including a first processor and a storage medium, where the storage medium includes readable instructions for being executed by the first processor to perform the equipment efficiency prediction method as described above.

[0035] An embodiment of the present application further provides a production scheduling method, including obtaining operation parameter data and production parameter data of multiple equipment within a set historical time period, respectively predicting the average efficiency values of the multiple equipment according to the operation parameter data and production parameter data of the multiple equipment, obtaining target order data, determining the total production quantity and total production time corresponding to the target order, obtaining the average production time of the multiple equipment within the set historical time period according to the operation parameter data of the multiple equipment, and screening out equipment that meets the expectations and determining the production quantity corresponding to the equipment that meets the expectations based on the total production quantity, the total production time, the average production time of the multiple equipment, and the average efficiency values of the multiple equipment.

[0036] An embodiment of the present application further provides a production scheduling device, including a communicator for receiving operation parameter data and production parameter data of multiple equipment within a set historical time period, and further for receiving target order data, and a second processor coupled to the communicator for respectively predicting the average efficiency values of the multiple equipment according to the received operation parameter data and production parameter data of the multiple equipment; determining the total production quantity and total production time corresponding to the target order according to the received target order data; obtaining the average production time of the multiple equipment within the set historical time period according to the operation parameter data of the multiple equipment; screening out equipment that meets the expectations and determining the production quantity corresponding to the equipment that meets the expectations based on the total production quantity, the total production time, the average production time of the multiple equipment, and the average efficiency values of the multiple equipment.

[0037] Combined with the accompanying drawings, some embodiments of the present application will be described in detail below.

[0038] As Figure 1As shown in the figure, an embodiment of the present application provides a method for predicting equipment efficiency. This method for predicting equipment efficiency is used to calculate the production efficiency value and efficiency weight value of the equipment through an efficiency prediction model based on multiple historical operation parameter data and historical production parameter data of the equipment, and then estimate the cross-efficiency value of the equipment. Subsequently, the average efficiency value of the equipment is calculated through the production efficiency value and the cross-efficiency value.

[0039] Please refer to Figure 1 , the method for predicting equipment efficiency includes:

[0040] S1. Obtain the historical operation parameter data and historical production parameter data of the equipment.

[0041] The historical operation parameter data and historical production parameter data of the equipment can be collected through online monitoring. Online monitoring can be used to automatically collect the historical operation parameter data and historical production parameter data of the equipment at different historical stages. Moreover, taking online monitoring as the main method and patrol monitoring and other non-online means as the supplement, the equipment data of the production equipment can be further improved to enhance the integrity and accuracy of the equipment data.

[0042] Among them, the historical operation parameter data includes parameters such as the overall equipment efficiency, the quantity cost of consumables, the alarm frequency, the maintenance cost, the power consumption, and the average production time of the equipment. The historical production parameter data includes parameters such as the output or yield.

[0043] It should be noted that considering that certain cost expenditures are required during the operation of the equipment for product production, which may include maintenance costs, spare parts consumption costs, etc., the present application takes the quantity cost of consumables and the maintenance cost as operation parameter data, and uses multi-dimensional input factors such as the overall equipment efficiency, the quantity cost of consumables, the alarm frequency, the maintenance cost, the power consumption, and the average production time of the equipment and the output data to calculate a more accurate efficiency value of the equipment.

[0044] S2. Calculate the production efficiency value and multiple different efficiency weight values of the equipment according to the historical operation parameter data and historical production parameter data of the equipment.

[0045] In some embodiments, as Figure 2 shown, the steps of calculating the production efficiency value and multiple different efficiency weight values of the equipment include:

[0046] S201. Construct an efficiency prediction model and preprocess the efficiency prediction model.

[0047] In some embodiments, the data envelopment analysis method (DEA) is used to construct a DEA-CCR model, and the DEA-CCR model is used as the efficiency prediction model. Among them, the non-linear programming equation of the efficiency prediction model is:

[0048]

[0049]

[0050]

[0051]

[0052] Among them, k is the number of the device, n is the number of production parameter data, u is the weight coefficient of the production parameter data, Y is the production parameter data, m is the number of operation parameter data, X is the operation parameter data, v is the weight coefficient of the operation parameter data, and E k is the efficiency value of device k.

[0053] It should be noted that preprocessing the efficiency prediction model means transforming the non-linear programming equation of the efficiency prediction model into a linear programming equation. The transformed linear programming equation is:

[0054]

[0055]

[0056]

[0057]

[0058]

[0059] Among them, k is the number of the device, n is the number of production parameter data, u is the weight coefficient of the production parameter data, Y is the production parameter data, m is the number of operation parameter data, X is the operation parameter data, and v is the weight coefficient of the operation parameter data.

[0060] S202. Input the historical operation parameter data and historical production parameter data of the device into the preprocessed efficiency prediction model to obtain an efficiency value interval of the device, and the efficiency value interval includes multiple different efficiency values.

[0061] Taking the historical operation parameter data of the device including six parameter data of the comprehensive efficiency, consumable quantity cost, alarm frequency, maintenance cost, power consumption, and average production time of the device, and the historical production parameter data of the device including output data as an example, the specific implementation manner of step S202 is:

[0062] Input the seven parameter data of the comprehensive efficiency, consumable quantity cost, alarm frequency, maintenance cost, power consumption, average production time, and output of the device into the preprocessed efficiency prediction model, and predict seven possible efficiency values of the device to form an efficiency value interval.

[0063] The number of efficiency values within the efficiency value range is related to the number of parameter data of the operating parameter data and production parameter data input to the efficiency prediction model. In this application, 6 pieces of operating parameter data and 1 piece of production parameter data are used. After being predicted by the efficiency prediction model, 7 possible efficiency values are obtained, including 1 production efficiency value and 6 efficiency weight values.

[0064] S203. Sort multiple different efficiency values within the efficiency value range to obtain a sorting result.

[0065] Since the production efficiency value of the device is the optimal efficiency value and is unique, therefore, when sorting multiple efficiency values within the efficiency value range, the upper limit of the efficiency value range is selected as the production efficiency value of the device, and the rest are used as efficiency weight values. The upper limit of the efficiency value range can reflect the best efficiency of the device.

[0066] Among them, sorting means sorting multiple different efficiency values within the efficiency value range from largest to smallest or from smallest to largest. The sorting result can show the upper limit of the efficiency value range, that is, the largest efficiency value among multiple different efficiency values.

[0067] S204. Based on the sorting result, use the largest efficiency value within the efficiency value range as the production efficiency value, and the remaining efficiency values as efficiency weight values.

[0068] In some embodiments, input the 7 parameter data of the comprehensive efficiency, consumable quantity cost, alarm frequency, maintenance cost, power consumption, average production time, and output of the device into the preprocessed efficiency prediction model. 7 possible efficiency values of the device are predicted, which are E1, E2, E3, E4, E5, E6, and E7 respectively. If E7>E6>E5>E4>E3>E2>E1, then E7 is the largest efficiency value within the efficiency value range, select E7 as the production efficiency value, and E1, E2, E3, E4, E5, and E6 as efficiency weight values.

[0069] S3. Estimate the cross-efficiency value of the device based on multiple different efficiency weight values of the device.

[0070] Taking the example of predicting 7 possible efficiency values of the device, the cross-efficiency value of the device is the average of the 6 efficiency values among the 7 possible efficiency values of the predicted device except the largest efficiency value, obtaining the optimal efficiency weight value, and using the optimal efficiency weight value as the cross-efficiency value of the device.

[0071] Among them, the calculation formula for calculating the cross-efficiency value of the device is:

[0072]

[0073] Among them, k is the number of the device, and R is the number of parameters of the input efficiency prediction model. is the efficiency weight value of device k, and CE k is the cross-efficiency value of device k.

[0074] S4. Using the production efficiency value and cross-efficiency value of the device, determine the average efficiency value of the device.

[0075] In some embodiments, the steps of determining the average efficiency value of the device include:

[0076] Select the optimal weight of the cross-efficiency value of the device;

[0077] Calculate the product of the cross-efficiency value of the device and the optimal weight, and obtain the average value of the sum of the product and the production efficiency value of the device, so as to obtain the average efficiency value of the device.

[0078] Among them, the formula for calculating the average efficiency value of the device is:

[0079]

[0080] Among them, k is the number of the device, and R is the number of parameters of the input efficiency prediction model, and CE k is the cross-efficiency value of device k, and h k is the production efficiency value of device k, and AE k is the average efficiency value of device k.

[0081] In some embodiments, select the number of historical operating parameters of the input efficiency prediction model as the optimal weight of the cross-efficiency value of the device. Taking the historical operating parameter data of the device including six parameter data of the comprehensive efficiency of the device, the quantity cost of consumables, the alarm frequency, the maintenance cost, the power consumption and the average production time as an example, the optimal weight (R - 1) of the cross-efficiency value CE k of device k is 6. Calculate the average value of the sum of 6 times the cross-efficiency value CE k of device k and the production efficiency value h k of device k, so as to obtain the optimal average efficiency value of device k.

[0082] The above device efficiency prediction method takes into account production conditions such as device performance and device quality. According to parameter data such as the historical comprehensive efficiency, alarm volume, output, quality, maintenance cost, and consumable cost of the device, the production efficiency value and efficiency weight value of the device are calculated through the efficiency prediction model, and then the cross-efficiency value of the device is estimated according to the efficiency weight value of the device. The average efficiency value of the device is calculated through the production efficiency value and the cross-efficiency value. This application can compare the efficiencies of multiple devices according to the average efficiency value of the device, which is convenient for recommending high-efficiency devices to be put into operation and improving production efficiency.

[0083] As Figure 3 shown, an embodiment of the present application further provides an electronic device 100. The electronic device 100 includes a first processor 10 and a storage medium 11. The storage medium 11 includes readable instructions for being executed by the first processor 10 to perform the following method:

[0084] Obtain the historical operation parameter data and historical production parameter data of the device;

[0085] Calculate the production efficiency value and multiple different efficiency weight values of the device according to the historical operation parameter data and historical production parameter data of the device;

[0086] Estimate the cross-efficiency value of the device based on multiple different efficiency weight values of the device;

[0087] Determine the average efficiency value of the device by using the production efficiency value and cross-efficiency value of the device.

[0088] As Figure 4 shown, an embodiment of the present application further provides a production scheduling method. The production scheduling method calculates the production efficiency value and efficiency weight value of each device through an efficiency prediction model according to the historical operation parameter data and historical production parameter data of multiple devices, and then estimates the cross-efficiency value of each device. Then, the average efficiency value of each device is calculated through the production efficiency value and cross-efficiency value. Based on the average efficiency values of multiple devices, the average efficiency values of multiple devices are compared, and devices with high efficiency are recommended to be put into operation according to the efficiency comparison result. And the production quantity of the devices put into operation is scheduled according to the average production time.

[0089] Please refer to the appendix Figure 4 , and the production scheduling method includes:

[0090] S11, obtain the operation parameter data and production parameter data of multiple devices within a set historical time period.

[0091] For example, the set historical time period is the time of the past week. The operation parameter data and production parameter data generated by the processing of multiple devices on the production line within the past week are obtained through online monitoring. The operation parameter data includes index data such as overall equipment effectiveness, consumable quantity cost, alarm frequency, maintenance cost, power consumption, and average production time of the device. The production parameter data includes index data such as output or yield.

[0092] S12, predict the average efficiency values of the multiple devices according to the operation parameter data and production parameter data of the multiple devices.

[0093] In some embodiments, as Figure 5As shown, the step of predicting the average efficiency values of multiple devices includes:

[0094] S121. Calculate the production efficiency value and multiple different efficiency weight values of each device respectively according to the operation parameter data and production parameter data of the multiple devices.

[0095] In some embodiments, to calculate the production efficiency value and multiple different efficiency weight values of each device, it is necessary to use the data envelopment analysis (DEA) to construct an efficiency prediction model and preprocess the efficiency prediction model. Input the operation parameter data and production parameter data of a device into the preprocessed efficiency prediction model to obtain an efficiency value range of the device, and the efficiency value range includes multiple different efficiency values. Sort the multiple different efficiency values within the efficiency value range of the device, and select the maximum efficiency value within the efficiency value range of the device as the production efficiency value of the device, and the remaining efficiency values as the efficiency weight values of the device.

[0096] S122. Estimate the cross-efficiency value of each device based on the multiple different efficiency weight values of each device.

[0097] In some embodiments, obtain the cross-efficiency value of each device by calculating the average value of the sum of the multiple different efficiency weight values of each device.

[0098] S123. Determine the average efficiency value of each device by using the production efficiency value and cross-efficiency value of each device.

[0099] In some embodiments, first select the optimal weight of the cross-efficiency value of a device, calculate the product of the cross-efficiency value of the device and the optimal weight, and obtain the average value of the sum of the product and the production efficiency value of the device as the average efficiency value of the device. Calculate the average efficiency values of other devices in turn according to the above method.

[0100] It should be noted that the average efficiency values of multiple devices are obtained by predicting multiple devices in sequence. For example, if there are d devices such as W1, W2, W3,..., Wd on a production line, if it is necessary to predict the average efficiency value of device W1, first obtain the operation parameter data and production parameter data in the production status of device W1 in the past week. The operation parameter data includes the overall equipment effectiveness, the quantity and cost of consumables, the alarm frequency, the maintenance cost, the power consumption, and the average production time of the equipment. The production parameter data includes the output data.

[0101] Then, input the seven parameter data of the comprehensive efficiency, consumable quantity cost, alarm frequency, maintenance cost, power consumption, average production time, and production volume of device W1 obtained into the preprocessed efficiency prediction model to obtain an efficiency value range of device W1. The efficiency value range includes seven different efficiency values. Sort the seven different efficiency values within the efficiency value range of device W1, and select the largest efficiency value within the efficiency value range of device W1 as the production efficiency value of device W1, and the remaining efficiency values as the efficiency weight values of device W1, thereby obtaining one production efficiency value and six efficiency weight values of device W1.

[0102] Then, calculate the average value of the sum of the six different efficiency weight values of device W1 to obtain the cross-efficiency value of device W1. Select the optimal weight of the cross-efficiency value of device W1, calculate the product of the cross-efficiency value of device W1 and the optimal weight, and obtain the average value of the sum of the product and the production efficiency value of device W1 to obtain the average efficiency value of device W1.

[0103] In the above manner, the average efficiency values of devices W2, W3, …… Wd can be calculated in sequence.

[0104] This application performs production scheduling based on historical data such as multiple operation parameter data and production parameter data of the device, can reasonably use the device, and helps to improve production efficiency.

[0105] S13. Obtain target order data, and determine the total production quantity and total production time corresponding to the target order.

[0106] Taking the order demand received on the production line as an example of the order quantity to be completed on the same day, first obtain the target order data to be completed on the same day, parse the target order data, extract the total production quantity to be completed on the same day corresponding to the target order, and calculate the total production time corresponding to the target order according to the total production quantity and the processing time of a single quantity.

[0107] S14. According to the operation parameter data of the multiple devices, obtain the average production time of the multiple devices within a set historical time period.

[0108] Taking the operation parameter data of each device including six parameter data of the comprehensive efficiency of the device, consumable quantity cost, alarm frequency, maintenance cost, power consumption, and the average production time of the device, and the set historical time period as one week as an example, based on the operation parameter data of each device in the past week, obtain the average production time of each device in the past week.

[0109] S15, based on the total production quantity, the total production time, the average production time of the multiple devices and the average efficiency value of the multiple devices, screen out the devices that meet the expectations, and determine the production quantity corresponding to the devices that meet the expectations.

[0110] In some embodiments, Figure 6 As shown, the method of screening out the devices that meet the expectations and determining the production quantity corresponding to the devices that meet the expectations includes:

[0111] S151, calculating the average of the sum of the average production time of the plurality of devices within a set historical time period, and obtaining the average production time corresponding to all the devices within the set historical time period.

[0112] For example, in the past week, among d devices W1, W2, W3, ... Wd, the average production time of devices W1, W2, W3, ... Wd is T1, T2, T3, ... Td respectively. Then the average production time of all devices in the past week is T P =(T1+T2+T3+……+Td) / d.

[0113] S152, calculating the number of devices required for the target order according to the total production time corresponding to the target order and the average production time corresponding to all devices.

[0114] Assume that the total production time corresponding to the target order is T Z , the number of devices required for the target order L = T Z / T P , where T P is the average production time corresponding to all equipment.

[0115] S153, based on the arrangement result of the average efficiency values ​​of the multiple devices and according to the number of devices required by the target order, select devices with high efficiency that meet expectations from the multiple devices.

[0116] Among them, the equipment that meets expectations refers to one or more equipment with a higher average efficiency value among multiple equipment, and the number of equipment that meets expectations is equal to the number of equipment required for the target order.

[0117] For example, after predicting the average efficiency values ​​of the multiple devices, the average efficiency values ​​of the multiple devices are further compared, and the average efficiency values ​​of the multiple devices are arranged according to a gradually decreasing rule, and the L devices with the highest average efficiency values ​​are selected as the expected devices for production.

[0118] For example, the average efficiency values of devices W1, W2, W3... Wd are sorted according to the rule of gradually decreasing, and the top L devices are selected from devices W1, W2, W3... Wd as the devices meeting the expectations.

[0119] S154. Calculate the production quantity of the devices meeting the expectations according to the total production quantity corresponding to the target order and the average production time corresponding to the devices meeting the expectations.

[0120] In some embodiments, according to the total production quantity corresponding to the target order and the average production time corresponding to the devices meeting the expectations in the past week, arrange the production quantity for the devices meeting the expectations. Produce through the devices with high efficiency to complete the target order.

[0121] Thus, this application uses the operation parameter data and production parameter data of multiple devices in the past period of time to construct an efficiency prediction model to calculate the average efficiency value of each device, and then compares the average efficiency values of each device to select the devices meeting the expectations, so that when arranging the order production quantity, it can be segmented to the device level, and try to use the devices meeting the expectations for production, improving production efficiency and enhancing product output and quality.

[0122] As Figure 7 shown, the embodiment of this application further provides a production arrangement device 200. The production arrangement device 200 includes: a communicator 20, configured to receive the operation parameter data and production parameter data of multiple devices within a set historical time period, and further configured to receive target order data; a second processor 21, coupled to the communicator 20, configured to respectively predict the average efficiency values of the multiple devices according to the received operation parameter data and production parameter data of the multiple devices; determine the total production quantity and total production time corresponding to the target order according to the received target order data; obtain the average production time of the multiple devices within the set historical time period according to the operation parameter data of the multiple devices; based on the total production quantity, the total production time, the average production time of the multiple devices, and the average efficiency values of the multiple devices, select the devices meeting the expectations and determine the production quantity corresponding to the devices meeting the expectations.

[0123] The first processor 10 and the second processor 21 may be a Central Processing Unit (CPU), and may also include other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center, connecting each part using various interfaces and lines.

[0124] The computer-readable program instructions may be downloaded from a computer-readable storage medium to the corresponding computing processing device, or downloaded to an external computer or external storage device or a wireless network through a network (e.g., the Internet, a local area network, a wide area network, and a network). The network may include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium within each computing processing device.

[0125] The computer-readable program instructions for performing the operations of the present application of the present invention may be assembly program instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, configuration data of an integrated circuit, or source code or object code written in any combination of one or more programming languages (including object-oriented programming languages such as Smalltalk, C++, etc.) and procedural programming languages (such as the "C" programming language or similar programming). The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network (including a local area network (LAN) or a wide area network (WAN)), or may establish a connection with an external computer (for example, by using the Internet of an Internet service provider). In some embodiments, an electronic circuit including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) may personalize the computer-readable program instructions by utilizing the status information of the computer-readable program instructions.

[0126] Reference is now made to the flowcharts and / or block diagrams of methods, apparatuses, and computer program products according to embodiments of the present application to describe aspects of the present application. It will be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0127] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions are executed via the processor of the computer or other programmable data processing. In the apparatus, means for implementing the functions / actions specified in the blocks of the flowcharts and / or block diagrams are created. These computer-readable program instructions can also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer-readable storage medium in which the instructions are stored. The computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer-implemented process, e.g., the instructions executed on the computer, other programmable apparatus, or other devices implement the functions / actions specified in the flowcharts and / or block diagrams.

[0128] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, segment, or portion of instructions, which includes one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, depending on the functionality involved, two blocks shown in succession may in fact be executed substantially concurrently, or sometimes the blocks may be executed in the reverse order. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated system based on hardware that performs the specified functions or actions or is based on a particular purpose.

[0129] The description of the various embodiments of the present application has been presented for purposes of illustration, but is not intended to be exhaustive or to limit the present application to the disclosed form. Many modifications and variations will be apparent to a person of ordinary skill in the art without departing from the scope and spirit of the present application. The embodiments were chosen and described in order to best explain the principles of the present application and its practical application, and to enable others of ordinary skill in the art to understand the present application in various embodiments having various modifications suitable for the particular purposes contemplated.

Claims

1. A method for predicting equipment efficiency, comprising: Obtaining historical operation parameter data and historical production parameter data of the equipment, where the historical operation parameter data includes equipment overall efficiency, consumable quantity cost, alarm frequency, maintenance cost, power consumption, average production time of the equipment, and the historical production parameter data includes output or yield; Calculating the production efficiency value and multiple different efficiency weight values of the equipment according to the historical operation parameter data and historical production parameter data of the equipment; Estimating the cross - efficiency value of the equipment based on the multiple different efficiency weight values of the equipment; Determining the average efficiency value of the equipment by using the production efficiency value and cross - efficiency value of the equipment; The step of calculating the production efficiency value and multiple different efficiency weight values of the equipment includes: Constructing an efficiency prediction model and pre - processing the efficiency prediction model. Using the data envelopment analysis method to construct a DEA - CCR model, and taking the DEA - CCR model as the efficiency prediction model; Inputting the historical operation parameter data and historical production parameter data of the equipment into the pre - processed efficiency prediction model to obtain an efficiency value interval of the equipment, where the efficiency value interval includes multiple different efficiency values; Sorting the multiple different efficiency values within the efficiency value interval to obtain a sorting result; Based on the sorting result, taking the largest efficiency value within the efficiency value interval as the production efficiency value, and the remaining efficiency values as efficiency weight values; The step of estimating the cross - efficiency value of the equipment includes: Calculating the average value of the sum of the multiple different efficiency weight values of the equipment to obtain the cross - efficiency value of the equipment; The step of determining the average efficiency value of the equipment includes: Selecting the number of historical operation parameters input into the efficiency prediction model as the optimal weight of the cross - efficiency value of the equipment; Calculating the product of the cross - efficiency value of the equipment and the optimal weight, and obtaining the average value of the sum of the product and the production efficiency value of the equipment to obtain the average efficiency value of the equipment.

2. An electronic device, comprising a first processor and a storage medium, where the storage medium includes readable instructions for being executed by the first processor to perform the equipment efficiency prediction method as claimed in claim 1.

3. A production scheduling method, comprising: Obtaining operation parameter data and production parameter data of multiple equipment within a set historical time period, where the operation parameter data includes equipment overall efficiency, consumable quantity cost, alarm frequency, maintenance cost, power consumption, average production time of the equipment, and the production parameter data includes output or yield; Respectively predicting the average efficiency values of the multiple equipment according to the operation parameter data and production parameter data of the multiple equipment; Obtaining target order data, and determining the total production quantity and total production time corresponding to the target order; According to the operation parameter data of the multiple equipment, obtaining the average production time of the multiple equipment within the set historical time period; Based on the total production quantity, the total production time, the average production time of the multiple equipment, and the average efficiency values of the multiple equipment, screening out the equipment that meets the expectations, and determining the production quantity corresponding to the equipment that meets the expectations; The step of predicting the average efficiency values of the multiple devices includes: Calculating the production efficiency value and multiple different efficiency weight values of each device respectively according to the operation parameter data and production parameter data of the multiple devices; Estimating the cross-efficiency value of each device based on the multiple different efficiency weight values of each device; Determining the average efficiency value of each device by using the production efficiency value and cross-efficiency value of each device; The step of calculating the production efficiency value and multiple different efficiency weight values of the device includes: Constructing an efficiency prediction model, preprocessing the efficiency prediction model, constructing a DEA-CCR model by using the data envelopment analysis method, and using the DEA-CCR model as the efficiency prediction model; Inputting the historical operation parameter data and historical production parameter data of the device into the preprocessed efficiency prediction model to obtain an efficiency value interval of the device, where the efficiency value interval includes multiple different efficiency values; Sorting the multiple different efficiency values within the efficiency value interval to obtain a sorting result; Based on the sorting result, taking the maximum efficiency value within the efficiency value interval as the production efficiency value, and taking the remaining efficiency values as the efficiency weight values; The step of estimating the cross-efficiency value of the device includes: Calculating the average value of the sum of the multiple different efficiency weight values of the device to obtain the cross-efficiency value of the device; The step of determining the average efficiency value of the device includes: Selecting the number of historical operation parameters input into the efficiency prediction model as the optimal weight of the cross-efficiency value of the device; Calculating the product of the cross-efficiency value of the device and the optimal weight, and obtaining the average value of the sum of the product and the production efficiency value of the device to obtain the average efficiency value of the device.

4. The production scheduling method according to claim 3, after predicting the average efficiency values of the plurality of devices, further includes: Comparing the magnitudes of the average efficiency values of the multiple devices, and arranging the average efficiency values of the multiple devices in a gradually decreasing order.

5. The production scheduling method according to claim 4, the step of determining the total production quantity and total production time corresponding to the target order includes: Parsing the target order data to extract the total production quantity corresponding to the target order; Calculating the total production time corresponding to the target order according to the total production quantity and the processing time of a single quantity.

6. The production scheduling method according to claim 5, the step of screening out the devices that meet the expectations and determining the production quantity corresponding to the devices that meet the expectations includes: Calculating the average value of the sum of the average production times of the multiple devices within a set historical time period to obtain the average production time corresponding to all devices within the set historical time period; Calculating the number of devices required for the target order according to the total production time corresponding to the target order and the average production time corresponding to all devices; Based on the arrangement result of the average efficiency values of the multiple devices, screening out the required number of devices that meet the expectations and rank among the top from the multiple devices; Calculating the production quantity of the devices that meet the expectations by using the total production quantity corresponding to the target order and the average production time corresponding to the devices that meet the expectations.

7. A production scheduling device includes: A communicator, configured to receive operation parameter data and production parameter data of multiple devices within a set historical time period, and further configured to receive target order data. The operation parameter data includes overall equipment effectiveness, quantity cost of consumables, alarm frequency, maintenance cost, power consumption, and average production time of the devices. The production parameter data includes production quantity or yield. A second processor, coupled to the communicator, configured to predict average efficiency values of the multiple devices respectively according to the received operation parameter data and production parameter data of the multiple devices. According to the received target order data, determine the total production quantity and total production time corresponding to the target order. According to the operation parameter data of the multiple devices, obtain the average production time of the multiple devices within the set historical time period; based on the total production quantity, the total production time, the average production time of the multiple devices, and the average efficiency values of the multiple devices, screen out the devices that meet the expectations, and determine the production quantity corresponding to the devices that meet the expectations. The step of predicting the average efficiency values of the multiple devices includes: respectively calculating the production efficiency value and multiple different efficiency weight values of each device according to the operation parameter data and production parameter data of the multiple devices; estimating the cross-efficiency value of each device based on the multiple different efficiency weight values of each device; using the production efficiency value and cross-efficiency value of each device to determine the average efficiency value of each device. The step of calculating the production efficiency value and multiple different efficiency weight values of the device includes: Construct an efficiency prediction model and preprocess the efficiency prediction model. Use the data envelopment analysis method to construct a DEA-CCR model, and use the DEA-CCR model as the efficiency prediction model. Input the historical operation parameter data and historical production parameter data of the device into the preprocessed efficiency prediction model to obtain an efficiency value interval of the device, where the efficiency value interval includes multiple different efficiency values. Sort the multiple different efficiency values within the efficiency value interval to obtain a sorting result. Based on the sorting result, use the largest efficiency value within the efficiency value interval as the production efficiency value, and the remaining efficiency values as the efficiency weight values. The step of estimating the cross-efficiency value of the device includes: Calculate the average value of the sum of the multiple different efficiency weight values of the device to obtain the cross-efficiency value of the device. The step of determining the average efficiency value of the device includes: Select the number of historical operation parameters input into the efficiency prediction model as the optimal weight of the cross-efficiency value of the device. Calculate the product of the cross-efficiency value of the device and the optimal weight, and obtain the average value of the sum of the product and the production efficiency value of the device to obtain the average efficiency value of the device.

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