A production efficiency optimization method, device and medium for an SMT production line

Abstract

The embodiment of the specification discloses a production efficiency optimization method, device and medium for an SMT production line, relates to the surface mounting technology field, and the method comprises the following steps: collecting a plurality of device real-time running data through an Internet of Things device arranged at each production device in the SMT production line, obtaining a production contribution coefficient of each production device in the SMT production line, performing key device screening in the plurality of production devices based on the production contribution coefficient of each production device, determining at least one key production device, obtaining a failure probability change curve of each key production device according to the running parameter data of the key production device, and determining a stability factor of each key production device at the current moment; obtaining initial production plan data to generate an analysis time window; generating supply efficiency of an upstream device and production efficiency of a downstream device; and adjusting device production parameters according to the supply efficiency of the upstream device and the production efficiency of the downstream device, so as to realize production efficiency optimization.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of surface mount technology, and in particular, to a production efficiency optimization method, device and medium for an SMT production line. BACKGROUND

[0002] With the rapid development of the electronic assembly industry, the electronic manufacturing industry pays more and more attention to the production efficiency of the surface mount technology (SMT) production line. SMT is an electronic assembly technology that automatically mounts electronic components on a printed circuit board (PCB) and forms a good mechanical and electrical connection between the components and the PCB through welding. The production line is mainly composed of a board loading machine, a printing device, a placement machine, a reflow soldering device, and a quality detection device.

[0003] The entire production process is a pipeline operation, in which the placement process of the placement machine is a key process. The production efficiency of the placement machine is closely related to the production efficiency of the entire production line. The equipment condition of the placement machine affects the placement quality. In addition to the influence of the placement machine itself, the placement speed is also affected by the upstream equipment of the production line. For example, when the printing speed of the printing device is low, the placement machine is idle and waits, wasting equipment resources. On the contrary, if the production efficiency of the upstream equipment is high, the workload of the placement machine per unit time increases, and the equipment wear and tear increases, and the risk of failure is correspondingly larger. Therefore, the production efficiency of the entire production process is affected by each production equipment of the production line, and the running parameters of the production equipment need to be optimized in a timely manner.

[0004] The prior art usually only considers the production efficiency of the core production equipment when optimizing the production efficiency of the production line, without considering the influence of the upstream and downstream equipment on the core production equipment, resulting in one-sidedness in the optimization of production efficiency. SUMMARY

[0005] One or more embodiments of the present specification provide a production efficiency optimization method, device and medium for an SMT production line, which solves the technical problem that the prior art usually only considers the production efficiency of the core production equipment when optimizing the production efficiency of the production line, without considering the influence of the upstream and downstream equipment on the core production equipment, resulting in one-sidedness in the optimization of production efficiency.

[0006] One or more embodiments of the present specification adopt the following technical solutions:

[0007] The one or more embodiments of the specification provide a production efficiency optimization method for an SMT production line, characterized in that the method comprises: collecting a plurality of device real-time running data through Internet of Things devices arranged at each production device in the SMT production line, wherein the real-time running data comprises running parameter data of the production device in a processing process and output quality data of an output process, and the production device comprises a plate feeding device, a production processing device, and a quality detection device; obtaining a pre-set production contribution coefficient of each production device in the SMT production line, to perform key device screening among the plurality of production devices based on the production contribution coefficient of each production device, and determine at least one key production device, wherein the production contribution coefficient is used for the contribution degree of the production device in the product production process of the SMT production line; performing real-time prediction of a running state according to the running parameter data of the key production device in the device real-time running data, to obtain a failure probability change curve of each key production device, to determine a stability factor of each key production device at a current time based on the failure probability change curve and the output quality data corresponding to each key production device; when there is a specified key production device whose stability factor at a specified time is lower than a pre-set stability expectation threshold, obtaining initial production plan data of the SMT production line, generating an analysis time window based on an input production start time in the initial production plan data and the specified time; performing production state analysis on upstream and downstream devices of the specified key production device based on a plurality of device running data in the analysis time window, to generate a supply efficiency of the upstream device and a production efficiency of the downstream device in the analysis time window; and performing device optimization on the production device according to the supply efficiency of the upstream device and the production efficiency of the downstream device, to adjust device production parameters, so as to realize production efficiency optimization of the SMT production line.

[0008] Further, the production contribution coefficient of each production device in the SMT production line is acquired, specifically including: acquiring a plurality of production processes of the SMT production line, performing contribution degree average distribution on each production process according to a preset rule to determine the initial contribution degree of each production process, wherein the production process includes a material supply process, a processing process, and a quality inspection process, and the processing process includes a printing process, a patching process, and a welding process; determining the corresponding belonging production process of each production device according to the pre-acquired device information of each production device, wherein the device information includes the device function type of each production device, and the device function type includes a board loading type, a production processing type, and a quality detection type, each production process corresponds to a production device group, and the production device group includes a plurality of production devices; determining the number of devices in the production device group corresponding to each production process; correcting the initial contribution degree based on the number of devices in the production device group corresponding to each production process to generate the production contribution coefficient of each production device in the SMT production line, wherein the number of devices and the production contribution coefficient are negatively correlated.

[0009] Further, based on the failure probability change curve and the output quality data corresponding to each key production device, the stability factor of each key production device at the current time is determined, specifically including: acquiring the current failure probability point, the initial failure probability point, and the reference failure probability point set in the failure probability change curve of the key production device, wherein the reference failure probability point set includes any one or more of the maximum value probability point and the minimum value probability point; when the current failure probability corresponding to the current failure probability point is greater than the initial failure probability corresponding to the initial failure probability point, and greater than the failure probability corresponding to the reference failure probability point set, determining the line connecting the initial failure probability point and the current failure probability point as a first reference line; determining the slope of the first reference line to determine the operation stability factor of the key production device based on the slope of the first reference line; acquiring a plurality of output quality data of each key production device within a preset time period, performing data analysis on the plurality of output quality data to determine the waste product output rate of each key production device, and determining the quality stability factor of the key production device based on the waste product output rate; and determining the stability factor of the key production device through the operation stability factor of the key production device and the quality stability factor of the key production device.

[0010] Further, based on the multiple device running data within the analysis time window, production state analysis is respectively performed on the upstream device and the downstream device of the specified key production device to generate the supply efficiency of the upstream device and the production efficiency of the downstream device within the analysis time window, specifically including: performing output state analysis on the upstream device of the specified key production device to generate output supply data provided by the upstream device to the specified key production device within the analysis time window; determining the supply efficiency of the upstream device through the output supply data; performing material supply analysis on the downstream device of the specified key production device to generate material supply data provided by the specified key production device to the downstream device within the analysis time window; and determining the production efficiency of the downstream device through the material supply data.

[0011] Further, before the production state analysis is respectively performed on the upstream device and the downstream device of the specified key production device based on the multiple device running data within the analysis time window to generate the supply efficiency of the upstream device and the production efficiency of the downstream device within the analysis time window, the method further includes: obtaining a pre-constructed production process structure model corresponding to the SMT production line, wherein the production process structure model includes multiple process nodes and single-direction connection links composed of adjacent process nodes, and each process node corresponds to a production process; determining the belonging production process corresponding to each production device according to pre-obtained device information of each production device, wherein the device information includes the device function type of each production device, and the device function type includes the panel type, the production processing type and the quality detection type; adding the device identifier of each production device at the corresponding process node in the production process structure model according to the belonging production process corresponding to each production device to generate a production process updated structure model; determining the key device identifier of the specified key production device, and based on the key device identifier, determining the specified process node corresponding to the specified key production device in the production process updated structure model; and determining two adjacent process nodes adjacent to the specified process node according to the multiple single-direction connection links in the production process updated structure model and the specified process node to determine the upstream device and the downstream device of the specified key production device.

[0012] Further, according to the supply efficiency of the upstream device and the production efficiency of the downstream device, the production device is optimized and the device production parameter is adjusted, specifically including: determining a remaining production time period based on the initial production plan data; determining a raw material backlog of the key production device in the remaining time period and a raw material demand gap of the downstream device according to the supply efficiency of the upstream device, the production efficiency of the downstream device and the remaining production time period; and optimizing the production device and adjusting the device production parameter through the raw material backlog and the raw material demand gap, wherein the device production parameter includes device running speed and device quantity.

[0013] Further, according to the supply efficiency of the upstream device, the production efficiency of the downstream device and the remaining production time period, the raw material backlog of the key production device in the remaining time period and the raw material demand gap of the downstream device are determined, specifically including: determining a predicted output of the upstream device in the remaining production time period based on the supply efficiency of the upstream device; determining a predicted demand of the downstream device in the remaining production time period according to the production efficiency of the downstream device; determining a current processing efficiency of the specified key production device according to the current running parameter data of the specified key production device in the processing process, so as to determine a material demand and a processing output of the specified key production device in the remaining production time period based on the current processing efficiency; calculating the raw material backlog of the key production device in the remaining time period through the predicted output of the upstream device in the remaining production time period and the material demand of the specified key production device in the remaining production time period; and determining the raw material demand gap of the downstream device through the predicted demand of the downstream device in the remaining production time period and the processing output.

[0014] Further, before the production flow structure model corresponding to the SMT production line is obtained, the method further includes: constructing a flow node corresponding to each production flow according to a plurality of production flows of the SMT production line, wherein the flow node includes a material supply flow node, a processing flow node and a quality inspection flow node, the processing flow includes a printing flow sub-node, a patch flow sub-node and a welding flow sub-node; obtaining a flow execution sequence between a plurality of production flows to construct a plurality of unidirectional connection links between a plurality of flow nodes; and constructing the production flow structure model corresponding to the SMT production line according to the flow node corresponding to each production flow and the plurality of unidirectional connection links between a plurality of flow nodes.

[0015] One or more embodiments of the present specification provide a production efficiency optimization device for an SMT production line, including:

[0016] at least one processor; and

[0017] a memory communicatively connected with the at least one processor; wherein

[0018] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above method.

[0019] One or more embodiments of the present specification provide a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are configured to:

[0020] Through the Internet of Things devices arranged at each production equipment in the SMT production line, real-time running data of multiple devices is collected, wherein the real-time running data includes running parameter data of the production equipment in the processing process and output quality data of the output process, and the production equipment includes a plate feeding device, a production processing device and a quality detection device; a pre-set production contribution coefficient of each production equipment in the SMT production line is obtained, so as to perform key equipment screening in multiple production equipment based on the production contribution coefficient of each production equipment, and determine at least one key production equipment, wherein the production contribution coefficient is used for the contribution degree of the production equipment in the product production process of the SMT production line; running parameter data of the key production equipment in the device real-time running data is used for real-time prediction of the running state, so as to obtain a failure probability change curve of each key production equipment, so as to determine a stability factor of each key production equipment at the current moment based on the failure probability change curve and the output quality data corresponding to each key production equipment; when the stability factor of a specified key production equipment at a specified moment is lower than a pre-set stability expectation threshold, initial production plan data of the SMT production line is obtained, an analysis time window is generated based on the input production start moment in the initial production plan data and the specified moment; based on the multiple device running data in the analysis time window, production state analysis is performed on the upstream equipment and the downstream equipment of the specified key production equipment respectively, and the supply efficiency of the upstream equipment and the production efficiency of the downstream equipment in the analysis time window are generated; based on the supply efficiency of the upstream equipment and the production efficiency of the downstream equipment, device optimization is performed on the production equipment, and device production parameters are adjusted, so as to realize production efficiency optimization of the SMT production line.

[0021] The above at least one technical solution adopted by the embodiments of the present specification can achieve the following beneficial effects: Through the above technical solution, by combining the Internet of Things technology and the industrial MES system, real-time monitoring and collection of various data in the production site are realized, the production process corresponding to each production equipment in the SMT production line is combined, the contribution degree and importance degree of the production equipment to the production line are quantified, the key production equipment is screened out, and targeted monitoring of the key production equipment is realized, which is suitable for the case that the number of production line equipment is large, and realizes key monitoring and targeted monitoring; the real-time stability of the key production equipment is quantified as a stability factor, which can more intuitively reflect the stability degree of the equipment; subsequent analysis is performed on the specified key production equipment that appears abnormally, which further reduces the calculation amount and realizes targeted analysis; when optimizing the production efficiency of the production line, the influence of upstream and downstream equipment on the core production equipment is considered, the raw material backlog generated by the upstream equipment and the raw material gap of the downstream equipment are combined, and the production efficiency is optimized, which ensures the maximum utilization of each equipment in the production line and avoids the waste of equipment resources due to idle waiting of the equipment; data analysis and optimization decision are performed in combination with the industrial MES system, and the optimization scheme is implemented, so as to improve the production efficiency and quality, optimize the production process, reduce the production cost, improve the intelligentization and automation level of the industrial automation technology, and realize the intelligentization and high efficiency of industrial production. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present specification or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present specification, and other drawings can be obtained by those skilled in the art without creative labor. In the drawings:

[0023] Figure 1 A flowchart of a production efficiency optimization method for an SMT production line provided by the embodiments of the present specification is shown in the figure;

[0024] Figure 2 A structural schematic diagram of a production efficiency optimization device for an SMT production line provided by the embodiments of the present specification is shown in the figure. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be described clearly and completely below with reference to the drawings in the embodiments of the present specification. Obviously, the described embodiments are only some embodiments of the present specification, not all embodiments. Based on the embodiments of the present specification, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present specification.

[0026] With the rapid development of electronic assembly industry, the electronic manufacturing industry pays more and more attention to the production efficiency of the surface mount technology (SMT) production line. SMT is an electronic assembly technology that automatically mounts electronic components on a printed circuit board (PCB) and forms a good mechanical and electrical connection between the components and the PCB through welding. The production line mainly consists of a board loading machine, a printing device, a chip mounter, a reflow soldering device, and a quality detection device.

[0027] The entire production process is a pipeline operation, wherein the chip mounting process of the chip mounter is a key process, and the production efficiency of the chip mounter is closely related to the production efficiency of the entire production line. The equipment condition of the chip mounter affects the chip mounting quality, and the chip mounting speed is affected by not only the chip mounter itself but also the upstream equipment of the production line. For example, when the printing speed of the printing device is low, the chip mounter is likely to be idle and wait, wasting equipment resources. On the contrary, if the production efficiency of the upstream equipment is high, the workload of the chip mounter per unit time increases, the equipment wear and tear increases, and the risk of failure increases accordingly. Therefore, the production efficiency of the entire production process is affected by each production equipment of the production line, and the running parameters of the production equipment need to be optimized in a timely manner.

[0028] The prior art usually only considers the production efficiency of the core production equipment when optimizing the production efficiency of the production line, without considering the influence of the upstream and downstream equipment on the core production equipment, resulting in one-sidedness in the optimization of the production efficiency.

[0029] The embodiments of the present specification provide a production efficiency optimization method for an SMT production line. It should be noted that the execution subject in the embodiments of the present specification can be a server or any device with data processing capability. Figure 1 A flowchart of a production efficiency optimization method for an SMT production line provided by the embodiments of the present specification is shown in Figure 1 The method mainly includes the following steps:

[0030] In step S101, real-time running data of multiple devices is collected through Internet of Things devices arranged at each production equipment in the SMT production line.

[0031] The real-time running data includes running parameter data and output quality data of the production equipment in the processing process, and the production equipment includes a board loading device, a production processing device, and a quality detection device.

[0032] A manufacturing enterprise production process execution system (MES) is a software system used to manage and monitor industrial production processes, and can realize production planning, production process control, data collection and analysis, quality control and other functions. The existing industrial MES system mainly relies on manual input of data and manual analysis of data for production monitoring and optimization, which has the problems of low efficiency, large error, long time-consuming and other problems, and cannot meet the needs of modern industrial production. Through the combination of Internet of Things technology and industrial MES system, real-time monitoring and collection of various data in the production site, combined with industrial MES system for data analysis and optimization decision-making, implementation of optimization scheme, so as to improve production efficiency and quality, optimize production process, reduce production cost, improve the intelligent and automatic level of industrial automation technology, realize the intelligent and efficient of industrial production. Internet of Things technology refers to the use of sensors, communication devices and Internet technology to connect various physical devices to form an Internet-like physical system, realizing automatic collection, transmission, processing and application of information.

[0033] In an embodiment of the present specification, it can be realized through an industrial MES system. First, deploy Internet of Things devices in the production site, the Internet of Things devices here can be a sensor network, connect sensors with industrial devices to realize real-time monitoring and collection of various data in the production site. Through wireless transmission, the data collected by the sensor is transmitted to the industrial MES system to realize real-time transmission and storage of data. In addition, in order to ensure the accuracy of the data, the data transmitted to the industrial MES system needs to be preprocessed, including data cleaning, data format conversion, data verification, etc., to ensure the accuracy and integrity of the data. Through the data analysis function provided by the industrial MES system, the data transmitted to the industrial MES system is analyzed, including production efficiency, production quality, equipment status and other aspects of data analysis. According to the data analysis result, an optimization scheme is formulated, including optimization decisions such as adjusting production plan, improving process flow, maintaining equipment, etc., to improve production efficiency and quality. According to the optimization decision result, the corresponding optimization scheme is implemented, including adjustment of production plan, improvement of process flow, maintenance of equipment, etc. The implemented optimization scheme is monitored and evaluated, including monitoring and evaluation of production efficiency, production quality, etc., to ensure the effectiveness and sustainability of the optimization scheme. According to the effect of the optimization scheme and the production demand, the equipment and production process in the production site are automatically controlled to realize automatic control and optimization of the production process.

[0034] In an embodiment of the present specification, a plurality of real-time operation data of the devices are collected by the Internet of Things devices arranged at each production device in the SMT production line. The SMT production line is an electronic assembly technology that directly mounts electronic components on a printed circuit board (PCB) and forms a good mechanical and electrical connection between the components and the PCB through welding. The production line is mainly composed of a board loading machine, a printing device, a chip mounter, a reflow soldering device, and a quality detection device. The Internet of Things devices are arranged at each production device in the production line to collect real-time operation data of each production device through the Internet of Things devices. The real-time operation data includes operation parameter data of the production device in the processing process and output quality data of the output process. The operation parameter data refers to the operation parameter of the production device in the operation process, which includes the processing parameter in the processing process, such as the suction nozzle pressure, the mounting speed, and the time to complete a task of the chip mounter. The output quality data refers to the number of scrap pieces and mounting errors, which are used to reflect the production effect of the production device. The production device includes a board loading device, a production processing device, and a quality detection device. The production processing device includes at least one core production device, such as a printing device, a chip mounter, and a reflow soldering device.

[0035] In step S102, the production contribution coefficient of each production device in the SMT production line is obtained in advance, and at least one key production device is determined based on the production contribution coefficient of each production device in the key device screening among the plurality of production devices.

[0036] The production contribution coefficient of each production device in the SMT production line is obtained in advance, and specifically includes: obtaining a plurality of production processes of the SMT production line, performing contribution degree average distribution on each production process according to a preset rule, and determining the initial contribution degree of each production process. The production process includes a material supply process, a processing process, and a quality inspection process. The processing process includes a printing process, a chip mounting process, and a soldering process. The device information of each production device is obtained in advance to determine the corresponding production process of each production device. The device information includes the device function type of each production device, which includes the board loading type, the production processing type, and the quality detection type. Each production process corresponds to a production device group, and the production device group includes a plurality of production devices. The number of devices in the production device group corresponding to each production process is determined. The initial contribution degree is corrected based on the number of devices in the production device group corresponding to each production process to generate the production contribution coefficient of each production device in the SMT production line. The number of devices and the production contribution coefficient are negatively correlated.

[0037] In actual application scenarios, multiple production devices exist in the SMT production line, and each production device corresponds to a production process. The production process corresponding to the board loading device is the material supply process, the production process corresponding to the production processing device is the processing process, and the processing process includes the printing process, the patching process, and the welding process in sequence, and the quality monitoring device corresponds to the quality inspection process. According to the actual production process of the SMT production line, a finished product needs to pass through the material supply process, the printing process, the patching process, the welding process, and the quality inspection process of the board loading device in sequence, and each process affects each other. If the material supply process has a problem, it will cause the lack of processing materials and quality inspection materials for the following processing process and quality inspection process. Therefore, the contribution degree of each production process is evenly distributed to obtain the initial contribution degree, and five processes jointly complete a finished product. The contribution degree of each production process can be allocated by 1 / 5, that is, the contribution degree of the five processes is 0.2. Here, the contribution degree of the production process is set, that is, the contribution degree of the production device corresponding to the process is set.

[0038] It should be noted that the setting of the initial contribution degree can also be based on user demand equipment, and the processing production process corresponding to the processing production device needs to be monitored according to historical production line operation experience, and plays an important role in the production line. The initial contribution degree of the production processing device occupies a large proportion. Since the material supply process provides the initial processing materials for the production line, and the quality inspection process is a subsequent guarantee process for the production line, the contribution degree proportion of the board loading type production device is set to be greater than that of the quality detection device. For example, the initial contribution degrees of the board loading device, the production processing device, and the quality detection device are set to be 3:6:1, the proportions of the printing device, the patching device, and the welding device in the production processing device are set to be 1:1:1, the initial contribution degree of the board loading device is 0.3, the initial contribution degree of the quality detection device is set to be 0.1, and the initial contribution degrees of the printing device, the patching device, and the welding device in the production processing device are set to be 0.2.

[0039] The device information of each production device is obtained in advance, and the device information includes device identification, device type, and other data. The device type here is the device function type, that is, the function setting of the production device in the SMT production line, including the board loading type, the production processing type, and the quality detection type. Since the production processing includes multiple processes, the production processing type can also be divided into the printing type, the patching type, and the welding type. According to the device information of each production device, each production device is matched with the production process according to the device function type to determine the belonging production process corresponding to each production device. It should be noted that multiple devices of the same type may work simultaneously in the SMT production line, so each production process corresponds to multiple production devices to form a production device group. The number of devices in the production device group corresponding to each production process is obtained.

[0040] The initial contribution degree is corrected based on the number of devices in the device group corresponding to each production process, and a contribution coefficient corresponding to each production process is generated, to generate a production contribution coefficient of each production device in the SMT production line. It should be noted that a production process corresponds to a production device group, and the contribution coefficient of this production process is the production contribution coefficient of all devices in the production device group of the production process. The number of devices and the production contribution coefficient are negatively correlated. The more the number of devices, the more the number of standby devices available when a device fails, and the smaller the impact on the production line. Therefore, the corresponding production contribution coefficient can be reduced. Here, the difference between the actual number of devices in the production device group and the reference number of devices can be calculated according to the set reference number of devices, and the ratio of the difference to the reference number of devices is taken as the number deviation rate. The number deviation rate is algebraically calculated based on the initial contribution degree. When the actual number of devices is greater than the reference number of devices, the difference between the initial contribution degree and the number deviation rate is calculated as the contribution coefficient of the process. When the actual number of devices is less than the reference number of devices, the sum of the initial contribution degree and the number deviation rate is calculated as the contribution coefficient of the process. The production contribution coefficient is used to quantify the contribution degree and importance of the production device in the product production process of the SMT production line.

[0041] Based on the production contribution coefficient of each production device, at least one key production device is determined by screening the key devices from the plurality of production devices. The production devices greater than the contribution coefficient threshold value can be set as the key devices. Generally, the key devices are usually processing production type devices.

[0042] Through the above technical solution, the contribution degree and importance of the production device to the production line are quantified by combining the production processes corresponding to each production device in the SMT production line, to screen out the key production devices and perform targeted monitoring of the key production devices. The method is suitable for the case where the number of production line devices is large, and realizes focused monitoring and targeted monitoring, avoids data redundancy, and reduces the system processing pressure.

[0043] In step S103, the running state is predicted in real time according to the running parameter data of the key production device in the device real-time running data, to obtain a failure probability change curve of each key production device. Based on the failure probability change curve and the output quality data corresponding to each key production device, a stability factor of each key production device at the current time is determined.

[0044] In one embodiment of the present specification, according to the running parameter data of the key production equipment in the real-time running data of the equipment, the running state real-time prediction is performed to obtain the failure probability of each key production equipment. Here, the running state prediction of the key production equipment can be achieved through a production equipment state prediction model. By constructing a machine learning model, the historical running parameter data of each key production equipment is used to train and optimize the machine model to obtain a production equipment state prediction model to obtain the failure probability at each moment according to the real-time running parameter data. Taking time as the horizontal coordinate and failure probability as the vertical coordinate, the failure probability change curve of each key production equipment is obtained.

[0045] Based on the failure probability change curve and the output quality data corresponding to each key production equipment, the stability factor of each key production equipment at the current moment is determined, specifically including: obtaining the current failure probability point, the initial failure probability point and the reference failure probability point set in the failure probability change curve of the key production equipment, wherein the reference failure probability point set includes any one or more of the maximum value probability point and the minimum value probability point; when the current failure probability corresponding to the current failure probability point is greater than the initial failure probability corresponding to the initial failure probability point, and greater than the failure probability corresponding to the reference failure probability point set, the line connecting the initial failure probability point and the current failure probability point is determined as the first reference line; the slope of the first reference line is determined to determine the running stability factor of the key production equipment based on the slope of the first reference line; a plurality of output quality data of each key production equipment within a preset time period is obtained, the plurality of output quality data is analyzed to determine the waste product output rate of each key production equipment, and the quality stability factor of the key production equipment is determined based on the waste product output rate; the stability factor of the key production equipment is determined by the running stability factor of the key production equipment and the quality stability factor of the key production equipment.

[0046] Specifically, in one embodiment of the present specification, the current failure probability point in the failure probability change curve of the key production equipment, the initial failure probability point and the reference failure probability point set are obtained. The current failure point refers to the failure probability point at the current time, that is, the end point of the failure probability change curve. The initial failure probability point is the failure probability point at the initial time, which can be the time when the current production line starts production. The reference failure probability point set includes any one or more of the maximum value probability point and the minimum value probability point. When the failure change curve shows a steady upward trend, the initial failure probability point is the minimum value probability point, and the current failure probability point is the maximum value probability point. In this embodiment, for the case of non-steady change, that is, the reference failure probability point set includes at least one of the maximum value probability point and the minimum value probability point. When the current failure probability corresponding to the current failure probability point is greater than the initial failure probability corresponding to the initial failure probability point, and greater than the failure probability corresponding to the reference failure probability point set, the line connecting the initial failure probability point and the current failure probability point is determined as the first reference line. The slope of the first reference line is calculated, and the slope of the first reference line is used as the running stability factor of the key production equipment. It should be noted that the greater the slope, the greater the change rate, and the poorer the stability of the corresponding equipment. Therefore, the difference between 1 and the slope can be used as the running stability factor of the key production equipment.

[0047] A plurality of output quality data of each key production equipment within a preset time period is obtained. The preset time period can be a time interval from the start of production in the current production line. Data analysis is performed on the plurality of output quality data to determine the waste product output rate of each key production equipment. The waste product output rate is obtained by the ratio of the waste product output quantity to the total output quantity. Based on the waste product output rate, the quality stability factor of the key production equipment is determined. The higher the waste product output rate, the lower the corresponding quality stability factor. Here, the quality stability factor can be obtained by the difference between 1 and the waste product output rate. The running stability factor of the key production equipment and the quality stability factor of the key production equipment are used to determine the stability factor of the key production equipment. Here, the stability factor of the key production equipment can be obtained by addition, or by setting weights for the running stability factor and the quality stability factor according to the demand, calculating the product of each weight and each stability factor, and then performing addition operation. Here, since the failure probability and the output quality data are real-time data, each real-time data corresponds to a stability factor, that is, the stability factor is used to represent the real-time stability of the key production equipment.

[0048] By the technical solution, the running state of the key production equipment is predicted in real time to obtain a fault probability change curve of each key production equipment, and a stability factor of each key production equipment at the current moment is determined according to the fault probability change curve and output quality data corresponding to each key production equipment, so that the real-time stability of the key production equipment is quantified as the stability factor, and the stability degree of the equipment can be more intuitively reflected.

[0049] In step S104, when the stability factor of the specified key production equipment at the specified moment is lower than the preset stability expectation threshold, initial production plan data of the SMT production line is acquired, and an analysis time window is generated based on the input production start moment and the specified moment in the initial production plan data.

[0050] In one embodiment of the present specification, according to the stability factor of each key production equipment, the equipment is monitored in real time, when it is monitored that the stability factor of the specified key production equipment at the specified moment is lower than the preset stability expectation threshold, the initial production plan data corresponding to the SMT production line is acquired, and a time interval corresponding to the input production start moment and the specified moment in the initial production plan data is taken as an analysis time window. The specified key production equipment with an abnormality is analyzed subsequently, and the calculation amount is further reduced to realize targeted analysis.

[0051] In step S105, based on the multiple equipment running data in the analysis time window, production state analysis is respectively performed on the upstream equipment and the downstream equipment of the specified key production equipment to generate supply efficiency of the upstream equipment and production efficiency of the downstream equipment in the analysis time window.

[0052] Before the production state analysis of the upstream equipment and the downstream equipment of the specified key production equipment is respectively performed based on the multiple equipment operation data within the analysis time window, the method further comprises: obtaining a pre-constructed production process structure model corresponding to the SMT production line, wherein the production process structure model comprises multiple process nodes and unidirectional connection links composed of adjacent process nodes, and each process node corresponds to a production process; determining a belonging production process corresponding to each production equipment according to pre-obtained equipment information of each production equipment, wherein the equipment information comprises a device function type of each production equipment, and the device function type comprises a board loading type, a production processing type and a quality detection type; adding a device identifier of each production equipment at a corresponding process node in the production process structure model according to the belonging production process corresponding to each production equipment, to generate an updated production process structure model; determining a key device identifier of the specified key production equipment, and based on the key device identifier, determining a specified process node corresponding to the specified key production equipment in the updated production process structure model; determining two adjacent process nodes adjacent to the specified process node in the updated production process structure model according to the multiple unidirectional connection links and the specified process node, to determine the upstream equipment and the downstream equipment of the specified key production equipment.

[0053] Specifically, in one embodiment of the present specification, before the pre-constructed production process structure model corresponding to the SMT production line is obtained, a process node corresponding to each production process is constructed according to multiple production processes of the SMT production line, wherein the process node comprises a material supply process node, a processing process node and a quality inspection process node, the processing process comprises a printing process sub-node, a patch process sub-node and a welding process sub-node; a flow execution sequence between multiple production processes is obtained to construct multiple unidirectional connection links between multiple process nodes. Wherein, the material supply process node and the processing process node form a unidirectional connection link in which the material supply process node points to the processing process node, the processing process node and the quality inspection process node form a unidirectional connection link in which the processing process node points to the quality inspection process node, in addition, the printing process sub-node and the patch process sub-node form a unidirectional connection link in which the printing process sub-node points to the patch process sub-node, the patch process sub-node and the welding process sub-node form a unidirectional connection link in which the patch process sub-node points to the welding process sub-node, and the production process structure model corresponding to the SMT production line is constructed according to the process node corresponding to each production process and the multiple unidirectional connection links between multiple process nodes.

[0054] Specifically, in one embodiment of the present specification, a production process structure model corresponding to an SMT production line is acquired, the production process structure model comprising a plurality of process nodes and unidirectional connection links composed of adjacent process nodes, each of the process nodes corresponding to a production process. According to the device function type in the device information of each production device acquired in advance, the production process to which each production device belongs is determined, and the device function type includes the board loading type, the production processing type and the quality detection type. According to the production process to which each production device belongs, the device identifier of the production device is added at the corresponding process node in the production process structure model to generate an updated production process structure model. The key device identifier of the specified key production device is determined, and based on the key device identifier, the specified process node corresponding to the specified key production device is determined in the updated production process structure model. According to the plurality of unidirectional connection links and the specified process node in the updated production process structure model, two adjacent process nodes adjacent to the specified process node are determined to determine the upstream device and the downstream device of the specified key production device. It should be noted that according to the direction of the unidirectional connection link, the upstream device and the downstream device are determined in the two adjacent process nodes. The upstream device refers to the production device in the adjacent process node pointing to the specified process node corresponding to the specified key production device, and the downstream device refers to the production device in the adjacent process node pointed to by the specified process node corresponding to the specified key production device.

[0055] Based on the plurality of device running data within the analysis time window, production state analysis is respectively performed on the upstream device and the downstream device of the specified key production device to generate the supply efficiency of the upstream device and the production efficiency of the downstream device within the analysis time window, specifically including: performing output state analysis on the upstream device of the specified key production device to generate output supply data provided by the upstream device to the specified key production device within the analysis time window; determining the supply efficiency of the upstream device through the output supply data; performing material supply analysis on the downstream device of the specified key production device to generate material supply data provided by the specified key production device to the downstream device within the analysis time window; and determining the production efficiency of the downstream device through the material supply data.

[0056] Specifically, in one embodiment of the present specification, the upstream equipment is used to provide production materials for the designated key production equipment, the designated key production equipment provides production materials for the downstream equipment, therefore, the upstream equipment for the designated key production equipment is analyzed for output state, and output supply data of the upstream equipment for the designated key production equipment in the analysis time window is generated, where the output state analysis refers to analyzing the product output of the upstream equipment in the analysis time window. Through the output supply data, the supply efficiency of the upstream equipment is determined, where the supply efficiency can be obtained by the ratio of the total output and the time length in the analysis time window. The downstream equipment of the designated key production equipment is analyzed for material supply, and material supply data of the designated key production equipment for the downstream equipment in the analysis time window is generated; through the material supply data, the production efficiency of the downstream equipment is determined, where the material supply amount of the downstream equipment can be obtained by the product output of the designated key equipment, and the production efficiency of the downstream equipment can be obtained by the ratio of the material supply amount and the time length of the analysis time window, it should be noted that the production efficiency of the downstream equipment can also be calculated by the actual production situation of the downstream equipment.

[0057] In step S106, the production equipment is optimized according to the supply efficiency of the upstream equipment and the production efficiency of the downstream equipment, and the equipment production parameters are adjusted to realize the production efficiency optimization of the SMT production line.

[0058] Specifically, in one embodiment of the present specification, the production equipment is optimized according to the supply efficiency of the upstream equipment and the production efficiency of the downstream equipment, and the equipment production parameters are adjusted. Based on the planned production time length in the initial production plan data and the analysis time window, the remaining production time period is determined. According to the supply efficiency of the upstream equipment, the production efficiency of the downstream equipment and the remaining production time period, the raw material backlog of the key production equipment in the remaining time period and the raw material demand gap of the downstream equipment are determined.

[0059] According to the supply efficiency of the upstream device, the production efficiency of the downstream device, and the remaining production time period, the raw material backlog of the key production device in the remaining time period and the raw material demand gap of the downstream device are determined, specifically including: based on the supply efficiency of the upstream device, the predicted output of the upstream device in the remaining production time period is determined; according to the production efficiency of the downstream device, the predicted demand of the downstream device in the remaining production time period is determined; according to the current running parameter data of the specified key production device in the processing process, the current processing efficiency of the specified key production device is determined, so as to determine the material demand and the processing output of the specified key production device in the remaining production time period based on the current processing efficiency; the raw material backlog of the key production device in the remaining time period is calculated through the predicted output of the upstream device in the remaining production time period and the material demand of the specified key production device in the remaining production time period; the raw material demand gap of the downstream device is determined through the predicted demand of the downstream device in the remaining production time period and the processing output.

[0060] Specifically, in one embodiment of the present specification, according to the supply efficiency of the upstream device, the production efficiency of the downstream device, and the remaining production time period, the raw material backlog of the key production device in the remaining time period and the raw material demand gap of the downstream device are determined.

[0061] Firstly, based on the supply efficiency of the upstream device, the predicted output of the upstream device in the remaining production time period is determined, which can be obtained by multiplying the supply efficiency by the length of the remaining production time period. Similarly, according to the production efficiency of the downstream device, the predicted demand of the downstream device in the remaining production time period is determined, which can be obtained by multiplying the production efficiency by the length of the remaining production time period. Secondly, according to the current running parameter data of the specified key production device in the processing process, the current processing efficiency of the specified key production device is determined, where the processing efficiency can be obtained by the ratio of the total output to the historical production time, and the product of the current processing efficiency and the length of the remaining production time period is calculated to determine the material demand and the processing output of the specified key production device in the remaining production time period; the raw material backlog of the key production device in the remaining time period is calculated through the difference between the predicted output of the upstream device in the remaining production time period and the material demand of the specified key production device in the remaining production time period. The raw material demand gap of the downstream device is determined through the difference between the predicted demand of the downstream device in the remaining production time period and the processing output of the specified key production device.

[0062] Specifically, in one embodiment of the present specification, the production equipment is optimized by the raw material backlog and the raw material demand gap, and the equipment production parameters are adjusted, wherein the equipment production parameters include the equipment running speed and the equipment quantity. When the production equipment is optimized by the raw material backlog and the raw material demand gap, and the equipment production parameters are adjusted, the to-be-adjusted production of the specified key production equipment can be determined according to the raw material backlog and the raw material demand gap by a back analysis method. Here, the to-be-adjusted production can be obtained by the average of the raw material backlog and the raw material demand gap. Then, the parameters of the production equipment are adjusted with the to-be-adjusted production as the target, so that the production equipment can complete the to-be-adjusted production in the remaining time period. When the equipment production parameters are adjusted, the running parameters of the equipment can be adjusted, such as increasing the running speed of the equipment, or the quantity of the equipment can be adjusted, such as increasing the production equipment of the corresponding type in the production line. When the production efficiency of the production line is optimized, the influence of the upstream and downstream equipment on the core production equipment is considered, and the production efficiency is optimized in combination with the raw material backlog generated by the upstream equipment and the raw material gap of the downstream equipment, so as to maximize the utilization of each equipment in the production line and avoid the waste of equipment resources due to idle waiting of the equipment.

[0063] By the above technical solution, the production field data is monitored and collected in real time by combining the Internet of Things technology and the industrial MES system, the contribution degree and the importance degree of the production equipment to the production line are quantified in combination with the production process corresponding to each production equipment in the SMT production line, the key production equipment is screened out, the targeted monitoring of the key production equipment is performed, the situation of a large number of production line equipment is adapted, the key monitoring and the targeted monitoring are realized, the real-time stability of the key production equipment is quantified as a stability factor, the equipment stability degree can be more intuitively reflected, the subsequent analysis of the specified key production equipment with an abnormality is performed, the calculation amount is further reduced, and the targeted analysis is realized. When the production efficiency of the production line is optimized, the influence of the upstream and downstream equipment on the core production equipment is considered, the production efficiency is optimized in combination with the raw material backlog generated by the upstream equipment and the raw material gap of the downstream equipment, the maximum utilization of each equipment in the production line is ensured, and the waste of equipment resources due to idle waiting of the equipment is avoided. The data analysis and optimization decision are performed in combination with the industrial MES system, the optimization scheme is implemented, and thus the production efficiency and the quality are improved, the production process is optimized, the production cost is reduced, the intelligentization and the automation level of the industrial automation technology are improved, and the intelligentization and the high efficiency of the industrial production are realized.

[0064] The embodiment of the present specification also provides a production efficiency optimization device for an SMT production line, as shown in Figure 2As shown, the device comprises at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above method.

[0065] The embodiment of the present specification also provides a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are configured to:

[0066] By the Internet of Things device arranged at each production device in the SMT production line, a plurality of device real-time running data are collected, wherein the real-time running data includes running parameter data of the production device in the processing process and output quality data of the output process, and the production device includes a plate feeding device, a production processing device and a quality detection device; a pre-set production contribution coefficient of each production device in the SMT production line is acquired, so as to perform key device screening in the plurality of production devices based on the production contribution coefficient of each production device, and at least one key production device is determined, wherein the production contribution coefficient is used for the contribution degree of the production device in the product production process of the SMT production line; the running parameter data of the key production device in the device real-time running data is used for real-time prediction of the running state, so as to obtain a failure probability change curve of each key production device, so as to determine a stability factor of each key production device at the current moment based on the failure probability change curve and the output quality data corresponding to each key production device; when the stability factor of a specified key production device at a specified moment is lower than a pre-set stability expectation threshold value, initial production plan data of the SMT production line is acquired, an analysis time window is generated based on the input production start moment in the initial production plan data and the specified moment; based on the plurality of device running data in the analysis time window, production state analysis is performed on the upstream device and the downstream device of the specified key production device respectively, and the supply efficiency of the upstream device and the production efficiency of the downstream device in the analysis time window are generated; and the production device is optimized according to the supply efficiency of the upstream device and the production efficiency of the downstream device, and the device production parameter is adjusted, so as to realize production efficiency optimization of the SMT production line.

[0067] Each of the embodiments in the present specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment mainly describes the difference from other embodiments. Especially, for the device, equipment and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0068] The above described embodiments of the present specification. Other embodiments are within the scope of the following claims. In some cases, the acts or steps recited in the claims can be performed in a different order than those in the embodiments and still achieve desirable results. Additionally, the processes depicted in the accompanying figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous or necessary.

[0069] The device and medium provided by the embodiments of the present specification are one-to-one corresponding with the method, therefore, the device and medium also have similar beneficial technical effects as the method corresponding thereto, since the beneficial technical effects of the method have been described in detail above, therefore, the beneficial technical effects of the device and medium will not be described here again.

[0070] Those skilled in the art will understand that the embodiments of the present specification can be provided as a method, system, or computer program product. Therefore, the present specification can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROMs, optical storage devices, etc.) containing computer usable program code.

[0071] The present specification is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as a combination of flows and / or blocks in the flowcharts and / or block diagrams, 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 apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the function specified by one or more blocks.

[0072] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0073] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0074] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0075] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), flash memory, or a combination of non-volatile memories in different types. The memory is an example of computer readable storage media.

[0076] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as 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 programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to computing devices. According to the definition herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0077] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to encompass a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0078] ​​The above merely provides one or more embodiments of the present specification and is not intended to limit the present specification. One of ordinary skill in the art can make various modifications and changes to one or more embodiments of the present specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present specification should be included in the scope of claims of the present specification.

Claims

1. A production efficiency optimization method for an SMT production line, characterized by, The method comprises: Collecting multiple device real-time running data through Internet of Things devices arranged at each production device in the SMT production line, wherein the real-time running data comprises running parameter data of the production device in the processing process and output quality data of the output process, and the production device comprises a plate loading device, a production processing device and a quality detection device; Obtaining a pre-set production contribution coefficient of each production device in the SMT production line to perform key device screening among multiple production devices based on the production contribution coefficient of each production device, and determine at least one key production device, wherein the production contribution coefficient is used to indicate the contribution degree of the production device in the product production process of the SMT production line; Performing real-time prediction of the running state according to the running parameter data of the key production device in the device real-time running data, obtaining a failure probability change curve of each key production device, and determining a stability factor of each key production device at the current moment based on the failure probability change curve and the output quality data corresponding to each key production device; When the stability factor of a specified key production device at a specified moment is lower than a pre-set stability expectation threshold, obtaining initial production plan data of the SMT production line, generating an analysis time window based on the input production start moment in the initial production plan data and the specified moment; Performing production state analysis on the upstream device and the downstream device of the specified key production device based on multiple device running data in the analysis time window, and generating supply efficiency of the upstream device and production efficiency of the downstream device in the analysis time window; Performing device optimization on the production device according to the supply efficiency of the upstream device and the production efficiency of the downstream device, and adjusting device production parameters to realize production efficiency optimization of the SMT production line.

2. The production efficiency optimization method for an SMT production line according to claim 1, characterized in that, Obtaining a pre-set production contribution coefficient of each production device in the SMT production line, specifically comprising: Obtaining multiple production processes of the SMT production line, performing contribution degree average distribution on each production process according to a pre-set rule, and determining an initial contribution degree of each production process, wherein the production process comprises a material supply process, a processing process and a quality inspection process, and the processing process comprises a printing process, a patching process and a welding process; Determining a corresponding belonging production process of each production device according to pre-obtained device information of each production device, wherein the device information comprises a device function type of each production device, the device function type comprises a plate loading type, a production processing type and a quality detection type, each production process corresponds to a production device group, and the production device group comprises multiple production devices; Determining the number of devices in the production device group corresponding to each production process; Based on the number of devices in the production device group corresponding to each production process, correcting the initial contribution degree to generate a production contribution coefficient of each production device in the SMT production line, wherein the number of devices and the production contribution coefficient are negatively correlated.

3. The production efficiency optimization method for an SMT production line according to claim 1, characterized in that, Determine a stability factor of each of the key production devices at the current time based on the failure probability change curve and the output quality data corresponding to each of the key production devices, specifically comprising: Obtain a current failure probability point, an initial failure probability point, and a reference failure probability point set in the failure probability change curve of the key production device, wherein the reference failure probability point set includes any one or more of a maximum value probability point and a minimum value probability point; When the current failure probability corresponding to the current failure probability point is greater than the initial failure probability corresponding to the initial failure probability point, and greater than the failure probability corresponding to the reference failure probability point set, determine a line connecting the initial failure probability point and the current failure probability point as a first reference line; Determine the slope of the first reference line to determine the operation stability factor of the key production device based on the slope of the first reference line; Obtain a plurality of output quality data of each of the key production devices within a preset time period, perform data analysis on the plurality of output quality data, determine a waste product output rate of each of the key production devices, and determine a quality stability factor of the key production device based on the waste product output rate; Determine the stability factor of the key production device through the operation stability factor of the key production device and the quality stability factor of the key production device.

4. The production efficiency optimization method for an SMT production line according to claim 1, characterized in that, Based on the plurality of device operation data within the analysis time window, perform production state analysis on the upstream device and the downstream device of the specified key production device respectively to generate the supply efficiency of the upstream device and the production efficiency of the downstream device within the analysis time window, specifically comprising: Perform output state analysis on the upstream device of the specified key production device to generate output supply data provided by the upstream device to the specified key production device within the analysis time window; Determine the supply efficiency of the upstream device through the output supply data; Perform material supply analysis on the downstream device of the specified key production device to generate material supply data provided by the specified key production device to the downstream device within the analysis time window; Determine the production efficiency of the downstream device through the material supply data.

5. The production efficiency optimization method for an SMT production line according to claim 1, characterized in that, Before generating the supply efficiency of the upstream device and the production efficiency of the downstream device within the analysis time window based on the plurality of device operation data within the analysis time window, the method further comprises: Obtain a pre-constructed production process structure model corresponding to the SMT production line, wherein the production process structure model includes a plurality of process nodes and a unidirectional connection link composed of adjacent process nodes, and each process node corresponds to a production process; Determine the belonging production process corresponding to each production device according to pre-obtained device information of each production device, wherein the device information includes the device function type of each production device, and the device function type includes the board loading type, the production processing type, and the quality detection type; According to the production process corresponding to each production equipment, the equipment identifier of the production equipment is added at the corresponding process node in the production process structure model to generate a production process update structure model; Determine the key equipment identifier of the specified key production equipment, and based on the key equipment identifier, determine the specified process node corresponding to the specified key production equipment in the production process update structure model; According to the plurality of unidirectional connection links in the production process update structure model and the specified process node, two adjacent process nodes adjacent to the specified process node are determined to determine the upstream equipment and downstream equipment of the specified key production equipment.

6. The production efficiency optimization method for an SMT production line according to claim 1, characterized by, According to the supply efficiency of the upstream equipment and the production efficiency of the downstream equipment, the equipment optimization of the production equipment is carried out, and the equipment production parameter is adjusted, specifically including: Based on the initial production plan data, the remaining production time period is determined; According to the supply efficiency of the upstream equipment, the production efficiency of the downstream equipment and the remaining production time period, the raw material backlog of the key production equipment in the remaining production time period and the raw material demand gap of the downstream equipment are determined; Through the raw material backlog and the raw material demand gap, the equipment optimization of the production equipment is carried out, and the equipment production parameter is adjusted, wherein the equipment production parameter includes equipment running speed and equipment quantity.

7. The production efficiency optimization method for an SMT production line according to claim 6, characterized in that, According to the supply efficiency of the upstream equipment, the production efficiency of the downstream equipment and the remaining production time period, the raw material backlog of the key production equipment in the remaining production time period and the raw material demand gap of the downstream equipment are determined, specifically including: Based on the supply efficiency of the upstream equipment, the predicted output of the upstream equipment in the remaining production time period is determined; according to the production efficiency of the downstream equipment, the predicted demand of the downstream equipment in the remaining production time period is determined; According to the current running parameter data of the specified key production equipment in the processing process, the current processing efficiency of the specified key production equipment is determined, so as to determine the material demand and processing output of the specified key production equipment in the remaining production time period based on the current processing efficiency; Through the predicted output of the upstream equipment in the remaining production time period and the material demand of the specified key production equipment in the remaining production time period, the raw material backlog of the key production equipment in the remaining production time period is calculated; Through the predicted demand of the downstream equipment in the remaining production time period and the processing output, the raw material demand gap of the downstream equipment is determined.

8. The production efficiency optimization method for an SMT production line according to claim 5, characterized by, Before obtaining the production process structure model corresponding to the SMT production line which is constructed in advance, the method further comprises: According to the plurality of production processes of the SMT production line, the process node corresponding to each production process is constructed, wherein the process node includes material supply process node, processing process node and quality inspection process node, and the processing process includes printing process subnode, patch process subnode and welding process subnode; Obtaining a process execution sequence between a plurality of production processes to construct a plurality of one-way connection links between a plurality of process nodes; According to the process node corresponding to each of the production processes and the plurality of one-way connection links between the plurality of process nodes, a production process structure model corresponding to the SMT production line is constructed.

9. A production efficiency optimization apparatus for an SMT production line, characterized by, The device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of claims 1-8.

10. A non-transitory computer storage medium storing computer-executable instructions, the computer-executable instructions comprising instructions for: receiving a request to access a file; determining whether the file is stored in a cache; and in response to determining that the file is stored in the cache, providing access to the file from the cache. The computer executable instructions are configured to: Collecting a plurality of device real-time running data through an Internet of Things device arranged at each production device in the SMT production line, wherein the real-time running data includes running parameter data of the production device in the processing process and output quality data of the output process, and the production device includes a board device, a production processing device, and a quality detection device; Obtaining a pre-set production contribution coefficient of each production device in the SMT production line to perform key device screening among a plurality of production devices based on the production contribution coefficient of each production device, and determine at least one key production device, wherein the production contribution coefficient is used for the contribution degree of the production device in the product production process of the SMT production line; According to the running parameter data of the key production device in the device real-time running data, the running state is predicted in real time to obtain a failure probability change curve of each of the key production devices, so as to determine a stability factor of each of the key production devices at the current time based on the failure probability change curve and the output quality data corresponding to each of the key production devices; When there is a specified key production device whose stability factor at a specified time is lower than a pre-set stability expectation threshold, obtaining initial production plan data of the SMT production line, and generating an analysis time window based on the input production start time in the initial production plan data and the specified time; Based on the device running data in the analysis time window, the production state of the upstream device and the downstream device of the specified key production device is analyzed respectively to generate the supply efficiency of the upstream device and the production efficiency of the downstream device in the analysis time window; According to the supply efficiency of the upstream device and the production efficiency of the downstream device, the production device is optimized, and the device production parameter is adjusted to realize the production efficiency optimization of the SMT production line.

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