SMT production line dynamic scheduling optimization method and system based on big data analysis

Abstract

The invention discloses an SMT production line dynamic scheduling optimization method and system based on big data analysis, and belongs to the technical field of dynamic scheduling optimization. Collecting equipment load data and qualified rate data of equipment in the SMT production line; constructing a qualified rate-load scatter coordinate system; in the qualification rate-load scatter coordinate system, respectively constructing a qualification rate line segment and a load mean value line segment; constructing an optimal performance point, a load mean value range and a qualified rate data range; constructing an optimal range circle; marking equipment outside the optimal range circle as priority scheduling equipment, and carrying out load adjustment; according to the method, the performance data of the equipment is visualized by constructing the scatter coordinate system of the qualified rate and the load, and it is ensured that the product quality is not affected in the load adjusting process; the optimal range circle is constructed, priority scheduling is performed, and the equipment load is dynamically adjusted, so that the scheduling flexibility and response speed of the production line are effectively improved, and the equipment can be quickly adapted when the production demand changes.

Description

Technical Field

[0001] The present invention relates to the field of dynamic scheduling optimization technology, and specifically to a method and system for dynamic scheduling optimization of an SMT production line based on big data analysis. Background Art

[0002] With the rapid development of the electronics manufacturing industry, surface mount technology (SMT) has become an indispensable part of modern electronic assembly; the efficiency and stability of SMT production lines directly affect production costs, product quality and the market competitiveness of enterprises; in recent years, with the rise of intelligent technology and big data analysis, many manufacturing companies have begun to optimize production processes through digital means, especially in production line scheduling optimization; traditional SMT production line scheduling usually relies on experience and fixed rules, and the scheduling strategy is relatively rigid and cannot fully consider the real-time status of equipment and the dynamic changes of production tasks; with the continuous expansion of production scale, the limitations of traditional scheduling methods are becoming increasingly apparent, especially when dealing with factors such as equipment load, production qualification rate and equipment failure, they often lack flexibility and accuracy.

[0003] However, the existing related technologies still have some shortcomings in realizing dynamic scheduling optimization; traditional scheduling methods cannot obtain the working status of each device in real time, especially in complex production environments, where equipment load changes, production quality fluctuations and potential failures often cannot be monitored and reflected in a timely manner, resulting in fluctuations in product quality and affecting the efficiency and stability of the entire production line; in addition, existing methods usually fail to effectively combine the real-time data of equipment with production scheduling, resulting in problems such as imbalance and low production efficiency during the production process of the production line. Summary of the invention

[0004] The purpose of the present invention is to provide a SMT production line dynamic scheduling optimization method and system based on big data analysis to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A dynamic scheduling optimization method for SMT production lines based on big data analysis, the method comprising the following steps: collecting equipment load data and pass rate data of equipment in the SMT production line; constructing a pass rate-load scatter point coordinate system based on the load mean and pass rate data of the load data of all equipment during a single real-time operation; obtaining performance points corresponding to the maximum and minimum values ​​of the pass rate data in the pass rate-load scatter point coordinate system, and constructing a pass rate line segment; obtaining performance points corresponding to the maximum and minimum values ​​of the load mean within the range of the maximum and minimum values ​​of the pass rate data, and constructing a load mean line segment; setting a load mean range and a pass rate data range; constructing an optimal range circle based on the load mean range, the pass rate data range and the optimal performance point; marking equipment outside the optimal range circle as priority scheduling equipment, and performing load adjustment.

[0007] As a preferred solution of the data warehouse-based medical microbiology experiment teaching interactive method described in the present invention, an SMT production line data warehouse is constructed, and storage partitions are divided in the SMT production line data warehouse according to the number of devices in the SMT production line, wherein one storage partition is divided for each device; the device status information of the device during operation is stored in the storage partition; the device status information includes device load data and pass rate data, and one device corresponds to one device load data and one pass rate data.

[0008] The equipment load data is collected by a load sensor; the qualified rate data is the ratio of the qualified quantity to the total production quantity, the qualified quantity refers to the qualified quantity of product components produced by a device, and the total production quantity is the total amount of product components produced by a device.

[0009] It should be noted that as the load on the equipment increases, the equipment may tend to work in an overloaded state, which may lead to a decline in product quality and thus a decrease in the pass rate, because when the load is high, the operating frequency and speed of the equipment increase, which may increase the probability of failure rate or small errors, thereby affecting product quality; the pass rate is not only an indicator of quality control, but also indirectly reflects the health status of the equipment; for example, when the load of the equipment is not high but the pass rate is low, it may indicate that the equipment has potential failures or maintenance needs. By observing the relationship between load and pass rate, it is possible to better judge whether the equipment is suitable for continuing the current production task, or whether load adjustment or maintenance is required. Therefore, the correlation between load and pass rate provides an important basis for dynamic scheduling, which helps to balance the production quality and equipment stability of the production line during the scheduling process.

[0010] As a preferred solution of the data warehouse-based medical microbiology experiment teaching interactive method described in the present invention, the load mean and qualified rate data of the equipment load data of the i-th equipment in the n-th real-time operation process are recorded as ERin and PH in .

[0011] Construct a qualified rate-load scatter point coordinate system, the ordinate of which is the load average ER of all devices during the nth real-time operation. in , and, i∈[1,I], the horizontal coordinate of the qualified rate-load scatter point coordinate system is the qualified rate data PH of all devices during the nth real-time operation process in , and, i∈[1,I], where I represents the total number of devices; the coordinate point in the pass rate-load scatter point coordinate system is recorded as the performance point PP(PH in ,ER in ).

[0012] It should be noted that by constructing a scattered coordinate system between the load mean and the pass rate, the performance data of each device at a specific moment is visualized and the "performance point" of the device is generated; the main function of this step is to identify the current working status of each device through data visualization, which helps to discover abnormal situations such as high load and low pass rate or low load and high pass rate, and provide a basis for scheduling optimization; the optimal operating state and non-optimal state of the equipment can be identified through intuitive data distribution, so that the system can make more targeted scheduling adjustments and reduce the probability of production line imbalance.

[0013] As a preferred solution of the medical microbiology experiment teaching interactive method based on data warehouse described in the present invention, in the qualified rate-load scatter point coordinate system, the performance points corresponding to the maximum and minimum values ​​of the qualified rate data are obtained, which are respectively recorded as the qualified rate maximum performance point PP (RH imax ,ER in ) and the minimum performance point PP(PH imin ,ER jn ), where PH imax Indicates the maximum value of the qualified rate data, PH imin Indicates the minimum value of qualified rate data, ER jn It represents the load mean of the equipment load data of the jth equipment in the nth real-time operation process; at the maximum performance point PP (PH imax ,ER in ) and the minimum performance point PP(PH imin ,ER jn ) is constructed between the two segments and recorded as qualified rate segment L1.

[0014] The maximum PH value of the qualified rate data imax and minimum PH value of qualified rate data imin As a benchmark, obtain the horizontal coordinate data in [PH imin ,PH imaxThe performance points corresponding to the maximum and minimum values ​​of the load mean within the range are recorded as the load mean maximum performance point PP (PH an ,ER imax ) and the load mean minimum performance point PP(PH bn ,ER imin ), where PH an Indicates the qualified rate data of the a-th device during the n-th real-time operation, PH bn Indicates the qualified rate data of the bth device during the nth real-time operation, ER imax Indicates the maximum load average value, ER imin Indicates the minimum load average, and PH an ,PH bn ∈[PH imin ,PH imax ], ER imin ≤ER in or ER imin ≤ER jn ; At the load mean maximum performance point PP(PH an ,ER imax ) and the load mean minimum performance point PP(PH bn ,ER imin ) is constructed between the load mean line segment and the load mean line segment L2.

[0015] It should be noted that, since the pass rate directly reflects the product quality, it is preferred to construct the pass rate segment in the scheduling optimization to facilitate the determination of the quality standard range that the equipment needs to achieve. By finding the maximum and minimum values ​​of the pass rate data and constructing the pass rate segment, the upper and lower limits of quality control can be identified and set to ensure that during the load adjustment process, the output quality of the equipment will not fluctuate significantly due to changes in load. Once the upper and lower limits of the pass rate are determined, constructing the load mean segment based on this pass rate range can help evaluate the allowable range of load adjustment. The reason for this is that the load of the equipment can vary within a large range, but the fluctuation space of the pass rate is relatively small, especially in a production environment with high quality requirements. Therefore, constructing the load mean segment based on the pass rate can ensure that load adjustment will not cause the pass rate to fall out of the set range, thereby ensuring product quality and equipment stability.

[0016] The intersection point between the load mean line segment L1 and the pass rate line segment L2 is recorded as the optimal performance point PP (PH opt ,ER opt ), where PH opt Indicates the optimal pass rate data, ER opt Indicates the optimal load average.

[0017] Set the load average range and pass rate data range as follows:

[0018] Set the load average range, denoted as [ER irmin ,ER irmax ], among which, ER irmin Indicates the minimum value within the load average range, ER irmax Indicates the maximum value within the load average range. The calculation formulas are as follows:

[0019]

[0020] in, Indicates the load average ER in The average value of Indicates the load average ER in The standard deviation of , α represents the adjustment factor of the preset load mean, and max() represents the maximum value function.

[0021] Set the qualified rate data range, denoted as [PH irmin ,PH irmax ], where PH irmin Indicates the minimum value within the qualified rate data range, PH irmax Indicates the maximum value within the qualified rate data range, and the calculation formulas are as follows:

[0022]

[0023] in, Indicates the pass rate data PH in The average value of Indicates the pass rate data PH in The standard deviation of , β represents the adjustment factor of the preset qualified rate data, and max() represents the maximum value function.

[0024] As a preferred solution of the medical microbiology experiment teaching interactive method based on data warehouse described in the present invention, based on the optimal performance point PP (HER opt ,HPH opt ), load average range [ER irmin ,ER irmax ] and pass rate data range [PH irmin ,PH irmax ], construct the optimal range circle in the qualified rate-load scatter point coordinate system, as follows:

[0025] According to the load average range [ER irmin ,ER irmax ] and pass rate data range [PH irmin ,PH irmax ]Calculate the radius using the following formula:

[0026]

[0027] Wherein, R represents the radius of the optimal range circle.

[0028] Based on the radius R of the optimal range circle, the optimal performance point PP (ER opt ,PH opt ) as the center of the optimal range circle to construct the optimal range circle.

[0029] The devices outside the optimal range circle are marked as priority scheduling devices, and load adjustment is performed on the priority scheduling devices.

[0030] Let n=n+1, reconstruct the optimal range circle, re-mark the priority scheduling equipment, and perform dynamic scheduling optimization of the SMT production line.

[0031] It should be noted that an optimal range circle is constructed with the optimal performance point as the center, and the equipment beyond the optimal range is marked as priority scheduling equipment through the circle and the load is adjusted. This step continuously optimizes the overall performance of the production line by dynamically adjusting the equipment load, ensuring real-time resource scheduling under different production conditions. The advantage is that it greatly improves the response speed and scheduling flexibility of the system, allowing the production line to quickly adjust the equipment operating status when production demand changes, effectively reducing the risk of equipment downtime or quality problems caused by excessive or low load.

[0032] The SMT production line dynamic scheduling optimization system based on big data analysis includes: data warehouse construction and equipment status storage module, equipment performance visualization and data analysis module, performance line segment construction and quality control module and dynamic scheduling optimization and load adjustment module.

[0033] The data warehouse construction and equipment status storage module collects equipment load data and qualified rate data of equipment in the SMT production line.

[0034] The equipment performance visualization and data analysis module constructs a pass rate-load scatter point coordinate system based on the load mean and pass rate data of the load data of all equipment during a single real-time operation.

[0035] The performance line segment construction and quality control module: in the pass rate-load scatter point coordinate system, obtain the performance points corresponding to the maximum and minimum values ​​of the pass rate data, and construct the pass rate line segment; obtain the performance points corresponding to the maximum and minimum values ​​of the load mean within the range of the maximum and minimum values ​​of the pass rate data, and construct the load mean line segment; set the load mean range and the pass rate data range.

[0036] The dynamic scheduling optimization and load adjustment module: constructs an optimal range circle based on the load mean range, the qualified rate data range and the optimal performance point; marks the devices outside the optimal range circle as priority scheduling devices, and performs load adjustment.

[0037] Furthermore, the data warehouse construction and device status storage module includes a device data storage unit and a data acquisition unit.

[0038] The device data storage unit: constructs an SMT production line data warehouse, divides storage partitions in the SMT production line data warehouse according to the number of devices in the SMT production line, wherein one storage partition is corresponding to one device; stores device status information of the device during operation in the storage partition; the device status information includes device load data and pass rate data, and one device corresponds to one device load data and one pass rate data.

[0039] The data acquisition unit: the equipment load data is acquired through a load sensor; the qualified rate data is the ratio of the qualified quantity to the total production quantity, the qualified quantity refers to the qualified quantity of product components produced by a device, and the total production quantity is the total quantity of product components produced by a device.

[0040] Furthermore, the device performance visualization and data analysis module includes a scattered point coordinate system construction unit.

[0041] The scatter point coordinate system construction unit constructs a pass rate-load scatter point coordinate system, wherein the ordinate of the pass rate-load scatter point coordinate system is the load average of all devices during a single real-time operation, and the abscissa of the pass rate-load scatter point coordinate system is the pass rate data of all devices during a single real-time operation; and the coordinate points in the pass rate-load scatter point coordinate system are recorded as performance points.

[0042] Furthermore, the performance line segment construction and quality control module includes an optimal performance point acquisition unit and a data range design unit.

[0043] The optimal performance point acquisition unit: in the pass rate-load scatter point coordinate system, obtains the performance points corresponding to the maximum and minimum values ​​of the pass rate data, which are recorded as the maximum pass rate performance point and the minimum pass rate performance point respectively; constructs a straight line segment between the maximum pass rate performance point and the minimum pass rate performance point, which is recorded as the pass rate segment; obtains the performance points corresponding to the maximum and minimum values ​​of the load mean within the range of the maximum pass rate data and the minimum pass rate data of the horizontal coordinate data, which are recorded as the maximum load mean performance point and the minimum load mean performance point respectively; constructs a straight line segment between the maximum load mean performance point and the minimum load mean performance point, which is recorded as the load mean segment; and records the intersection between the load mean segment and the pass rate segment as the optimal performance point.

[0044] The data range design unit is used to set the load mean value range and the pass rate data range.

[0045] Furthermore, the dynamic scheduling optimization and load adjustment module includes an optimal range circle construction unit and an equipment load adjustment unit.

[0046] The optimal range circle construction unit: constructs an optimal range circle in the pass rate-load scatter point coordinate system based on the optimal performance point, the load mean value range and the pass rate data range, as follows:

[0047] The radius is calculated according to the load mean range and the qualified rate data range; based on the radius, the optimal range circle is constructed with the optimal performance point as the center of the optimal range circle.

[0048] The equipment load adjustment unit: marks the equipment outside the optimal range circle as priority scheduling equipment, and adjusts the load of the priority scheduling equipment; after each real-time operation, rebuilds the optimal range circle and re-marks the priority scheduling equipment to perform dynamic scheduling optimization of the SMT production line.

[0049] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in the SMT production line dynamic scheduling optimization method and system based on big data analysis provided by the present invention, by constructing an equipment status data warehouse and storing the equipment load and pass rate information, real-time monitoring of the equipment is achieved, and the health status and production capacity of the equipment are accurately evaluated, thereby providing reliable data support for subsequent scheduling optimization; by constructing a scattered coordinate system of pass rate and load, the performance data of the equipment is visualized, which helps to quickly identify the optimal and non-optimal operating states of the equipment, and ensure that the product quality is not affected during the load adjustment process; further, by setting the pass rate line segment and the load mean line segment, product quality is prioritized and equipment load is optimized, reducing the risk of equipment failure and downtime; finally, by constructing an optimal range circle and performing priority scheduling, the equipment load is dynamically adjusted, which effectively improves the scheduling flexibility and response speed of the production line, ensures that the equipment can quickly adapt when production demand changes, and ultimately greatly improves production efficiency, equipment stability and product quality control capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0051] Figure 1 It is a schematic diagram of the steps of the SMT production line dynamic scheduling optimization method based on big data analysis of the present invention;

[0052] Figure 2It is a structural schematic diagram of the SMT production line dynamic scheduling optimization system based on big data analysis of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 In the first embodiment of the present invention, a method for dynamic scheduling optimization of an SMT production line based on big data analysis is provided, and the method comprises the following steps:

[0055] Step S1: Collect equipment load data and qualified rate data of equipment in the SMT production line.

[0056] Specifically, an SMT production line data warehouse is constructed, and storage partitions are divided in the SMT production line data warehouse according to the number of devices in the SMT production line, wherein one storage partition corresponds to one device; device status information of the device during operation is stored in the storage partition; the device status information includes device load data and pass rate data, and one device corresponds to one device load data and one pass rate data.

[0057] Furthermore, the equipment load data is collected by a load sensor; the qualified rate data is the ratio of the qualified quantity to the total production quantity, the qualified quantity refers to the qualified quantity of product components produced by a device, and the total production quantity is the total amount of product components produced by a device.

[0058] Step S2: construct a pass rate-load scatter point coordinate system based on the load mean and pass rate data of the load data of all devices during a single real-time operation.

[0059] Specifically, the load mean and qualified rate data of the equipment load data of the i-th equipment in the n-th real-time operation process are recorded as ER in and PH in .

[0060] Furthermore, a pass rate-load scatter point coordinate system is constructed, wherein the ordinate of the pass rate-load scatter point coordinate system is the load average ER of all devices during the nth real-time operation. in , and, i∈[1,I], the horizontal coordinate of the qualified rate-load scatter point coordinate system is the qualified rate data PH of all devices during the nth real-time operation process in, and, i∈[1,I], where I represents the total number of devices; the coordinate point in the pass rate-load scatter point coordinate system is recorded as the performance point PP(PH in ,ER in ).

[0061] Step S3: In the pass rate-load scatter point coordinate system, obtain the performance points corresponding to the maximum and minimum values ​​of the pass rate data, and construct a pass rate line segment; obtain the performance points corresponding to the maximum and minimum values ​​of the load mean within the range of the maximum and minimum values ​​of the pass rate data, and construct a load mean line segment; set the load mean range and the pass rate data range.

[0062] Specifically, in the pass rate-load scatter point coordinate system, the performance points corresponding to the maximum and minimum values ​​of the pass rate data are obtained and recorded as the maximum pass rate performance point PP (PH imax ,ER in ) and the minimum performance point PP(PH imin ,ER jn ), where PH imax Indicates the maximum value of the qualified rate data, PH imin Indicates the minimum value of qualified rate data, ER jn It represents the load mean of the equipment load data of the jth equipment in the nth real-time operation process; at the maximum performance point PP (PH imax ,ER in ) and the minimum performance point PP(PH imin ,ER jn ) is constructed between the two segments and recorded as qualified rate segment L1.

[0063] The maximum PH value of the qualified rate data imax and minimum pass rate data PR min As a benchmark, obtain the horizontal coordinate data in [PH imin ,PH imax The performance points corresponding to the maximum and minimum values ​​of the load mean within the range are recorded as the load mean maximum performance point PP (PH an ,ER imax ) and the load mean minimum performance point PP(PH bn ,ER imin ), where PH an Indicates the qualified rate data of the a-th device during the n-th real-time operation, PH bn Indicates the qualified rate data of the bth device during the nth real-time operation, ER imax Indicates the maximum load average value, ER imin Indicates the minimum load average, and PH an ,PH bn ∈[PHimin ,PH imax ], ER imin ≤ER in or ER imin ≤ER jn ; At the load mean maximum performance point PP(PH an ,ER imax ) and the load mean minimum performance point PP(PH bn ,ER imin ) is constructed between the load mean line segment and the load mean line segment L2.

[0064] Furthermore, the intersection point between the load mean line segment L1 and the pass rate line segment L2 is recorded as the optimal performance point PP (PH opt ,ER opt ), where PH opt Indicates the optimal pass rate data, ER opt Indicates the optimal load average.

[0065] Set the load average range and pass rate data range as follows:

[0066] Set the load average range, denoted as [ER irmin ,ER irmax ], among which, ER irmin Indicates the minimum value within the load average range, ER irmax Indicates the maximum value within the load average range. The calculation formulas are as follows:

[0067]

[0068] in, Indicates the load average ER in The average value of Indicates the load average ER in The standard deviation of , α represents the adjustment factor of the preset load mean, and max() represents the maximum value function.

[0069] Set the qualified rate data range, denoted as [PH irmin ,PH irmax ], where PH irmin Indicates the minimum value within the qualified rate data range, PH irmax Indicates the maximum value within the qualified rate data range, and the calculation formulas are as follows:

[0070]

[0071] in, Indicates the pass rate data PH in The average value of Indicates the pass rate data PH inThe standard deviation of , β represents the adjustment factor of the preset qualified rate data, and max() represents the maximum value function.

[0072] Step S4: construct an optimal range circle based on the load mean range, the qualified rate data range and the optimal performance point; mark the devices outside the optimal range circle as priority scheduling devices, and perform load adjustment.

[0073] Specifically, based on the optimal performance point PP (HER opt ,HPH opt ), load average range [PH irmin ,ER irmax ] and pass rate data range [PH irmin ,PH irmax ], construct the optimal range circle in the qualified rate-load scatter point coordinate system, as follows:

[0074] According to the load average range [ER irmin ,ER irmax ] and pass rate data range [PH irmin ,PH irmax ]Calculate the radius using the following formula:

[0075]

[0076] Wherein, R represents the radius of the optimal range circle.

[0077] Based on the radius R of the optimal range circle, the optimal performance point PP (ER opt ,PH opt ) as the center of the optimal range circle to construct the optimal range circle.

[0078] Furthermore, the devices outside the optimal range circle are marked as priority scheduling devices, and load adjustment is performed on the priority scheduling devices.

[0079] Let n=n+1, reconstruct the optimal range circle, re-mark the priority scheduling equipment, and perform dynamic scheduling optimization of the SMT production line.

[0080] See also Figure 2 In the second embodiment of the present invention, a dynamic scheduling optimization system for an SMT production line based on big data analysis is provided, the system comprising: a data warehouse construction and equipment status storage module, an equipment performance visualization and data analysis module, a performance line segment construction and quality control module, and a dynamic scheduling optimization and load adjustment module.

[0081] The data warehouse construction and equipment status storage module collects equipment load data and qualified rate data of equipment in the SMT production line.

[0082] The equipment performance visualization and data analysis module constructs a pass rate-load scatter point coordinate system based on the load mean and pass rate data of the load data of all equipment during a single real-time operation.

[0083] The performance line segment construction and quality control module: in the pass rate-load scatter point coordinate system, obtain the performance points corresponding to the maximum and minimum values ​​of the pass rate data, and construct the pass rate line segment; obtain the performance points corresponding to the maximum and minimum values ​​of the load mean within the range of the maximum and minimum values ​​of the pass rate data, and construct the load mean line segment; set the load mean range and the pass rate data range.

[0084] The dynamic scheduling optimization and load adjustment module: constructs an optimal range circle based on the load mean range, the qualified rate data range and the optimal performance point; marks the devices outside the optimal range circle as priority scheduling devices, and performs load adjustment.

[0085] Furthermore, the data warehouse construction and device status storage module includes a device data storage unit and a data acquisition unit.

[0086] The device data storage unit: constructs an SMT production line data warehouse, divides storage partitions in the SMT production line data warehouse according to the number of devices in the SMT production line, wherein one storage partition is corresponding to one device; stores device status information of the device during operation in the storage partition; the device status information includes device load data and pass rate data, and one device corresponds to one device load data and one pass rate data.

[0087] The data acquisition unit: the equipment load data is acquired through a load sensor; the qualified rate data is the ratio of the qualified quantity to the total production quantity, the qualified quantity refers to the qualified quantity of product components produced by a device, and the total production quantity is the total quantity of product components produced by a device.

[0088] Furthermore, the device performance visualization and data analysis module includes a scattered point coordinate system construction unit.

[0089] The scatter point coordinate system construction unit constructs a pass rate-load scatter point coordinate system, wherein the ordinate of the pass rate-load scatter point coordinate system is the load average of all devices during a single real-time operation, and the abscissa of the pass rate-load scatter point coordinate system is the pass rate data of all devices during a single real-time operation; and the coordinate points in the pass rate-load scatter point coordinate system are recorded as performance points.

[0090] Furthermore, the performance line segment construction and quality control module includes an optimal performance point acquisition unit and a data range design unit.

[0091] The optimal performance point acquisition unit: in the pass rate-load scatter point coordinate system, obtains the performance points corresponding to the maximum and minimum values ​​of the pass rate data, which are recorded as the maximum pass rate performance point and the minimum pass rate performance point respectively; constructs a straight line segment between the maximum pass rate performance point and the minimum pass rate performance point, which is recorded as the pass rate segment; obtains the performance points corresponding to the maximum and minimum values ​​of the load mean within the range of the maximum pass rate data and the minimum pass rate data of the horizontal coordinate data, which are recorded as the maximum load mean performance point and the minimum load mean performance point respectively; constructs a straight line segment between the maximum load mean performance point and the minimum load mean performance point, which is recorded as the load mean segment; and records the intersection between the load mean segment and the pass rate segment as the optimal performance point.

[0092] The data range design unit is used to set the load mean value range and the pass rate data range.

[0093] Furthermore, the dynamic scheduling optimization and load adjustment module includes an optimal range circle construction unit and an equipment load adjustment unit.

[0094] The optimal range circle construction unit: constructs an optimal range circle in the pass rate-load scatter point coordinate system based on the optimal performance point, the load mean value range and the pass rate data range, as follows:

[0095] The radius is calculated according to the load mean range and the qualified rate data range; based on the radius, the optimal range circle is constructed with the optimal performance point as the center of the optimal range circle.

[0096] The equipment load adjustment unit: marks the equipment outside the optimal range circle as priority scheduling equipment, and adjusts the load of the priority scheduling equipment; after each real-time operation, rebuilds the optimal range circle and re-marks the priority scheduling equipment to perform dynamic scheduling optimization of the SMT production line.

[0097] In the third embodiment, a dynamic scheduling optimization method for SMT production lines based on big data analysis is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0098] First, comprehensive data collection is carried out on the equipment in the SMT production line; the experimental objects are five devices on the SMT production line, marked as equipment A, B, C, D and E respectively; in this experiment, the load data of the equipment is collected in real time by the load sensor, and the qualified rate data is calculated by the ratio of the number of qualified products produced by each device to the total production quantity.

[0099] During the experimental design process, we first collected the load data and pass rate data of all equipment in the same time period. Then, we constructed a pass rate-load scatter point coordinate system, plotted the performance points corresponding to the load mean and pass rate of each device in the coordinate system, and found the performance points corresponding to the maximum and minimum values ​​of the pass rate data through the analysis of the scatter point coordinate system, and constructed the pass rate line segment based on these points; then, we analyzed the maximum and minimum values ​​of the load mean, and constructed the load mean line segment, further optimizing the scheduling strategy of the production line equipment.

[0100] After setting the load mean range and pass rate data range, the optimal range circle was constructed based on these two ranges and the optimal performance point in the pass rate-load scatter point coordinate system; all equipment outside the optimal range circle was marked as priority scheduling equipment and load adjustments were made; after each run of the experiment, the optimal performance point was updated, the optimal range circle was recalculated, and the load was adjusted again to ensure that the production line is always in the best operating state.

[0101] This experiment conducted a total of five rounds of scheduling optimization. After each round of scheduling, the load data and pass rate data of all equipment, as well as the changes in the operating status of the equipment before and after optimization were recorded. Finally, the experiment verified the positive role of this dynamic scheduling optimization method in improving production line production efficiency and equipment health by comparing the data before and after optimization.

[0102] Table 1 Comparison of equipment operation optimization effects

[0103]

[0104] According to Table 1, the changes in the load mean and qualified rate of the equipment before and after optimization show that after optimization, the load mean of the equipment has decreased, while the qualified rate has generally increased; this indicates that through dynamic scheduling optimization, the equipment can avoid overload working conditions while maintaining high production efficiency, thereby improving the qualified rate of the product.

[0105] Optimization effect of device A: The average load of device A before optimization was 80, and the pass rate was 92. After scheduling optimization, the average load was reduced to 74, and the pass rate was increased to 96. This shows that device A achieved a lower workload through load adjustment, while improving production quality and reducing the risk of failure or quality degradation caused by overload.

[0106] Optimization effect of equipment B: The average load of equipment B before optimization was 70, and the qualified rate was 88. After optimization, the average load dropped to 67, and the qualified rate increased to 92. The optimized scheduling further reduced the load of equipment B and significantly improved the production quality.

[0107] Optimization effect of device C: The load mean of device C decreased after optimization, from 85 to 80, and the pass rate increased from 87 to 90. Although the load decreased slightly, the increase in the pass rate proved that the equipment can maintain higher production quality after load optimization.

[0108] Optimization effect of equipment D and equipment E: The optimization effect of equipment D and E is more obvious. The load of equipment D is reduced from 65 to 58, and the qualified rate is increased from 75 to 82; the load of equipment E is reduced from 92 to 88, and the qualified rate is increased from 90 to 94. The optimization effect of these two equipments shows that by reducing the load of equipment, the operation stability of equipment and product quality can be effectively improved, especially for equipment with higher load, the optimization effect is more significant.

[0109] It can be seen from the data analysis that the dynamic scheduling optimization method of the present invention shows obvious advantages in practical applications; compared with the traditional production line scheduling method, the optimization method based on big data analysis can dynamically monitor the real-time changes of equipment load and qualified rate, and adjust the load through scientific scheduling strategies, avoiding equipment overload or uneven load. This optimization not only improves the production efficiency of the production line, but also significantly improves the stability of the equipment and the qualified rate of the product.

[0110] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0111] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. The SMT production line dynamic scheduling optimization method based on big data analysis is characterized by: The method comprises the following steps: Step S1: Collecting equipment load data and qualified rate data of equipment in the SMT production line; Step S2: constructing a pass rate-load scatter point coordinate system based on the load mean and pass rate data of the load data of all devices during a single real-time operation; Step S3: in the pass rate-load scatter point coordinate system, construct a pass rate line segment and a load mean line segment respectively; record the intersection of the pass rate line segment and the load mean line segment as the optimal performance point; set the load mean range and the pass rate data range; Step S4: construct an optimal range circle based on the load mean range, the qualified rate data range and the optimal performance point; mark the devices outside the optimal range circle as priority scheduling devices, and perform load adjustment.

2. The SMT production line dynamic scheduling optimization method based on big data analysis according to claim 1 is characterized in that: The specific implementation process of step S1 includes: Construct an SMT production line data warehouse, divide storage partitions in the SMT production line data warehouse according to the number of devices in the SMT production line, wherein one storage partition is divided corresponding to one device; store device status information of the device during operation in the storage partition; the device status information includes device load data and pass rate data, and one device corresponds to one device load data and one pass rate data; The equipment load data is collected by a load sensor; the qualified rate data is the ratio of the qualified quantity to the total production quantity, the qualified quantity refers to the qualified quantity of product components produced by a device, and the total production quantity is the total amount of product components produced by a device.

3. The SMT production line dynamic scheduling optimization method based on big data analysis according to claim 2 is characterized in that: The specific implementation process of step S2 includes: The load mean and qualified rate data of the equipment load data of the i-th equipment in the n-th real-time operation process are recorded as ER in and PH in ; Construct a qualified rate-load scatter point coordinate system, the ordinate of which is the load average ER of all devices during the nth real-time operation. in , and, i∈[1,I], the horizontal coordinate of the qualified rate-load scatter point coordinate system is the qualified rate data PH of all devices during the nth real-time operation process in , and, i∈[1,I], where I represents the total number of devices; the coordinate point in the pass rate-load scatter point coordinate system is recorded as the performance point PP(PH in ,ER in ).

4. The SMT production line dynamic scheduling optimization method based on big data analysis according to claim 3 is characterized in that: The specific implementation process of step S3 includes: In the pass rate-load scatter point coordinate system, the performance points corresponding to the maximum and minimum values ​​of the pass rate data are obtained and recorded as the maximum pass rate performance point PP (PH imax ,ER in ) and the minimum performance point PP(PH imin ,ER jn ), where PH imax Indicates the maximum value of the qualified rate data, PH imin Indicates the minimum value of qualified rate data, ER jn It represents the load mean of the equipment load data of the jth equipment in the nth real-time operation process; at the maximum performance point PP (PH imax ,ER in ) and the minimum performance point PP(PH imin ,ER jn ) construct a straight line segment between them, recorded as qualified rate segment L1; The maximum PH value of the qualified rate data imax and minimum PH value of qualified rate data imin As a benchmark, obtain the horizontal coordinate data in [PH imin ,PH imax The performance points corresponding to the maximum and minimum values ​​of the load mean within the range are recorded as the load mean maximum performance point PP (PH an ,ER imax ) and the load mean minimum performance point PP(PH bn ,ER imin ), where PH an Indicates the qualified rate data of the a-th device during the n-th real-time operation, PH bn Indicates the qualified rate data of the bth device during the nth real-time operation, ER imax Indicates the maximum load average value, ER imin Indicates the minimum load average, and PH an ,PH bn ∈[PH imin ,PH imax ], ER imin ≤ER in or ER imin ≤ER jn ; At the load mean maximum performance point PP(PH an ,ER imax ) and the load mean minimum performance point PP(PH bn ,ER imin ) construct a straight line segment between them, which is recorded as load mean line segment L2; The intersection point between the load mean line segment L1 and the pass rate line segment L2 is recorded as the optimal performance point PP (PH opt ,ER opt ), where PH opt Indicates the optimal pass rate data, ER opt represents the optimal load mean; Set the load average range and pass rate data range as follows: Set the load average range, denoted as [ER irmin ,ER imax ], among which, ER irmin Indicates the minimum value within the load average range, ER irmax Indicates the maximum value within the load average range. The calculation formulas are as follows: in, Indicates the load average ER in The average value of Indicates the load average ER in The standard deviation of , α represents the adjustment factor of the preset load mean, and max() represents the maximum value function; Set the qualified rate data range, denoted as [PH irmin ,PH irmax ], where PH irmin Indicates the minimum value within the qualified rate data range, PH irmax Indicates the maximum value within the qualified rate data range, and the calculation formulas are as follows: in, Indicates the pass rate data PH in The average value of Indicates the pass rate data PH in The standard deviation of , β represents the adjustment factor of the preset qualified rate data, and max() represents the maximum value function.

5. The SMT production line dynamic scheduling optimization method based on big data analysis according to claim 4 is characterized in that: The specific implementation process of step S4 includes: Based on the optimal performance point PP (HER opt ,HPH opt ), load average range [ER irmin ,ER irmax ] and pass rate data range [PH irmin ,PH irmax ], construct the optimal range circle in the qualified rate-load scatter point coordinate system, as follows: According to the load average range [ER irmin ,ER irmax ] and pass rate data range [PH irmin ,PH irmax ]Calculate the radius using the following formula: Where R represents the radius of the optimal range circle; Based on the radius R of the optimal range circle, the optimal performance point PP (ER opt ,PH opt ) as the center of the optimal range circle, construct the optimal range circle; Marking the equipment outside the optimal range circle as priority scheduling equipment, and performing load adjustment on the priority scheduling equipment; Let n=n+1, reconstruct the optimal range circle, re-mark the priority scheduling equipment, and perform dynamic scheduling optimization of the SMT production line.

6. A SMT production line dynamic scheduling optimization system based on big data analysis, which executes the SMT production line dynamic scheduling optimization method based on big data analysis as described in any one of claims 1 to 5, characterized in that: The system includes: a data warehouse construction and equipment status storage module, an equipment performance visualization and data analysis module, a performance line segment construction and quality control module, and a dynamic scheduling optimization and load adjustment module; The data warehouse construction and equipment status storage module collects equipment load data and qualified rate data of equipment in the SMT production line; The equipment performance visualization and data analysis module: constructs a pass rate-load scatter point coordinate system based on the load mean and pass rate data of the load data of all equipment during a single real-time operation; The performance line segment construction and quality control module: in the pass rate-load scatter point coordinate system, obtain the performance points corresponding to the maximum and minimum values ​​of the pass rate data, and construct the pass rate line segment; obtain the performance points corresponding to the maximum and minimum values ​​of the load mean within the range of the maximum and minimum values ​​of the pass rate data, and construct the load mean line segment; set the load mean range and the pass rate data range; The dynamic scheduling optimization and load adjustment module: constructs an optimal range circle based on the load mean range, the qualified rate data range and the optimal performance point; marks the devices outside the optimal range circle as priority scheduling devices, and performs load adjustment.

7. The SMT production line dynamic scheduling optimization system based on big data analysis according to claim 6 is characterized by: The data warehouse construction and device status storage module includes a device data storage unit and a data acquisition unit; The device data storage unit: constructs an SMT production line data warehouse, divides storage partitions in the SMT production line data warehouse according to the number of devices in the SMT production line, wherein one storage partition is divided corresponding to one device; stores device status information of the device during operation in the storage partition; the device status information includes device load data and pass rate data, and one device corresponds to one device load data and one pass rate data; The data acquisition unit: the equipment load data is acquired through a load sensor; the qualified rate data is the ratio of the qualified quantity to the total production quantity, the qualified quantity refers to the qualified quantity of product components produced by a device, and the total production quantity is the total quantity of product components produced by a device.

8. The SMT production line dynamic scheduling optimization system based on big data analysis according to claim 7 is characterized in that: The equipment performance visualization and data analysis module includes a scattered point coordinate system construction unit; The scatter point coordinate system construction unit constructs a pass rate-load scatter point coordinate system, wherein the ordinate of the pass rate-load scatter point coordinate system is the load average of all devices during a single real-time operation, and the abscissa of the pass rate-load scatter point coordinate system is the pass rate data of all devices during a single real-time operation; and the coordinate points in the pass rate-load scatter point coordinate system are recorded as performance points.

9. The SMT production line dynamic scheduling optimization system based on big data analysis according to claim 8 is characterized in that: The performance line segment construction and quality control module includes an optimal performance point acquisition unit and a data range design unit; The optimal performance point acquisition unit: in the pass rate-load scatter point coordinate system, obtains the performance points corresponding to the maximum and minimum values ​​of the pass rate data, which are recorded as the maximum pass rate performance point and the minimum pass rate performance point respectively; constructs a straight line segment between the maximum pass rate performance point and the minimum pass rate performance point, which is recorded as the pass rate segment; Obtain the performance points corresponding to the maximum and minimum values ​​of the load mean value within the range of the maximum and minimum values ​​of the pass rate data of the horizontal coordinate data, and record them as the load mean maximum performance point and the load mean minimum performance point respectively; construct a straight line segment between the load mean maximum performance point and the load mean minimum performance point, and record it as the load mean line segment; record the intersection point between the load mean line segment and the pass rate line segment as the optimal performance point; The data range design unit is used to set the load mean value range and the pass rate data range.

10. The SMT production line dynamic scheduling optimization system based on big data analysis according to claim 9, characterized in that: The dynamic scheduling optimization and load adjustment module includes an optimal range circle construction unit and an equipment load adjustment unit; The optimal range circle construction unit: constructs an optimal range circle in the pass rate-load scatter point coordinate system based on the optimal performance point, the load mean value range and the pass rate data range, as follows: The radius is calculated according to the load mean value range and the qualified rate data range; based on the radius, the optimal range circle is constructed with the optimal performance point as the center of the optimal range circle; The device load adjustment unit is used to mark the devices outside the optimal range circle as priority scheduling devices, and perform load adjustment on the priority scheduling devices; After each real-time operation, the optimal range circle is rebuilt and the priority scheduling equipment is re-marked to perform dynamic scheduling optimization of the SMT production line.

Images