Production line bottleneck identification method and device, electronic equipment, medium and product

Abstract

The invention relates to the technical field of production and manufacturing, and discloses a production line bottleneck identification method and device, electronic equipment, a medium and a product. And analyzing the production cycle index consumed by each device in unit time in different device states and when different types of waste parts are produced, and obtaining the unit cycle index of each device. And comparing and analyzing the production cycle times consumed by the equipment and the working procedures in the production line by combining the unit cycle times of each equipment, and dynamically and accurately positioning the bottleneck working procedures and / or bottleneck equipment of the production line. And in combination with production cycle times consumed by different equipment states and different waste part types, bottleneck reasons for generating bottlenecks are traced, so that a bottleneck optimization decision basis is provided for managers.

Description

Technical Field

[0001] This invention relates to the field of manufacturing technology, specifically to methods, devices, electronic equipment, media, and products for identifying bottlenecks in production lines. Background Technology

[0002] In modern discrete manufacturing industries such as automotive, aerospace, and precision machining, accurate identification of production line bottlenecks is a core element in improving overall equipment effectiveness (OEE) and achieving lean manufacturing. However, the high degree of automation and intelligence in production lines increases the dynamism and concealment of bottleneck workstations. Traditional bottleneck identification solutions mainly rely on workstation cycle time analysis, which has the limitation of a single data dimension, only able to identify bottlenecks caused by cycle time.

[0003] Existing technologies dynamically identify bottlenecks by fusing multiple indicators, such as calculating equipment load rate and utilization rate. This represents an initial leap from manual experience to data-driven approaches and has solved the problem of single data dimensions to some extent. However, the effect of identifying bottlenecks caused by external factors such as fluctuations in incoming material quality is limited, and the cause of the bottleneck cannot be identified. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, medium, and product for identifying bottlenecks in production lines, in order to solve the problem that existing technologies cannot accurately identify bottlenecks in production lines and the causes of bottlenecks.

[0005] In a first aspect, the present invention provides a method for identifying bottlenecks in a production line, wherein the production line includes multiple sequential processes, each process includes at least one piece of equipment, and each piece of equipment includes multiple equipment states; the method includes:

[0006] Acquire production line data for each piece of equipment; this production line data includes equipment status data, production cycle time data, and quality loss data. Based on equipment status data and production cycle data, the first unit cycle count for each piece of equipment is obtained. The first unit cycle count is used to characterize the number of production cycles consumed per unit time for different equipment states. Each production cycle count corresponds to the production time of one workpiece. Based on the quality loss data, the second unit cycle count for each piece of equipment is obtained; the second unit cycle count is used to characterize the number of production cycles consumed per unit time for different types of scrap parts of the equipment. Based on the first unit cycle count and the second unit cycle count of each device, the bottleneck identification results of the production line are obtained. The bottleneck identification results include the bottleneck process and / or bottleneck equipment and / or bottleneck cause.

[0007] This invention analyzes the number of production cycles consumed per unit time by each piece of equipment under different equipment states and when producing different types of scrap parts, using equipment status data, production cycle time data, and quality loss data. This yields the unit cycle count for each piece of equipment. By combining the unit cycle counts of each piece of equipment with the comparative analysis of the production cycle counts consumed by equipment and processes in the production line, the bottleneck processes and / or bottleneck equipment of the production line can be dynamically and accurately located. Furthermore, by combining the production cycle counts consumed under different equipment states and different types of scrap parts, the causes of bottlenecks can be traced, thus providing managers with a basis for bottleneck optimization decisions.

[0008] In some optional implementations, the first unit cycle number of each piece of equipment is obtained based on equipment status data and production cycle time data, including: The planned production time of the production line is determined, and the total duration of each equipment status is obtained based on the equipment status data within the planned production time. The equipment status includes: offline, shutdown, emergency stop, abnormal stop, tool change, idling, maintenance, through mode, non-automatic mode, cyclic stop, calibration, material blockage, waiting for material, and production. For each device, based on the device's production time and the total duration of each device state, the occupancy time of each device state per unit time is obtained; For each piece of equipment, the first unit cycle count is obtained based on the production cycle data and the time occupied by each equipment state within a unit of time.

[0009] This invention first calculates the total duration of various equipment states within the planned production time range, then normalizes it to a comparable unit time occupancy time by using the equipment's production time, and then uses the equipment's average production cycle time to convert the unit time occupancy time into the number of unit cycles of the equipment in different equipment states, so as to analyze the equipment production capacity consumed by each equipment state and thus achieve bottleneck location.

[0010] In some optional implementations, the first unit cycle number of the equipment is obtained based on production cycle time data and the occupancy time of each equipment state per unit time, including: The average production cycle time of the equipment is obtained based on the production cycle time data; For each equipment state, the number of production cycles consumed per unit time is obtained based on the ratio between the equipment state's occupied time per unit time and the average production cycle time.

[0011] This invention calculates the number of production cycles consumed per unit time for each equipment state by measuring the occupancy time of various equipment states within a unit time and the average production cycle time of the equipment. This quantifies equipment efficiency losses and provides a basis for accurately assessing the actual impact of various equipment states on production capacity.

[0012] In some alternative implementations, the second unit cycle number for each device is obtained based on mass loss data, including: Based on the quality loss data of each piece of equipment during the production time, the quantity of different types of scrap produced by each piece of equipment per unit time is obtained; scrap types include rework pieces, industrial scrap, and material scrap. Based on the number of different types of scrap produced by each piece of equipment per unit time, the number of production cycles consumed per unit time for each piece of equipment in terms of repeated workpieces, industrial waste, and material waste is obtained.

[0013] This invention quantifies different types of quality losses, such as repeated production runs, waste from work, and waste from materials, into the number of production cycles consumed per unit time. This allows for the analysis of the bottleneck impact of work waste caused by process problems, waste from materials caused by material problems, and repeated production runs on equipment and processes. It effectively solves the problem that quality factors are difficult to quantify and account for in traditional bottleneck analysis, and that the causes of bottlenecks are difficult to trace.

[0014] In some optional implementations, bottleneck identification results for the production line are obtained based on the first unit cycle count and the second unit cycle count of each device, including: The third unit number of each device is calculated based on the first and second unit number of each device. The third unit number of each device is used to characterize the number of production cycles consumed per unit time for different device states and different types of scrap. For each piece of equipment, based on the third unit cycle count of the equipment, the number of production cycles consumed per unit time in the production equipment status of the equipment is extracted to obtain the equipment's net production capacity index; among which, the production equipment status includes material blockage, waiting for material, and production; Based on the net production capacity index of all equipment in each process, the net production capacity index of each process is calculated, and the process with the smallest net production capacity index is identified, thus obtaining the bottleneck process of the production line.

[0015] This invention obtains the net production capacity index of equipment by statistically analyzing the number of unit cycles per unit in production-related equipment states such as material blockage, waiting for material, and production. Furthermore, it categorizes and sums the number of unit cycles per unit in production-related equipment states within a process to obtain the process's net production capacity index, thereby quantifying the actual effective output capacity of the process. Compared to traditional solutions that simply consider static cycle time, this invention can accurately identify the bottleneck process restricting overall output in complex production scenarios based on the process with the lowest net production capacity index, providing a basis for production line balance optimization and resource scheduling.

[0016] In some optional implementations, the bottleneck identification result of the production line is obtained based on the first unit cycle number and the second unit cycle number of each device, and further includes: Identify the equipment with the lowest net production capacity index within the bottleneck process to obtain the bottleneck equipment of the production line.

[0017] This invention identifies bottlenecks within a process by determining the equipment with the lowest net production capacity index within the identified bottleneck process.

[0018] In some optional implementations, the bottleneck identification result of the production line is obtained based on the first unit cycle number and the second unit cycle number of each device, and further includes: Identify the target equipment state and target scrap type that consumes the most production cycles per unit time for bottleneck processes and / or bottleneck equipment; where the target equipment state is the equipment state other than production equipment state. Based on the status of the target equipment and the type of the target scrap, the bottleneck cause of the production line is determined.

[0019] This invention sorts the number of unit cycles in various non-production states, automatically identifies and sorts the target equipment state and target scrap type that cause the bottleneck, achieves precise location of the bottleneck cause, effectively removes the bottleneck, increases overall production capacity, and improves the efficiency of bottleneck problem solving.

[0020] In some alternative implementations, the method further includes: The third unit cycle count, net production capacity index of each piece of equipment, and net production capacity index of each process are visualized.

[0021] This embodiment visualizes the unit cycle count and net production capacity index of each piece of equipment and each process, so that users can intuitively analyze bottleneck processes, bottleneck equipment and bottleneck causes, thereby improving the efficiency of identifying bottlenecks in the production line.

[0022] Secondly, the present invention provides a bottleneck identification device for a production line, wherein the production line includes multiple sequential processes, each process includes at least one piece of equipment, and each piece of equipment includes multiple equipment states; the device includes: The first processing module is used to acquire production line data for each device; the production line data includes device status data, production cycle time data, and quality loss data. The second processing module is used to obtain the first unit cycle count of each device based on the device status data and production cycle data. The first unit cycle count is used to characterize the number of production cycles consumed per unit time for different device states. Each production cycle count corresponds to the production time of one workpiece. The third processing module is used to obtain the second unit cycle count for each piece of equipment based on the quality loss data; the second unit cycle count is used to characterize the number of production cycles consumed per unit time for different types of scrap parts of the equipment. The fourth processing module is used to obtain the bottleneck identification results of the production line based on the first unit cycle number and the second unit cycle number of each device. The bottleneck identification results include the bottleneck process and / or bottleneck equipment and / or bottleneck cause.

[0023] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the production line bottleneck identification method of the first aspect or any corresponding embodiment described above.

[0024] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the production line bottleneck identification method of the first aspect or any corresponding embodiment described above.

[0025] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the production line bottleneck identification method described in the first aspect or any corresponding embodiment thereof.

[0026] The beneficial effects of this invention are as follows: This invention analyzes the number of production cycles consumed per unit time by each piece of equipment under different equipment states and when producing different types of scrap parts, using equipment status data, production cycle time data, and quality loss data. This yields the unit cycle count for each piece of equipment. By combining the unit cycle counts of each piece of equipment with the comparative analysis of the production cycle counts consumed by equipment and processes in the production line, the bottleneck processes and / or bottleneck equipment of the production line can be dynamically and accurately located. Furthermore, by combining the production cycle counts consumed under different equipment states and different types of scrap parts, the causes of bottlenecks can be traced, thus providing managers with a basis for bottleneck optimization decisions. Attached Figure Description

[0027] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0028] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the production line bottleneck identification method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of the production line bottleneck identification method according to an embodiment of the present invention; Figure 4 (a) is a schematic diagram of the total duration of various device states according to an embodiment of the present invention; Figure 4 (b) is a schematic diagram of the occupancy time of various device states according to an embodiment of the present invention; Figure 4 (c) in the figure is a schematic diagram of the number of unit cycles for various device states according to an embodiment of the present invention; Figure 4 (d) in the figure is a schematic diagram of the unit cycle number of various equipment states and various types of scrap parts according to embodiments of the present invention; Figure 5 (a) is a schematic diagram of the number of unit cycles for a single equipment operation according to an embodiment of the present invention; Figure 5 (b) is a schematic diagram of the number of unit cycles of the process equipment according to an embodiment of the present invention; Figure 5 (c) is a schematic diagram of the number of unit cycles of another process equipment according to an embodiment of the present invention; Figure 5 (d) in the figure is a schematic diagram of the number of unit cycles in a multi-equipment process according to an embodiment of the present invention; Figure 6 This is an overview diagram of the process bottleneck according to an embodiment of the present invention; Figure 7 This is an overview diagram of the equipment bottleneck according to an embodiment of the present invention; Figure 8 This is a bottleneck trend diagram of the equipment according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the third process of the production line bottleneck identification method according to an embodiment of the present invention; Figure 10 This is a structural block diagram of a production line bottleneck identification device according to an embodiment of the present invention; Figure 11 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0031] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0032] As an optional application scenario of this invention, such as Figure 1 As shown, the production line bottleneck identification system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0033] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0034] According to an embodiment of the present invention, a method for identifying bottlenecks in a production line is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] This embodiment provides a method for identifying bottlenecks in a production line, which can be used in the aforementioned terminal devices, such as computers and tablets. Figure 2 This is a flowchart of a production line bottleneck identification method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain production line data for each device; the production line data includes device status data, production cycle time data, and quality loss data.

[0036] Specifically, the production line in this embodiment includes multiple processes connected in series, each process includes at least one piece of equipment, and each piece of equipment includes multiple equipment states. Taking a cylinder head machining production line as an example, the production line consists of multiple processes connected in series, and each process consists of one or more pieces of equipment.

[0037] This embodiment provides a detailed standardized definition of the equipment status of the production line equipment, including equipment status grouping, status group code, status name, status code and status description, as shown in Table 1 below. There are 14 types of equipment status classification, divided into 5 status groups. The equipment status of each piece of equipment includes, but is not limited to: offline, shutdown, emergency stop, abnormal stop, tool change, no-load operation, maintenance, through mode, non-automatic mode, cycle stop, calibration, material blockage, waiting for material and production.

[0038] Table 1 Equipment Status Definition

[0039] It should be noted that the offline status of a device can be determined by the system, while other device statuses are output by the corresponding device controller. When multiple statuses coexist on a device at the same time, the lower the status code, the higher the priority; that is, the status with the smaller status code is taken as the current device status. Status codes do not need to be consecutive, so as to fill in undefined device states between status codes, thereby adapting to different types of production lines.

[0040] In this embodiment, the system polls the status code output value of the device. When the status code output by the device changes, the system collects and saves the device number, status code and status change time to form a status time series of each device, thus obtaining the device status data of each device.

[0041] In this embodiment, each time the equipment completes a production cycle (i.e., produces one workpiece), it collects and saves the equipment number, production cycle time, and completion time to obtain the production cycle time data for each equipment. The production cycle time refers to the time required for the equipment to produce one workpiece. For a process consisting of multiple parallel equipment, the production cycle time of a single equipment is longer, but the overall cycle time of the process remains balanced with the entire production line.

[0042] In this embodiment, the quality loss data mainly includes workpiece line-passing data, scrap data, and material scrap data. Specifically, the equipment collects and saves the equipment number, workpiece number, and production time after each production cycle, thus obtaining the workpiece line-passing data. The scrap data, material scrap data, and production line scheduling data can be directly obtained from the relevant Manufacturing Execution System (MES).

[0043] Step S202: Based on the equipment status data and production cycle data, obtain the first unit cycle count for each piece of equipment; the first unit cycle count is used to characterize the number of production cycles consumed per unit time for different equipment states, and each production cycle count corresponds to the production time of one workpiece.

[0044] Specifically, in an ideal state, the equipment produces a qualified workpiece for each production cycle. However, in real-world scenarios, a production cycle does not always correspond to a single workpiece. For example, when the equipment is under maintenance or in an emergency stop, it will lose its production capacity (i.e., occupy production time and consume production cycle counts).

[0045] Therefore, this embodiment analyzes the equipment status data and production cycle data of each device, calculates the number of production cycles consumed per unit time for each equipment status, obtains the first unit cycle number of each device, and quantifies the workpiece capacity consumed by the device in various equipment statuses, so as to analyze which equipment status has the greatest impact on equipment capacity and thus analyze the cause of the bottleneck.

[0046] It should be noted that Average Cycles Per Hour (ACH) refers to the number of production cycles consumed per unit of time.

[0047] Step S203: Based on the quality loss data, obtain the second unit cycle number for each device; the second unit cycle number is used to characterize the number of production cycles consumed per unit time for different types of scrap parts of the device.

[0048] Specifically, besides consuming equipment capacity in various operating states, even when equipment is in normal production cycles and produces workpieces, if the produced workpieces are defective (material scrap, workpiece defective due to manufacturing defects), or reworked workpieces, the equipment consumes production time for qualified workpieces, resulting in a loss of equipment capacity. It should be noted that workpiece defective refers to scrap caused by operational errors, equipment malfunctions, etc., during production, while material scrap is scrap caused by quality problems with the material itself (such as excessive impurities or insufficient strength).

[0049] Therefore, this embodiment effectively distinguishes the number of production cycles consumed per unit time when the equipment produces different types of scrap parts by integrating quality loss data from the equipment's underlying system and the quality system. This allows for the determination of the second unit cycle number for each piece of equipment, which helps to analyze which type of scrap part has the greatest impact on the equipment's capacity and thus identify the cause of the bottleneck.

[0050] Step S204: Based on the first unit cycle count and the second unit cycle count of each device, obtain the bottleneck identification result of the production line. The bottleneck identification result includes the bottleneck process and / or bottleneck equipment and / or bottleneck cause.

[0051] Specifically, by comparing and analyzing the first and second unit cycle counts of each piece of equipment, and in conjunction with the composition relationship between the process and the equipment, the number of production cycles consumed by each process and each piece of equipment under different equipment states and when producing different types of scrap parts per unit time is compared and analyzed. Bottleneck processes and / or bottleneck equipment and / or bottleneck causes with capacity consumption problems are identified, and bottleneck identification results are obtained.

[0052] The bottleneck identification method for production lines provided in this embodiment analyzes the number of production cycles consumed per unit time for each piece of equipment under different equipment states and when producing different types of scrap parts, using equipment status data, production cycle time data, and quality loss data. This yields the unit cycle count for each piece of equipment. By combining the unit cycle counts of each piece of equipment with the comparative analysis of the production cycle counts consumed by equipment and processes in the production line, the bottleneck processes and / or bottleneck equipment can be dynamically and accurately located. Furthermore, by combining the production cycle counts consumed under different equipment states and different types of scrap parts, the causes of bottlenecks can be traced, thus providing managers with a basis for bottleneck optimization decisions.

[0053] This embodiment provides a method for identifying bottlenecks in a production line, which can be used in the aforementioned terminal devices, such as computers and tablets. Figure 3 This is a flowchart of a production line bottleneck identification method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Obtain production line data for each piece of equipment; this data includes equipment status data, production cycle time data, and quality loss data. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0054] Step S302: Based on the equipment status data and production cycle data, obtain the first unit cycle count for each piece of equipment; the first unit cycle count is used to characterize the number of production cycles consumed per unit time for different equipment states, and each production cycle count corresponds to the production time of one workpiece.

[0055] Specifically, step S302 includes: Step S3021: Determine the planned production time of the production line. Based on the equipment status data within the planned production time, obtain the total duration of each equipment status for each piece of equipment.

[0056] Specifically, the planned production time for the production line is obtained based on the production line's scheduling data, including the start and end times. Equipment status data for each device within the start and end times is extracted. Furthermore, equipment status records for each device at the start time are supplemented; that is, the status change time is the start time, and the status code is the status code of the last record before the start time. After the last status code data record for each device, a new status code record with a unified time is added; that is, the status code is the status code of the last status record for each device, and the time is the earlier of the current time and the end time. Through the above data extraction and data supplementation steps, the cleaned equipment status data for each device is obtained.

[0057] In this embodiment, for each device, the total duration of each type of device state is calculated based on the device state data after data cleaning. For example, as shown... Figure 4 As shown in (a), the total duration of the equipment in emergency stop, empty circulation, waiting for material, material blockage and production is 1232 seconds, 1848 seconds, 3080 seconds, 6160 seconds and 16016 seconds, respectively.

[0058] Step S3022: For each device, based on the device's production time and the total duration of each device state, obtain the occupancy time of each device state per unit time.

[0059] Specifically, the production time of the equipment is the time difference between the current time and the start time of the production line. For each equipment state, the occupancy time of that state per unit time is obtained by dividing the total duration of that state by the production time. For ease of understanding, the following example uses 1 hour as the unit time, but this can be adjusted according to the actual needs of the scenario.

[0060] For example, see again Figure 4 In (a), the production time is approximately 8 hours. For example... Figure 4 As shown in (b), the total duration of each equipment state is divided by the 8 hours of production time to obtain the occupancy time of each equipment state in 1 hour, that is, the average duration per hour. The occupancy times of the equipment in emergency stop, empty cycle, waiting for material, material blockage and production are 154 seconds, 231 seconds, 385 seconds, 770 seconds and 2002 seconds, respectively.

[0061] Step S3023: For each device, based on the production cycle data and the time occupied by each device state within a unit of time, obtain the first unit cycle number of the device.

[0062] In some optional implementations, step S3023 above includes: Step a1: Obtain the average production cycle time of the equipment based on the production cycle time data.

[0063] Specifically, for each piece of equipment, the average production cycle time data of that equipment within the range of start and end times is calculated to obtain the average production cycle time t1 of that equipment.

[0064] Step a2: For each equipment state, the number of production cycles consumed per unit time is obtained based on the ratio between the equipment state's occupied time per unit time and the average production cycle time.

[0065] For example, the formula for calculating the first unit loop count ACH1 is as follows:

[0066] in, This indicates the first loop count corresponding to the device state with status code i. This indicates the duration of the device state with status code i within a unit of time.

[0067] For example, taking an average production cycle time of 78 seconds as an example, the time occupied by various equipment states per unit time is divided by the average production cycle time to obtain the number of cycles consumed by each equipment state. Figure 4 As shown in (c), the number of unit cycles consumed during emergency stop, empty cycle, waiting for material, material blockage, and production are 2, 3, 5, 10, and 26, respectively.

[0068] This embodiment calculates the number of production cycles consumed per unit time for each equipment state by measuring the occupation time of various equipment states within a unit time and the average production cycle time of the equipment. This quantifies the equipment efficiency loss and provides a basis for accurately assessing the actual impact of various equipment states on production capacity.

[0069] In the above embodiments, the present invention first calculates the total duration of various equipment states within the planned production time range, and then normalizes it to a comparable unit time occupancy time by using the equipment's production time. Then, it uses the equipment's average production cycle time to convert the unit time occupancy time into the unit cycle number of the equipment in different equipment states, so as to analyze the equipment production capacity consumed by each equipment state and thus achieve bottleneck location.

[0070] Step S303: Based on the quality loss data, obtain the second unit cycle number for each device; the second unit cycle number is used to characterize the number of production cycles consumed per unit time for different types of scrap parts of the device.

[0071] Specifically, step S303 includes: Step S3031: Based on the quality loss data of each equipment within the production time, obtain the quantity of different scrap types produced by each equipment per unit time; scrap types include reworked parts, industrial scrap, and material scrap.

[0072] Specifically, based on the quality loss data, the workpiece passing data, work waste data, and material waste data of each equipment within the production time are obtained.

[0073] In some embodiments, based on the workpiece passing data, the number of repeatedly passing workpieces recorded by each equipment within the range of the start and end times is calculated. The number of repeatedly passing workpieces is divided by the production time to obtain the number of repeatedly passing workpieces per unit time for each equipment, that is, the number of repeatedly passing workpieces per unit time.

[0074] In some embodiments, waste data is extracted by matching the workpiece number with the workpiece line data, calculating the amount of waste for each equipment within the range of start and end times, and dividing the amount of waste by the production time to obtain the amount of waste per unit time for each equipment.

[0075] In some embodiments, scrap data is extracted by matching workpiece numbers with workpiece line data to calculate the amount of scrap for each piece of equipment within the range of start and end times. The amount of scrap is then divided by the production time to obtain the amount of scrap per unit time for each piece of equipment.

[0076] Step S3032: Based on the number of scrap parts of different scrap types produced by each equipment within a unit time, obtain the number of production cycles consumed by each equipment in a unit time for repetitive workpieces, industrial waste, and material waste.

[0077] Specifically, the number of repetitive workpieces produced per unit time, the number of scrapped workpieces, and the number of scrapped materials are respectively taken as the number of production cycles consumed per unit time for each of these repetitive workpieces, scrapped workpieces, and scrapped materials. For example, such as... Figure 4 As shown in (d), for example, if the quantity of waste is 8 and the production time is 8 hours, then the quantity of waste per unit time is 1, and the number of production cycles consumed by the waste per unit time is 1.

[0078] For example, the formula for calculating the second unit number of iterations is as follows:

[0079] in, , This represents the number of production cycles consumed per unit time for waste type k.

[0080] This embodiment quantifies different types of quality losses, such as repeated production runs, work waste, and material waste, into the number of production cycles consumed per unit time. This allows for the analysis of the bottleneck impact of work waste caused by process problems, material waste caused by material problems, and repeated production runs on equipment and processes. It effectively solves the problem that quality factors are difficult to quantify and account for in traditional bottleneck analysis, and that it is difficult to trace the cause of bottlenecks.

[0081] Step S304: Based on the first unit cycle count and the second unit cycle count of each device, obtain the bottleneck identification result of the production line. The bottleneck identification result includes the bottleneck process and / or bottleneck equipment and / or bottleneck cause.

[0082] Specifically, step S304 includes: Step S3041: Calculate the third unit cycle number of each device based on the first unit cycle number and the second unit cycle number of each device; the third unit cycle number is used to characterize the number of production cycles consumed per unit time for different device states and different types of scrap parts.

[0083] Specifically, for each piece of equipment, the unit cycle count is supplemented with the unit cycle counts consumed by three types of scrap: repeated production runs, industrial waste, and material waste. This supplements the number of repeated production runs, industrial waste, and material waste per unit time. The sum of these supplemented unit cycle counts is then subtracted from the unit cycle count corresponding to the equipment's production state, resulting in the third unit cycle count ACH3 for each piece of equipment. The third unit cycle count characterizes the production cycle count consumed per unit time for different equipment states and different scrap types; it includes unit cycle counts for various equipment states, repeated production runs, industrial waste, and material waste data. See again. Figure 4In (c), the original unit cycle count corresponding to state (in production) was 26. See again. Figure 4 In (d), after replenishing the unit cycle number corresponding to the waste material, the unit cycle number corresponding to the current state in production is 25.

[0084] In some embodiments, such as Figure 5 As shown in (a), for a process that includes a single machine, the third unit cycle number of the process is the same as that of the machine.

[0085] In some embodiments, for a process involving multiple parallel devices, the third unit cycle count of all devices in the process is summed according to various device states, repetitive workpieces, work waste, and material waste, to form the unit cycle count of the process corresponding to the device state, repetitive workpieces, work waste, and material waste, respectively. Figure 5 In the diagram, (b) and (c) respectively correspond to the unit cycle count diagram of two machines in one process. Figure 5 (d) in the diagram is a combined schematic of the unit cycle counts of the two devices.

[0086] Step S3042: For each device, based on the third unit cycle count of the device, extract the number of production cycles consumed per unit time for the production-related equipment status of the device to obtain the net production capacity index of the device; wherein, the production-related equipment status includes material blockage, waiting for material, and production.

[0087] Specifically, among the various equipment states, repetitive workpieces, and waste materials corresponding to each piece of equipment, the sum of the unit cycle counts corresponding to production-related equipment states such as "in production," "material blockage," and "waiting for material" represents the production capacity of that process. By summing the unit cycle counts corresponding to each production-related equipment state, the net production capacity index of the equipment is obtained. ,Right now .

[0088] Step S3043: Based on the net production capacity index of all equipment in each process, calculate the net production capacity index of each process, and identify the process with the smallest net production capacity index to obtain the bottleneck process of the production line.

[0089] Specifically, the net production capacity index of all equipment within each process is summed to obtain the process net production capacity index for each process. ,Right now Where M represents the total number of devices in the process. Let be the net production capacity index of the j-th equipment. The smaller the net production capacity index of a process, the lower its production capacity and the more prominent the bottleneck. Therefore, the process with the smallest net production capacity index is the bottleneck process of the production line.

[0090] This invention obtains the net production capacity index of equipment by statistically analyzing the number of unit cycles per unit in production-related equipment states such as material blockage, waiting for material, and production. Furthermore, it categorizes and sums the number of unit cycles per unit in production-related equipment states within a process to obtain the process's net production capacity index, thereby quantifying the actual effective output capacity of the process. Compared to traditional solutions that simply consider static cycle time, this invention can accurately identify the bottleneck process restricting overall output in complex production scenarios based on the process with the lowest net production capacity index, providing a basis for production line balance optimization and resource scheduling.

[0091] In some optional implementations, step S304 further includes the following steps: Step b1: Identify the equipment with the lowest net production capacity index within the bottleneck process to obtain the bottleneck equipment of the production line.

[0092] Specifically, if the bottleneck process consists of multiple machines operating in parallel, then the machines within that process are further compared. The sum of the number of unit cycles corresponding to the machine's production states (such as in production, blocked, and waiting for materials) is the machine's net production capacity index. The machine with the lowest net production capacity index is the bottleneck in the bottleneck process, thus identifying the bottleneck machine affecting the production line efficiency.

[0093] This embodiment obtains a net production capacity index for quantifying equipment production capacity by summing the number of unit cycles corresponding to the production equipment status of each device within the identified bottleneck process. The device with the smallest net production capacity index is then identified as the bottleneck device, thus identifying the bottleneck within the process.

[0094] Step b2: Identify the target equipment state and target scrap type that consumes the most production cycles per unit time for the bottleneck process and / or bottleneck equipment; wherein, the target equipment state is the equipment state other than the production equipment state.

[0095] Specifically, among the third unit cycle counts corresponding to various equipment states and scrap types of each bottleneck process and / or bottleneck equipment, the target equipment state and target scrap type with the highest third unit cycle count are determined.

[0096] In some embodiments, among the third unit cycle counts corresponding to various equipment states and scrap types of each bottleneck process and / or bottleneck equipment, categories other than production, material blockage, and waiting for material (such as emergency stop, cutter jam, cycle stop, repeated line crossing, and work waste) as well as repeated line crossing, work waste, and material waste can all be regarded as bottleneck causes.

[0097] It should be noted that a higher value for the third unit cycle count indicates more unit cycle counts are consumed, thus increasing its weight as a bottleneck cause. Among these, repeated line runs, workpiece scrap, and material scrap affect workpiece quality, while the remaining bottleneck causes affect equipment availability.

[0098] Step b3: Based on the target equipment status and the target scrap type, determine the bottleneck cause of the production line.

[0099] For example, if the target device with the highest number of cycles in the third unit is in an abnormal shutdown state, it indicates that the bottleneck of the production line is caused by abnormal equipment shutdown.

[0100] In some embodiments, the bottleneck device is further analyzed. If the sum of the third unit cycle counts of various states of the bottleneck device is lower than the preset unit cycle count, it indicates that the bottleneck device has insufficient performance, and the device performance problem is regarded as the bottleneck cause of the bottleneck device.

[0101] This embodiment sorts the number of unit cycles in various non-production states, automatically identifies and sorts the target equipment state and target scrap type that cause the bottleneck, achieves accurate location of the bottleneck cause, effectively removes the bottleneck, increases overall production capacity, and improves the efficiency of bottleneck problem solving.

[0102] In some optional implementations, the third unit cycle count, equipment net production capacity index, and process net production capacity index of each process are visualized.

[0103] Specifically, after identifying bottleneck processes, bottleneck equipment, and analyzing bottleneck causes, the system can visually display the analysis results through a graphical interface. For example, it can provide visual charts in the form of stacked bar charts.

[0104] For example, Figure 6 The process bottleneck overview chart shown uses stacked bars to display the bottleneck situation of each process on the entire production line. The X-axis represents the process number, and the Y-axis represents ACH (Average Cost of Work). This process bottleneck overview chart can be sorted in two ways: the first is arranged in the original order of process number, and the second is arranged from high to low net production capacity index. The latter can directly highlight the process with the lowest net production capacity index, which is the bottleneck process on the production line.

[0105] See you again Figure 6 In this chart, each process bar chart is composed of stacked ACH values ​​for production in progress, material blockage, waiting for material, and other states such as emergency stop, tool change, and cycle stop, distinguished by different colors. For example, process 5 is ranked first, meaning that process 5 is the bottleneck process, and the proportion of ACH values ​​for abnormal shutdowns in process 5 is relatively large, indicating that the abnormal shutdown of process 5 is the cause of the bottleneck.

[0106] For example, Figure 7 The equipment bottleneck overview diagram shown further illustrates the detailed bottleneck situation of each piece of equipment within the identified bottleneck process. The X-axis represents the equipment number, the Y-axis represents the ACH (Automated Choke Point), and the bar chart also uses a stacked format to display the status of various equipment and the ACH corresponding to different scrap types. This equipment bottleneck overview diagram allows for a direct comparison of the performance of various equipment within the same process, quickly pinpointing the bottleneck equipment in that process. Figure 7 As shown in the figure, the bottleneck data of the four devices in the bottleneck process 5 are displayed. Among them, the ACH of device 3 is the lowest when it is in production, blocked, or waiting for material, indicating that device 3 is the bottleneck device. The abnormal shutdown rate is relatively large, indicating that the abnormal shutdown of device 3 is the cause of the bottleneck.

[0107] For example, Figure 8 The equipment bottleneck trend chart shown illustrates the changing trend of bottleneck data for a specific piece of equipment over a period of time, categorized by shift. The X-axis represents the date or shift number, and the Y-axis represents the shift number (ACH). This bottleneck trend chart allows observation of how equipment bottleneck conditions change across different shifts, aiding in the analysis of long-term stability and trend changes in the production process. Figure 8 As shown in the diagram, the bottleneck trend chart of this equipment displays the bottleneck data trend of equipment 3 in process 5 over the past few shifts. In the latest shift, its abnormal downtime accounted for a large proportion, causing it to become the bottleneck of the entire production line.

[0108] In some embodiments, to meet the management needs of different levels, the visualization system provides a simplified display mode. Detailed equipment status can be grouped and merged by status category, and quality data such as duplicate runs, waste materials, and scrap materials can be unified into a single quality group. This simplifies chart display, allowing managers to quickly pinpoint bottleneck locations without being overwhelmed by excessive details. When detailed analysis is needed, the system can be restored to a detailed status display mode by adjusting parameters. Through this complete bottleneck analysis visualization system, a complete closed loop is achieved, from locating bottlenecks across the entire production line and comparing equipment within processes to analyzing bottleneck change trends.

[0109] The bottleneck identification method for production lines of the present invention will be described below with reference to a specific application example.

[0110] like Figure 9 As shown, this application example provides a bottleneck identification system, which includes a data acquisition module, a data processing module, a bottleneck analysis module, and a visualization output module, and the modules work together.

[0111] Specifically, the data acquisition module includes an equipment status acquisition unit, a cycle time data acquisition unit, and a data interface unit. The equipment status acquisition unit communicates with the equipment controller, obtains the equipment status code through polling, and collects the equipment number, status code, and status change timestamp when the status changes. The cycle time data acquisition unit communicates with the equipment controller, and collects and records the equipment number, production cycle time, and completion timestamp each time the equipment completes a production cycle. The data interface unit is used to acquire scheduling data, waste data, and material waste data from external systems such as the MES system and the Quality Management System (QMS).

[0112] The data processing module receives raw data from the data acquisition module and cleans the raw data to prepare a clean and well-organized dataset for bottleneck identification.

[0113] The bottleneck analysis module includes a net productivity index calculation submodule, a bottleneck station identification submodule, and a root cause analysis submodule. The net productivity index calculation submodule calculates the number of cycles per unit for each piece of equipment and process based on the cleaned data, and further calculates the final net productivity index. The bottleneck station identification submodule has built-in bottleneck station identification logic, sorts the processes based on the calculated net productivity index, and identifies the bottleneck processes and equipment. The root cause analysis submodule, after identifying the bottleneck location, automatically analyzes and outputs the main causes of the bottleneck, such as equipment failure, material waiting, or quality defects.

[0114] The visualization output module displays bottleneck identification results, including a process bottleneck overview chart, an equipment bottleneck overview chart, and an equipment bottleneck trend chart. All three charts are stacked bar charts, with the X-axis representing the process number or equipment number and the Y-axis representing the ACH (Automatic Control Point). The process bottleneck overview chart can be sorted by process number and net production capacity index to identify bottleneck processes. When it's not necessary to directly view the detailed bottleneck causes, but only to quickly pinpoint the bottleneck, detailed status can be grouped and merged by equipment status. Simultaneously, quality data can be merged into quality groups, reducing the categories in the charts and providing a more concise display of bottleneck data.

[0115] This invention constructs a standardized set of equipment status definitions and priority collection rules, integrates multi-source data such as equipment status, production cycle time data, and quality information, and designs a net production capacity index model based on unit cycle count. This enables dynamic identification and attribution analysis of bottleneck workstations and their root causes on the production line, effectively distinguishing bottlenecks caused by different factors such as equipment performance, material supply, or product defects. It breaks through the limitations of traditional single-data analysis, making implicit bottlenecks explicit. Combined with a modular system architecture design, it provides manufacturing enterprises with a real-time, accurate, and traceable bottleneck diagnosis and optimization solution.

[0116] This invention is applicable to various production lines in the discrete manufacturing field, such as machining, assembly, and testing. Through standardized data governance, multi-dimensional fusion analysis logic, and dynamic attribution mechanism, it realizes a closed loop from data acquisition to decision feedback, providing quantifiable technical support for efficiency optimization of various discrete manufacturing production lines in intelligent manufacturing. It helps enterprises locate the root cause of bottlenecks in real time and improve overall production efficiency.

[0117] This embodiment also provides a production line bottleneck identification device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0118] This embodiment provides a production line bottleneck identification device, such as... Figure 10 As shown, the production line includes multiple sequential processes, each process includes at least one piece of equipment, and each piece of equipment includes multiple equipment states; the bottleneck identification device for this production line includes: The first processing module 1001 is used to acquire production line data of each device; wherein, the production line data includes device status data, production cycle data and quality loss data; The second processing module 1002 is used to obtain the first unit cycle count of each device based on the device status data and production cycle data. The first unit cycle count is used to characterize the number of production cycles consumed per unit time for different device states. Each production cycle count corresponds to the production time of one workpiece. The third processing module 1003 is used to obtain the second unit cycle number of each device based on the quality loss data; the second unit cycle number is used to characterize the number of production cycles consumed per unit time for different types of scrap parts of the device. The fourth processing module 1004 is used to obtain the bottleneck identification result of the production line based on the first unit cycle number and the second unit cycle number of each device. The bottleneck identification result includes the bottleneck process and / or bottleneck equipment and / or bottleneck cause.

[0119] In some optional implementations, the second processing module 1002 is further configured to: The planned production time of the production line is determined, and the total duration of each equipment status is obtained based on the equipment status data within the planned production time. The equipment status includes: offline, shutdown, emergency stop, abnormal stop, tool change, idling, maintenance, through mode, non-automatic mode, cyclic stop, calibration, material blockage, waiting for material, and production. For each device, based on the device's production time and the total duration of each device state, the occupancy time of each device state per unit time is obtained; For each piece of equipment, the first unit cycle count is obtained based on the production cycle data and the time occupied by each equipment state within a unit of time.

[0120] In some optional implementations, the second processing module 1002 is further configured to: The average production cycle time of the equipment is obtained based on the production cycle time data; For each equipment state, the number of production cycles consumed per unit time is obtained based on the ratio between the equipment state's occupied time per unit time and the average production cycle time.

[0121] In some optional implementations, the third processing module 1003 is further configured to: Based on the quality loss data of each piece of equipment during the production time, the quantity of different types of scrap produced by each piece of equipment per unit time is obtained; scrap types include rework pieces, industrial scrap, and material scrap. Based on the number of different types of scrap produced by each piece of equipment per unit time, the number of production cycles consumed per unit time for each piece of equipment in terms of repeated workpieces, industrial waste, and material waste is obtained.

[0122] In some optional implementations, the fourth processing module 1004 is further configured to: The third unit number of each device is calculated based on the first and second unit number of each device. The third unit number of each device is used to characterize the number of production cycles consumed per unit time for different device states and different types of scrap. For each piece of equipment, based on the third unit cycle count of the equipment, the number of production cycles consumed per unit time in the production equipment status of the equipment is extracted to obtain the equipment's net production capacity index; among which, the production equipment status includes material blockage, waiting for material, and production; Based on the net production capacity index of all equipment in each process, the net production capacity index of each process is calculated, and the process with the smallest net production capacity index is identified, thus obtaining the bottleneck process of the production line.

[0123] In some optional implementations, the fourth processing module 1004 is further configured to: Identify the equipment with the lowest net production capacity index within the bottleneck process to obtain the bottleneck equipment of the production line.

[0124] In some optional implementations, the fourth processing module 1004 is further configured to: Identify the target equipment state and target scrap type that consumes the most production cycles per unit time for bottleneck processes and / or bottleneck equipment; where the target equipment state is the equipment state other than production equipment state. Based on the status of the target equipment and the type of the target scrap, the bottleneck cause of the production line is determined.

[0125] In some optional implementations, the fourth processing module 1004 is further configured to: The third unit cycle count, net production capacity index of each piece of equipment, and net production capacity index of each process are visualized.

[0126] The production line bottleneck identification device provided in this embodiment of the invention can execute the production line bottleneck identification method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the various modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0127] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0128] The following is a detailed reference. Figure 11 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 1101, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1102 or a program loaded from memory 1108 into random access memory (RAM) 1103. The RAM 1103 also stores various programs and data required for the operation of the electronic device. The processor 1101, ROM 1102, and RAM 1103 are interconnected via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0129] Typically, the following devices can be connected to I / O interface 1105: input devices 1106 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 1107 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 1108 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1109. Communication device 1109 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 11 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0130] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 1109, or installed from a memory 1108, or installed from a ROM 1102. When the computer program is executed by the processor 1101, it performs the functions defined in the production line bottleneck identification method of the embodiments of the present invention.

[0131] Figure 11 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0132] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the production line bottleneck identification method shown in the above embodiments is implemented.

[0133] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0134] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for identifying bottlenecks in a production line, characterized in that, The production line includes multiple sequential processes, each process including at least one piece of equipment, and each piece of equipment including multiple equipment states; the method includes: Acquire production line data for each piece of equipment; wherein, the production line data includes equipment status data, production cycle time data, and quality loss data; Based on the equipment status data and the production cycle data, the first unit cycle count of each equipment is obtained; the first unit cycle count is used to characterize the number of production cycles consumed per unit time for different equipment states, and each production cycle count corresponds to the production time of one workpiece. Based on the quality loss data, the second unit cycle count for each device is obtained; the second unit cycle count is used to characterize the number of production cycles consumed per unit time for different types of scrap parts of the device. Based on the first unit cycle count and the second unit cycle count of each device, the bottleneck identification result of the production line is obtained. The bottleneck identification result includes the bottleneck process and / or bottleneck equipment and / or bottleneck cause.

2. The production line bottleneck identification method according to claim 1, characterized in that, The step of obtaining the first unit cycle number for each piece of equipment based on the equipment status data and the production cycle time data includes: The planned production time of the production line is determined, and the total duration of each equipment state is obtained based on the equipment status data within the planned production time. The equipment states include: offline, shutdown, emergency stop, abnormal stop, tool change, idling, maintenance, through mode, non-automatic mode, cyclic stop, calibration, material blockage, waiting for material, and production. For each device, the occupancy time of each device state per unit time is obtained based on the device's production time and the total duration of each device state. For each device, the first unit cycle number is obtained based on the production cycle data and the time occupied by each device state within a unit of time.

3. The production line bottleneck identification method according to claim 2, characterized in that, The step of obtaining the first unit cycle number of the equipment based on the production cycle data and the occupancy time of each equipment state within a unit of time includes: The average production cycle time of the equipment is obtained based on the production cycle time data. For each equipment state of the equipment, the number of production cycles consumed by the equipment state per unit time is obtained based on the ratio between the occupied time of the equipment state per unit time and the average production cycle time.

4. The production line bottleneck identification method according to claim 2, characterized in that, The step of obtaining the second unit cycle number for each device based on the quality loss data includes: Based on the quality loss data of each piece of equipment during the production time, the quantity of different types of scrap produced by each piece of equipment per unit time is obtained; the scrap types include reworked parts, industrial scrap, and material scrap. Based on the number of different types of scrap produced by each piece of equipment per unit time, the number of production cycles consumed per unit time for each piece of equipment in terms of repeated workpieces, industrial waste, and material waste is obtained.

5. The production line bottleneck identification method according to any one of claims 2-4, characterized in that, The bottleneck identification result of the production line, based on the first unit cycle number and the second unit cycle number of each device, includes: The third unit number of each device is calculated based on the first unit number of cycles and the second unit number of cycles for each device; the third unit number of cycles is used to characterize the number of production cycles consumed per unit time for different device states and different types of scrap. For each device, based on the third unit cycle count of the device, the number of production cycles consumed by the production equipment status of the device per unit time is extracted to obtain the net production capacity index of the device; wherein, the production equipment status includes material blockage, waiting for material, and production; Based on the net production capacity index of all equipment in each process, the net production capacity index of each process is calculated, and the process with the smallest net production capacity index is identified, thus obtaining the bottleneck process of the production line.

6. The production line bottleneck identification method according to claim 5, characterized in that, The process of obtaining the bottleneck identification result of the production line based on the first unit cycle count and the second unit cycle count of each device also includes: The bottleneck equipment in the bottleneck process is identified by determining the equipment with the lowest net production capacity index.

7. The production line bottleneck identification method according to claim 6, characterized in that, The process of obtaining the bottleneck identification result of the production line based on the first unit cycle count and the second unit cycle count of each device also includes: Identify the target equipment state and target scrap type that consumes the most production cycles per unit time for the bottleneck process and / or bottleneck equipment; wherein, the target equipment state is an equipment state other than production equipment state; Based on the target equipment status and the target scrap type, the bottleneck cause of the production line is determined.

8. The production line bottleneck identification method according to claim 5, characterized in that, The method further includes: The third unit cycle count, net production capacity index of each piece of equipment, and net production capacity index of each process are visualized.

9. A bottleneck identification device for a production line, characterized in that, The production line includes multiple processes connected in series, each process includes at least one piece of equipment, and each piece of equipment includes multiple equipment states; the device includes: The first processing module is used to acquire production line data for each device; wherein, the production line data includes device status data, production cycle time data, and quality loss data; The second processing module is used to obtain the first unit cycle count of each device based on the device status data and the production cycle count data; the first unit cycle count is used to characterize the number of production cycles consumed per unit time for different device states, and each production cycle count corresponds to the production time of one workpiece. The third processing module is used to obtain the second unit cycle number of each device based on the quality loss data; the second unit cycle number is used to characterize the number of production cycles consumed per unit time for different types of scrap parts of the device; The fourth processing module is used to obtain the bottleneck identification result of the production line based on the first unit cycle number and the second unit cycle number of each device. The bottleneck identification result includes the bottleneck process and / or bottleneck equipment and / or bottleneck cause.

10. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the production line bottleneck identification method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the production line bottleneck identification method according to any one of claims 1 to 8.

12. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the production line bottleneck identification method according to any one of claims 1 to 8.

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