Production management method and system based on bar code recognition

Abstract

The invention relates to the technical field of production management, and discloses a production management method and system based on bar code recognition, and the method comprises the steps: carrying out the bar code identification of each process of a production line, recording the starting and ending time, and calculating the actual working time; and comparing the man-hour quota with the actual man-hour to obtain difference data. Judging the process state according to the difference data, and recording and alarming when the state is abnormal; meanwhile, the man-hour quota is adjusted according to historical data and difference data. According to the invention, a bar code identification technology is utilized, rapid scanning and storage of bar code information are realized, and data processing efficiency and accuracy are improved; meanwhile, by calculating multiple production indexes, the stability and efficiency of the production process are comprehensively evaluated; by analyzing the man-hour difference data, the abnormal state of the process is judged, an alarm is given in time, and the controllability and safety of the production process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production management, and in particular to a production management method and system based on barcode recognition. Background Art

[0002] With the continuous development of science and technology, modern manufacturing is facing unprecedented challenges and opportunities. In this context, refined management and efficiency optimization are crucial to improving the operating efficiency and product quality of production lines. However, the current production management methods still have some disadvantages, such as lack of real-time data support and cumbersome management processes, which to some extent limit the improvement of production line operating efficiency and product quality.

[0003] Therefore, in response to the challenges faced by the current manufacturing industry, it is urgent to study a new production management method to overcome the limitations of existing methods. Summary of the invention

[0004] In view of this, the present invention proposes a production management method and system based on barcode recognition, aiming to realize the intelligence, efficiency and refinement of the production line to meet the urgent needs of the manufacturing industry for improving production efficiency, optimizing resource allocation and reducing costs.

[0005] The present invention proposes a production management method based on barcode recognition, comprising:

[0006] Mark each process on the production line with a barcode, and scan the barcode to record the start and end time of each process;

[0007] Calculate the actual working hours for each process;

[0008] Compare the working hours quota with the actual working hours to obtain working hours difference data;

[0009] According to the working time difference data, the abnormal state of each process is judged. When the judgment result is abnormal, the abnormal state is recorded and an alarm is issued;

[0010] At the same time, the working hour quota is adjusted based on historical data and working hour difference data.

[0011] Preferably, when barcodes are used to identify each process on the production line and the start and end time of each process are recorded by scanning the barcodes, the following steps are included:

[0012] Uniquely encode each process on the production line to form a process barcode;

[0013] At the beginning and end of a process, the process barcode is associated with the process operation information by scanning the process barcode;

[0014] Process operation information includes process name, operator, equipment status, material information and production quantity;

[0015] At the beginning of the process, by scanning the process barcode, the process barcode is associated with the process name, operator, equipment status and material information; at the same time, the timing function is turned on to record the start time and end time of the process.

[0016] Preferably, the actual working hours for each process are calculated, including:

[0017] Calculate the difference between the start time and end time of each process to get the actual time of each process;

[0018] Adjust the actual time according to the equipment's operating efficiency and the stability of the production environment to obtain the actual working hours;

[0019] Among them, the operating efficiency of the equipment is evaluated based on historical operating data, and the stability of the production environment is evaluated based on historical production data;

[0020] According to the operating efficiency of the equipment and the stability of the production environment, the actual time is adjusted to obtain the calculation formula for the actual working hours:

[0021]

[0022] Ta is the actual working hours; Tm is the actual time; E is the equipment operation efficiency coefficient, ranging from 0 to 1; S is the stability coefficient of the production environment, ranging from 0 to 1.

[0023] Preferably, the operating efficiency of the equipment is evaluated based on historical operating data, including:

[0024] Collect historical operation data on the production line, including equipment operation time, number of failures and maintenance records;

[0025] Conduct statistical analysis on historical operation data to calculate the average operation time, mean time between failures and mean repair time of equipment;

[0026] According to the statistical analysis results, the operating efficiency of the equipment is evaluated and the equipment operating efficiency coefficient E is obtained. The calculation formula is:

[0027]

[0028] Among them, Mf is the mean time between failures; Mr is the mean maintenance time; Tt is the total operating time; Tf is the total maintenance time; n is the number of failures; and E is the operating efficiency coefficient.

[0029] Preferably, the stability of the production environment is evaluated based on historical production data, including:

[0030] Collect historical production data on the production line, including production efficiency, production quality, number of production interruptions and equipment failure rate of each process;

[0031] Conduct statistical analysis on historical production data to calculate the average value of process production efficiency, the qualified rate of process production quality, the average number of production interruptions and the average value of equipment failure rate;

[0032] According to the statistical analysis results, the stability of the production environment is evaluated and the production environment stability coefficient S is obtained. The calculation formula is:

[0033]

[0034]

[0035] Among them, S is the production environment stability coefficient; A is the average value of the process production efficiency; Q is the qualified rate of the process production quality; D is the average number of production interruptions; F is the average value of the equipment failure rate; a is the total production efficiency of the process; Na is the number of processes; q is the total number of qualified products; Nq is the number of products produced; d is the total number of production interruptions; Nd is the statistical period; f is the total number of equipment failures; Nf is the total equipment operating time.

[0036] Preferably, the abnormal state of the working state of each process is determined based on the working time difference data, including:

[0037] Determine the time variance threshold, where:

[0038] Upper threshold = working hours quota + allowable error;

[0039] Lower limit threshold = working hours quota - allowable error;

[0040] Calculates the time variance, where:

[0041] Working hours difference = actual working hours - working hours quota;

[0042] When the actual working hours are not less than the lower threshold and not greater than the upper threshold, the process status is determined to be normal;

[0043] When the actual working hours are greater than the upper limit threshold, the process is judged to have exceeded the working hours, and an alarm for overspending will be issued;

[0044] When the actual working hours are less than the lower limit threshold, it is determined that the process working hours are insufficient, and an alarm prompts for insufficient working hours.

[0045] Preferably, when adjusting the working time quota according to the historical data and working time difference data, it includes:

[0046] Collect the actual working time data and working time quota data of each process within a certain time range, and record the working time difference of each process;

[0047] Calculating statistical values ​​of working time differences, and evaluating the accuracy of working time quotas based on the statistical values ​​of working time differences;

[0048] When the working time quota is inaccurate, adjust the working time quota based on the statistical value of the working time difference.

[0049] Preferably, when calculating the statistical value of the working time difference and evaluating the accuracy of the working time quota according to the statistical value of the working time difference, it includes:

[0050] Collect the actual working time data of each process in multiple production cycles, calculate the difference between each actual working time data and the current working time quota, and obtain the working time difference of each process in multiple production cycles; calculate the average working time difference of all processes, and calculate the standard deviation of the actual working time of all processes;

[0051] The mean value and the standard deviation are calculated according to the following formula;

[0052]

[0053] Among them, Ba is the average value of working time difference; Z is the number of data points; B is the working time difference; C is the standard deviation of working time difference;

[0054] The steps to assess the accuracy of time quotas based on the mean and standard deviation of time variances include:

[0055] When the average value of the working time difference is close to zero, the working time quota is determined to be accurate;

[0056] When the average value of the working time difference deviates significantly from zero, it is determined that there is a systematic deviation in the working time quota;

[0057] When the standard deviation of working time differences is small, it is judged that the dispersion of working time quota is low and working time is stable;

[0058] When the standard deviation of working hours is large, it is judged that the dispersion of working hours quota is high and working hours are unstable;

[0059] Among them, a mean value threshold of working time differences is pre-set, when the mean value of working time differences is less than the mean value threshold, it is determined that the mean value is close to zero; when the mean value of working time differences is not less than the mean value threshold, it is determined that the mean value deviates significantly from zero; at the same time, a standard deviation threshold of working time differences is pre-set, when the standard deviation of working time differences is less than the standard deviation threshold, it is determined that the standard deviation of working time differences is small; when the standard deviation of working time differences is not less than the standard deviation threshold, it is determined that the standard deviation of working time differences is large;

[0060] When there is a systematic deviation in the working time quota and the working hours are unstable, the accuracy of the working time quota is determined to be inaccurate; at this time, the working time quota is adjusted according to the average value and standard deviation of the working time difference.

[0061] Preferably, when there is a systematic deviation in the working hour quota and the working hours are unstable, the accuracy of the working hour quota is determined to be inaccurate; at this time, adjusting the working hour quota according to the average value and standard deviation of the working hour difference includes:

[0062] Calculate the adjusted working hours quota according to the following calculation formula:

[0063]

[0064] Among them, Tn is the adjusted working hours quota; To is the original working hours quota; is the average value of working time difference; σ d is the standard deviation; k and m are adjustment coefficients, k is used to adjust the average value of working time differences, and m is used to adjust the impact of standard deviation.

[0065] The present invention also provides a production management system based on barcode recognition, which is used to implement the above-mentioned production management method based on barcode recognition, and the production management system includes:

[0066] Barcode scanning module, used to scan barcode information during the production process;

[0067] A data storage module, used to store scanned barcode information and production data;

[0068] The data processing module is used to calculate the production stability coefficient, process production efficiency, production quality qualification rate, production interruption times and equipment failure rate based on the stored production data, and to determine the abnormal state of the working state of each process based on the working time difference data, and to adjust the working time quota based on the historical data and the working time difference data;

[0069] The alarm prompt module is used to issue an alarm prompt when the process working hours are overspent or insufficient;

[0070] The user interaction module is used to display the calculation results of production indicators and the abnormal status determination results.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] The present invention realizes the rapid scanning and storage of barcode information in the production process through barcode recognition technology, and improves the efficiency and accuracy of data processing. At the same time, the present invention can comprehensively evaluate the stability and efficiency of the production process by calculating production indicators such as production stability coefficient, process production efficiency, production quality qualification rate, number of production interruptions and equipment failure rate, and provide powerful decision-making support for production managers. In addition, the present invention also realizes the abnormal state judgment of the process working state through the analysis and processing of working time difference data, and can promptly issue an alarm prompt when an abnormality is found, thereby improving the controllability and safety of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0074] Figure 1 A flowchart of a production management method based on barcode recognition provided by an embodiment of the present invention;

[0075] Figure 2 This is a functional block diagram of a production management system based on barcode recognition provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0076] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0077] like Figure 1 As shown, this embodiment proposes a production management method based on barcode recognition, including:

[0078] S1. Label each process on the production line with a barcode, scan the barcode and record the start and end time of each process;

[0079] S2. Calculate the actual working hours for each process;

[0080] S3, comparing the working hour quota with the actual working hours to obtain working hour difference data;

[0081] S4. According to the working time difference data, the abnormal state of the working status of each process is judged. When the judgment result is abnormal, the abnormal state is recorded and an alarm is issued; at the same time, the working time quota is adjusted according to the historical data and the working time difference data.

[0082] It is understandable that this embodiment proposes an innovative production management method based on barcode recognition, which achieves accurate tracking and effective management of each process on the production line by introducing barcode technology. The following will elaborate on each step of the method and the logic and significance behind it.

[0083] First, each process on the production line is barcoded. This step is crucial because it ensures that each process can be uniquely and accurately identified. As an efficient information carrier, barcodes can carry key information such as process name, location, and operator, providing a solid foundation for subsequent data recording and analysis.

[0084] Next, the start and end time of each process is recorded by scanning the barcode. This step uses modern barcode scanning equipment to automatically record the process time. Compared with the traditional manual recording method, barcode scanning not only improves the accuracy of data recording, but also greatly reduces the impact of human factors on the data.

[0085] After obtaining the start and end time of each process, the actual working hours of each process can be calculated. This step can be achieved through simple mathematical operations, but the information it reflects is extremely valuable. The actual working hours not only reflect the execution efficiency of the process, but also provide data support for the subsequent adjustment of the working hours quota.

[0086] Subsequently, the working hours quota is compared with the actual working hours to obtain the working hours difference data. This step is the key link in evaluating the efficiency of process execution. By comparing the actual working hours with the working hours quota, it is possible to intuitively see whether the process is overtime or completed ahead of schedule, and then make a preliminary assessment of the execution status of the process.

[0087] According to the working time difference data, the abnormal state of each process is judged. When the judgment result is abnormal, the system will record the abnormal state and issue an alarm. This step makes full use of the information value of the working time difference data. By setting reasonable abnormal judgment standards, problems in the process of process execution can be discovered and handled in time to ensure the stable operation of the production line.

[0088] At the same time, the method also adjusts the working time quota based on historical data and working time difference data. This step reflects the flexibility and adaptability of the management method. As time goes by and the production environment changes, the working time quota may need to be adjusted accordingly. By continuously collecting and analyzing actual working time data, the working time quota can be gradually optimized to make it more in line with actual production conditions.

[0089] In general, the production management method based on barcode recognition proposed in this embodiment realizes accurate tracking and effective management of each process on the production line by introducing barcode technology. This method not only improves the accuracy and efficiency of data recording, but also provides strong data support for the evaluation of process execution status and the adjustment of working hours quota. In practical applications, this method is expected to help enterprises improve production efficiency, reduce costs and improve product quality.

[0090] In some embodiments of the present application, when barcodes are used to identify each process on a production line and the start time and end time of each process are recorded by scanning the barcodes, the following steps are included:

[0091] Uniquely encode each process on the production line to form a process barcode;

[0092] At the beginning and end of a process, the process barcode is associated with the process operation information by scanning the process barcode;

[0093] Process operation information includes process name, operator, equipment status, material information and production quantity;

[0094] At the beginning of the process, by scanning the process barcode, the process barcode is associated with the process name, operator, equipment status and material information; at the same time, the timing function is turned on to record the start time and end time of the process.

[0095] It is understandable that, for each process on the production line, this embodiment adopts a meticulous barcode identification and scanning recording method to achieve accurate control and efficient management of the production process. This method not only helps to optimize production efficiency, but also improves product quality and reduces production costs.

[0096] First, in order to ensure the uniqueness of the identification of each process, we uniquely encode each process on the production line to form a process barcode. These barcodes are like the "identity cards" of each process, which not only facilitate our identification but also ensure the accuracy of the information.

[0097] Next, at the beginning and end of the process, we scan the process barcode and associate it with the process operation information. The process operation information includes key elements such as process name, operator, equipment status, material information, and production quantity. This step not only closely connects the process with the operation information, but also provides a basis for subsequent data analysis and optimization.

[0098] At the beginning of the process, pay special attention to several key steps. First, by scanning the process barcode, associate the process barcode with the process name, operator, equipment status and material information. In this way, you can clearly know which operator is responsible for which process, the status of the equipment, and the material information used. At the same time, turn on the timing function to record the start and end time of the process. These data not only help us understand the time consumption of the process, but also help us analyze the bottlenecks and optimization space in the production process.

[0099] In addition, in order to further improve production efficiency and quality, this embodiment also introduces some advanced technical means in the scanning and recording process. For example, by using RFID technology or mobile scanning equipment, the process barcode is scanned quickly and accurately, which greatly improves the efficiency and accuracy of data collection. At the same time, big data analysis and artificial intelligence technology are also used to deeply mine and analyze the collected data to find potential problems and optimization space in the production process.

[0100] In general, by barcode identification and scanning records of each process on the production line, we can achieve accurate control and efficient management of the production process. This not only helps to improve production efficiency and quality, but also helps us better optimize the production process and reduce production costs.

[0101] In some embodiments of the present application, the actual working hours of each process are calculated, including:

[0102] Calculate the difference between the start time and end time of each process to get the actual time of each process;

[0103] Adjust the actual time according to the equipment's operating efficiency and the stability of the production environment to obtain the actual working hours;

[0104] Among them, the operating efficiency of the equipment is evaluated based on historical operating data, and the stability of the production environment is evaluated based on historical production data;

[0105] According to the operating efficiency of the equipment and the stability of the production environment, the actual time is adjusted to obtain the calculation formula for the actual working hours:

[0106]

[0107] Ta is the actual working hours; Tm is the actual time; E is the equipment operation efficiency coefficient, ranging from 0 to 1; S is the stability coefficient of the production environment, ranging from 0 to 1.

[0108] It is understandable that this embodiment deeply explores how to accurately calculate the actual working hours of each process. This process involves considerations at multiple levels, aiming to more accurately reflect the time consumption in the actual production process.

[0109] First, the difference between the start time and the end time of each process is calculated to obtain the preliminary actual time of each process. This step is basic and necessary, as it provides us with an initial time data and provides a basis for subsequent adjustments.

[0110] However, the actual time calculated solely based on the difference often cannot fully reflect the real situation. This is because the operating efficiency of the equipment and the stability of the production environment have a significant impact on the actual working time. Therefore, the actual time needs to be adjusted according to these factors to obtain a more accurate actual working time.

[0111] The operating efficiency of the equipment is evaluated with the help of historical operating data. These data record the operating status and performance of the equipment in the past period of time. By analyzing these data, the operating efficiency coefficient E of the equipment can be obtained. This coefficient reflects the efficiency level of the equipment under normal operating conditions, and the value range is usually 0 to 1, where 1 indicates the highest operating efficiency of the equipment and 0 indicates the lowest operating efficiency of the equipment.

[0112] Similarly, in order to evaluate the stability of the production environment, historical production data is used as a reference. These data cover various influencing factors in the production process and derive the stability coefficient S of the production environment. This coefficient reflects the degree of influence of the production environment on the production process, and the value range is also 0 to 1, where 1 indicates the most stable production environment and 0 indicates the most unstable production environment.

[0113] After obtaining the equipment operating efficiency coefficient E and the production environment stability coefficient S, these coefficients can be used to adjust the actual working hours to obtain the actual working hours Ta. The calculation formula comprehensively considers the impact of equipment operating efficiency and production environment stability on actual working hours, making the calculation results closer to the actual situation.

[0114] By implementing this calculation method, we can more accurately understand the actual working hours of each process, thus providing strong support for production management and optimization. At the same time, this method can also help us to timely discover potential problems in the production process, such as abnormal equipment operation or unstable production environment, so as to take timely measures to solve them and ensure the smooth progress of the production process.

[0115] In some embodiments of the present application, the operating efficiency of the equipment is evaluated based on historical operating data, including:

[0116] Collect historical operation data on the production line, including equipment operation time, number of failures and maintenance records;

[0117] Conduct statistical analysis on historical operation data to calculate the average operation time, mean time between failures and mean repair time of equipment;

[0118] According to the statistical analysis results, the operating efficiency of the equipment is evaluated and the equipment operating efficiency coefficient E is obtained. The calculation formula is:

[0119]

[0120] Among them, Mf is the mean time between failures; Mr is the mean maintenance time; Tt is the total operating time; Tf is the total maintenance time; n is the number of failures; and E is the operating efficiency coefficient.

[0121] It is understandable that this embodiment deeply explores how to use historical operation data to accurately evaluate the operating efficiency of the equipment. This method not only helps us to understand the operating status of the equipment more comprehensively, but also provides strong data support for the optimization and maintenance of the equipment.

[0122] First, collect comprehensive historical operation data on the production line. This data covers key information such as equipment operation time, number of failures, and maintenance records. Through detailed data collection, a rich data set can be built, providing a solid foundation for subsequent analysis and evaluation.

[0123] Next, we used advanced statistical analysis methods to conduct in-depth mining of historical operation data. By calculating the equipment operation time, failure interval time and maintenance time, we obtained key indicators such as the average operation time, mean failure interval time and mean maintenance time of the equipment. These indicators can intuitively reflect the operation efficiency and stability of the equipment, providing a strong basis for subsequent evaluation work.

[0124] After obtaining these statistical analysis results, a scientific evaluation method is further used to comprehensively evaluate the operating efficiency of the equipment. This embodiment adopts a comprehensive evaluation method based on indicators such as equipment operating time, fault interval time, maintenance time, and number of faults, and quantifies the operating efficiency of the equipment by calculating the equipment operating efficiency coefficient E. This coefficient can comprehensively consider multiple aspects of the equipment, thereby more accurately reflecting the overall performance of the equipment.

[0125] In the specific calculation process, Mf is the mean time between failures, which indicates the average time interval between failures of the equipment during continuous operation; Mr is the mean maintenance time, which indicates the average time required for each maintenance of the equipment; Tt is the total operating time, which indicates the total operating time of the equipment since it was put into use; Tf is the total maintenance time, which indicates the total time the equipment has stopped running due to maintenance since it was put into use; n is the number of failures, which indicates the number of failures that occurred during the operation of the equipment; E is the operating efficiency coefficient, which is used to quantitatively evaluate the operating efficiency of the equipment.

[0126] This evaluation method can produce an objective and accurate equipment operating efficiency coefficient E. This coefficient not only reflects the equipment's operating efficiency, but also identifies possible problems and bottlenecks during the equipment's operation, providing an important reference for subsequent optimization and maintenance work.

[0127] In addition, the evaluation results can be analyzed and discussed in depth in combination with the actual situation. For example, the operating efficiency coefficients of different equipment can be compared to find out the equipment with excellent performance and the equipment that needs improvement; the changing trend of the equipment operating efficiency coefficient over time can be analyzed to understand whether the equipment's operating status is stable or whether there is a downward trend. These analyses will help us understand the equipment's operating status more comprehensively and provide strong support for equipment optimization and maintenance.

[0128] In short, by collecting historical operating data, conducting statistical analysis, and evaluating the equipment operating efficiency coefficient E, we can have a more comprehensive understanding of the equipment's operating status and provide strong data support for equipment optimization and maintenance.

[0129] In some embodiments of the present application, the stability of the production environment is evaluated based on historical production data, including:

[0130] Collect historical production data on the production line, including production efficiency, production quality, number of production interruptions and equipment failure rate of each process;

[0131] Conduct statistical analysis on historical production data to calculate the average value of process production efficiency, the qualified rate of process production quality, the average number of production interruptions and the average value of equipment failure rate;

[0132] According to the statistical analysis results, the stability of the production environment is evaluated and the production environment stability coefficient S is obtained. The calculation formula is:

[0133]

[0134]

[0135] Among them, S is the production environment stability coefficient; A is the average value of the process production efficiency; Q is the qualified rate of the process production quality; D is the average number of production interruptions; F is the average value of the equipment failure rate; a is the total production efficiency of the process; Na is the number of processes; q is the total number of qualified products; Nq is the number of products produced; d is the total number of production interruptions; Nd is the statistical period; f is the total number of equipment failures; Nf is the total equipment operating time.

[0136] It is understandable that this embodiment proposes a method for comprehensively evaluating the stability of the production environment based on historical production data. This method can not only help us gain a deeper understanding of the actual operating status of the production line, but also provide strong data support for the optimization of the production environment.

[0137] First, the historical production data on the production line is systematically and comprehensively collected. This data includes, but is not limited to, key indicators such as production efficiency, production quality, number of production interruptions, and equipment failure rate of each process. The collection scope of this data covers all aspects of the production line to ensure that the overall situation of the production environment can be fully understood.

[0138] Next, we conduct in-depth statistical analysis on these historical production data. By calculating the average production efficiency of the process, we can understand the average production efficiency level of the production line under stable operation; by calculating the qualified rate of process production quality, we can evaluate the stability and reliability of product quality; by calculating the average number of production interruptions and the average equipment failure rate, we can understand the unstable factors of the production environment and their impact.

[0139] Based on the statistical analysis, the stability of the production environment is further evaluated according to the various data results obtained. In order to quantify the evaluation results, an indicator called "production environment stability coefficient S" is introduced. The calculation formula of this coefficient integrates data from multiple dimensions such as production efficiency, production quality, number of production interruptions and equipment failure rate, aiming to fully reflect the stability level of the production environment.

[0140] Specifically, A is the average value of process production efficiency, Q is the qualified rate of process production quality, D is the average number of production interruptions, and F is the average value of equipment failure rate. These parameters are obtained through statistical analysis of historical production data and can accurately reflect the actual situation of the production environment.

[0141] In addition, in order to more accurately calculate the production environment stability coefficient S, some auxiliary parameters need to be introduced. For example, a represents the total production efficiency of the process, Na represents the number of processes; q represents the total number of qualified products, Nq represents the number of products produced; d represents the total number of production interruptions, Nd represents the statistical cycle; f represents the total number of equipment failures, and Nf represents the total equipment operation time. These parameters play a key regulatory role in the calculation formula, making the calculation of the production environment stability coefficient S more accurate and reliable.

[0142] Through this method, the stability of the production environment can be comprehensively evaluated based on historical production data. When the production environment stability coefficient S is high, it means that the production environment is relatively stable, with high production efficiency, good product quality, few production interruptions, and low equipment failure rate; otherwise, it means that the production environment is highly unstable and corresponding optimization measures need to be taken for specific problems.

[0143] In short, the method of evaluating the stability of the production environment based on historical production data has practical application value. It can help us gain a deeper understanding of the actual operating status of the production line, discover potential problems and hidden dangers, and provide strong data support for the optimization of the production environment.

[0144] In some embodiments of the present application, the abnormal state of the working state of each process is determined based on the working time difference data, including:

[0145] Determine the time variance threshold, where:

[0146] Upper threshold = working hours quota + allowable error;

[0147] Lower limit threshold = working hours quota - allowable error;

[0148] Calculates the time variance, where:

[0149] Working hours difference = actual working hours - working hours quota;

[0150] When the actual working hours are not less than the lower threshold and not greater than the upper threshold, the process status is determined to be normal;

[0151] When the actual working hours are greater than the upper limit threshold, the process is judged to have exceeded the working hours, and an alarm for overspending will be issued;

[0152] When the actual working hours are less than the lower limit threshold, it is determined that the process working hours are insufficient, and an alarm prompts for insufficient working hours.

[0153] It is understandable that this embodiment describes in detail a method for determining abnormal working conditions based on working time difference data. The method compares and analyzes the actual working hours of each process with the working time quota, effectively identifies and handles possible abnormal conditions, and thus ensures the stability and efficiency of the production process.

[0154] First, determine the time difference threshold. This step is crucial because it provides a benchmark for subsequent status determination. Specifically, the time difference threshold consists of an upper threshold and a lower threshold. The upper threshold is set based on the sum of the standard time and the allowable error. This means that when the actual time exceeds this threshold, it may indicate that the process is at risk of overspending. Similarly, the lower threshold is the difference between the standard time and the allowable error. If the actual time is lower than this threshold, it may mean that the process has a problem of insufficient time.

[0155] Next, calculate the difference between the actual working hours and the working hours quota. This difference value directly reflects the gap between the actual operation status of the process and the expected target. Through calculation, the working hours difference data of each process can be obtained, which provides a basis for the subsequent abnormal state judgment.

[0156] After obtaining the working time difference data, we can start to judge the abnormal state of the working status of each process. Specifically, when the actual working time is between the lower threshold and the upper threshold, the process status is judged to be normal and no additional intervention is required. However, when the actual working time exceeds this range, corresponding measures need to be taken.

[0157] If the actual working hours are greater than the upper threshold, the process is judged to be in an abnormal state of working hours overrun. In this case, the system will trigger a working hours overrun alarm prompt so that relevant personnel can pay attention to and deal with this problem in time. By analyzing the reasons for overruns, the key factors affecting working hours can be found, and then targeted measures can be taken to optimize the production process and reduce working hours costs.

[0158] On the other hand, if the actual working hours are less than the lower limit, the process is judged to be in an abnormal state of insufficient working hours. At this time, the system will issue an alarm prompt for insufficient working hours to remind relevant personnel to pay attention to and improve this situation. To address the problem of insufficient working hours, we can start from improving production efficiency, optimizing work processes, etc. to ensure that the process can complete the expected goals within the specified time.

[0159] In summary, through in-depth analysis of the working time difference data, the abnormal status of each process can be effectively identified and handled. This method not only improves the stability and efficiency of the production process, but also helps to reduce production costs.

[0160] In some embodiments of the present application, when adjusting the working time quota according to the historical data and the working time difference data, it includes:

[0161] Collect the actual working time data and working time quota data of each process within a certain time range, and record the working time difference of each process;

[0162] Calculating statistical values ​​of working time differences, and evaluating the accuracy of working time quotas based on the statistical values ​​of working time differences;

[0163] When the working time quota is inaccurate, adjust the working time quota based on the statistical value of the working time difference.

[0164] It is understandable that this embodiment provides a more detailed explanation and expansion of the process of how to adjust the working hour quota based on the historical data and working hour difference data.

[0165] First, in terms of data collection, the time range for collection is clarified, and the importance of each process is emphasized. Specifically, the actual working hours data and working hours quota data of each process in the past period of time (such as one month, one quarter or one year) are regularly collected. These data can help users gain an in-depth understanding of the working hours of each process. At the same time, the working hours difference of each process, that is, the difference between the actual working hours and the working hours quota, is recorded in detail for subsequent analysis and adjustment.

[0166] Next, in the data analysis phase, calculate the statistical values ​​of the working time difference, such as the mean and standard deviation. These statistical values ​​will help users evaluate the accuracy of the working time quota. By comparing the difference between the actual working time data and the working time quota data, it can be determined whether the working time quota is too loose or too strict. If the working time quota is inaccurate, then it needs to be adjusted accordingly.

[0167] When adjusting the working hours quota, comprehensive consideration should be given to the statistical value of working hours difference. If the working hours difference is generally large, it means that there may be a big problem with the working hours quota and a big adjustment is needed. Specifically, the working hours quota can be re-formulated based on historical data and working hours difference data to make it closer to the actual working hours situation. At the same time, attention should be paid to maintaining the fairness and rationality of the working hours quota to avoid new problems caused by excessive adjustments.

[0168] To sum up, by collecting actual working hour data and working hour quota data within a certain time range, calculating the statistical value of working hour differences, evaluating the accuracy of working hour quotas, and adjusting working hour quotas according to the statistical value of working hour differences, we can adjust working hour quotas more scientifically and reasonably, and improve production efficiency and employee satisfaction.

[0169] In some embodiments of the present application, when calculating the statistical value of the working time difference and evaluating the accuracy of the working time quota according to the statistical value of the working time difference, it includes:

[0170] Collect the actual working time data of each process in multiple production cycles, calculate the difference between each actual working time data and the current working time quota, and obtain the working time difference of each process in multiple production cycles; calculate the average working time difference of all processes, and calculate the standard deviation of the actual working time of all processes;

[0171] The mean value and the standard deviation are calculated according to the following formula;

[0172]

[0173] Among them, Ba is the average value of working time difference; Z is the number of data points; B is the working time difference; C is the standard deviation of working time difference;

[0174] The steps to assess the accuracy of time quotas based on the mean and standard deviation of time variances include:

[0175] When the average value of the working time difference is close to zero, the working time quota is determined to be accurate;

[0176] When the average value of the working time difference deviates significantly from zero, it is determined that there is a systematic deviation in the working time quota;

[0177] When the standard deviation of working time differences is small, it is judged that the dispersion of working time quota is low and working time is stable;

[0178] When the standard deviation of working hours is large, it is judged that the dispersion of working hours quota is high and working hours are unstable;

[0179] Among them, a mean value threshold of working time differences is pre-set, when the mean value of working time differences is less than the mean value threshold, it is determined that the mean value is close to zero; when the mean value of working time differences is not less than the mean value threshold, it is determined that the mean value deviates significantly from zero; at the same time, a standard deviation threshold of working time differences is pre-set, when the standard deviation of working time differences is less than the standard deviation threshold, it is determined that the standard deviation of working time differences is small; when the standard deviation of working time differences is not less than the standard deviation threshold, it is determined that the standard deviation of working time differences is large;

[0180] When there is a systematic deviation in the working time quota and the working hours are unstable, the accuracy of the working time quota is determined to be inaccurate; at this time, the working time quota is adjusted according to the average value and standard deviation of the working time difference.

[0181] It is understandable that the evaluation of the accuracy of the working time quota in this embodiment is a complex and critical process, which involves the collection and statistical analysis of actual working time data in multiple production cycles. This process not only helps to understand the rationality of the existing working time quota, but also provides a basis for optimizing the working time quota.

[0182] First, collect the actual working time data of each process in multiple production cycles. These data can reflect the actual working efficiency of different processes in different production environments. Then, calculate the difference between each actual working time data and the current working time quota to obtain the working time difference of each process in multiple production cycles. These difference values ​​can help us understand the deviation between the current working time quota and the actual working time.

[0183] After obtaining the time differences of all processes, calculate the average and standard deviation of these differences. The average can reflect the overall trend of the time differences, while the standard deviation can reveal the degree of dispersion of the time differences. Through these two statistics, the accuracy of the time quota can be preliminarily evaluated.

[0184] Next, the accuracy of the time quota is evaluated based on the mean and standard deviation of the time variance. Specifically, when the mean of the time variance is close to zero, the time quota can be considered relatively accurate, because it means that the difference between the actual working hours and the standard working hours is small. On the contrary, when the mean of the time variance deviates significantly from zero, it can be determined that there is a systematic deviation in the time quota, which may be caused by errors or omissions in the quota setting process.

[0185] In addition, the standard deviation of the difference in working hours is also an important indicator for evaluating the accuracy of the working time quota. When the standard deviation is small, it means that the dispersion of the working time quota is low, that is, the difference in working hours in different processes or different production cycles is small, and the working hours are relatively stable. However, when the standard deviation is large, it means that the dispersion of the working time quota is high, that is, the difference in working hours in different processes or different production cycles is large, and the working hours are unstable.

[0186] In order to more accurately evaluate the accuracy of the working time quota, it is also necessary to set the mean value threshold and standard deviation threshold of the working time difference. When the mean value of the working time difference is less than the mean value threshold, the mean value is judged to be close to zero; when the mean value is not less than the threshold, the mean value is judged to be significantly deviated from zero. Similarly, when the standard deviation of the working time difference is less than the standard deviation threshold, the standard deviation of the working time difference is judged to be small; when the standard deviation is not less than the threshold, the standard deviation is judged to be large.

[0187] Finally, according to the evaluation results of the mean and standard deviation of the working time difference, necessary adjustments are made to the working time quota. When there is a systematic deviation in the working time quota and the working hours are unstable, the accuracy of the working time quota is judged to be inaccurate, and the working time quota is corrected and optimized accordingly.

[0188] In summary, through the collection, statistics and analysis of actual working hours data, combined with statistical indicators such as the mean and standard deviation of working hours differences, we can conduct a comprehensive and in-depth assessment of the accuracy of working hours quotas. This process not only helps us understand the rationality of existing working hours quotas, but also provides us with a basis for optimizing working hours quotas, which helps to improve production efficiency and quality.

[0189] In some embodiments of the present application, when there is a systematic deviation in the working hour quota and the working hours are unstable, the accuracy of the working hour quota is determined to be inaccurate; at this time, adjusting the working hour quota according to the average value and standard deviation of the working hour difference includes:

[0190] Calculate the adjusted working hours quota according to the following calculation formula:

[0191]

[0192] Among them, Tn is the adjusted working hours quota; To is the original working hours quota; is the average value of working time difference; σ d is the standard deviation; k and m are adjustment coefficients, k is used to adjust the average value of working time differences, and m is used to adjust the impact of standard deviation.

[0193] It is understandable that this embodiment has conducted in-depth discussions and considerations on the accuracy of the working time quota. When there is a systematic deviation in the working time quota and the working hours are unstable, the accuracy of the working time quota is clearly determined to be inaccurate. This judgment is not only based on experience, but also based on in-depth analysis and comparison of a large amount of actual data.

[0194] As an important parameter in production management, working time quota plays a vital role in improving production efficiency and optimizing resource allocation. However, when there is a deviation in working time quota, it may lead to confusion in production plan and even affect the overall operation of the enterprise. Therefore, it is particularly important to accurately determine the accuracy of working time quota and make adjustments accordingly.

[0195] After determining that the working hours quota is inaccurate, it is necessary to adjust the working hours quota based on the average and standard deviation of the working hours difference. This process is not a simple addition and subtraction operation, but requires a certain calculation formula and flexible adjustment based on actual conditions.

[0196] First, the adjustment coefficients k and m play a vital role in the adjustment process. Among them, k is used to adjust the average value of the working time difference, which reflects the overall level of the working time difference; while m is used to adjust the impact of the standard deviation, which reflects the stability of the working time difference.

[0197] Next, according to the calculation formula, the original working hours quota To, the average value of working hours difference and the standard deviation σ d Substitute it into the formula to calculate the adjusted working hours quota Tn. In this process, the adjustment coefficients k and m will play a key role. They will make appropriate adjustments to the original working hours quota based on the average and standard deviation of the working hours difference.

[0198] It is worth noting that the adjusted working hours quota is not static. In the actual production process, with the change of production conditions, the improvement of workers' skills and the improvement of equipment performance, the working hours quota will also change accordingly. Therefore, it is necessary to regularly adjust and optimize the working hours quota to ensure that it always maintains its accuracy and applicability.

[0199] In addition, in order to ensure the accuracy and effectiveness of the adjustment of working hours quota, it is also necessary to verify and evaluate the adjusted working hours quota in combination with the actual situation. This can be achieved by comparing indicators such as production efficiency and resource utilization before and after the adjustment. Through comparative analysis, it is possible to more accurately understand whether the adjusted working hours quota has achieved the expected effect, and further optimize and improve it accordingly.

[0200] like Figure 2 As shown, this embodiment provides a production management system based on barcode recognition, which is used to implement the above-mentioned production management method based on barcode recognition. The production management system includes:

[0201] Barcode scanning module, used to scan barcode information during the production process;

[0202] A data storage module, used to store scanned barcode information and production data;

[0203] The data processing module is used to calculate the production stability coefficient, process production efficiency, production quality qualification rate, production interruption times and equipment failure rate based on the stored production data, and to determine the abnormal state of the working state of each process based on the working time difference data, and to adjust the working time quota based on the historical data and the working time difference data;

[0204] The alarm prompt module is used to issue an alarm prompt when the process working hours are overspent or insufficient;

[0205] User interaction module, used to display the calculation results of production indicators and abnormal status determination results

[0206] This embodiment aims to provide an efficient and comprehensive production management system based on barcode recognition to achieve accurate monitoring and efficient management of the production process. The system integrates multiple modules such as barcode scanning, data storage, data processing, alarm prompts and user interaction to achieve real-time monitoring and evaluation of key indicators in the production process, thereby helping enterprises improve production efficiency, optimize resource allocation, and enhance overall competitiveness.

[0207] The barcode scanning module is one of the core components of this system. It is responsible for real-time scanning of the barcode information on the product during the production process. These barcode information usually contains important data such as the batch, production time, and production line of the product, providing the original basis for subsequent data processing. The barcode scanning module is efficient and accurate, and can quickly identify and capture barcode information to ensure the integrity and accuracy of the data.

[0208] The data storage module is responsible for storing the scanned barcode information and production data. This module uses an efficient database management system to achieve rapid storage and retrieval of large amounts of data. At the same time, it can also classify and store data according to its type and characteristics, facilitating subsequent data analysis and processing.

[0209] The data processing module is a key part of this system. It is responsible for in-depth analysis and processing of the stored production data. Based on these data, the module can calculate key indicators such as production stability coefficient, process production efficiency, production quality qualification rate, production interruption times and equipment failure rate. These indicators can fully reflect the stability and efficiency of the production process and provide important decision-making basis for enterprises.

[0210] In addition, the data processing module can also determine the abnormal status of the working status of each process based on the working time difference data. By analyzing and comparing the working time data, the module can promptly detect abnormal situations in the production process, such as working time overruns, working time shortages, etc., thereby providing timely alarm prompts for the company.

[0211] When the alarm prompt module finds an abnormal situation, it will immediately sound an alarm to remind relevant personnel to pay attention and deal with it. This timely alarm prompt can help enterprises quickly respond to problems in the production process, reduce losses and improve production efficiency.

[0212] Finally, the user interaction module provides users with an intuitive and friendly interface for displaying the calculation results of production indicators and the results of abnormal state determination. Through this module, users can easily view various data of the production process, understand the production status, and make corresponding adjustments and optimizations as needed.

[0213] In summary, the barcode recognition-based production management system provided in the embodiment realizes comprehensive monitoring and management of the production process by integrating multiple functional modules. It can help enterprises improve production efficiency, optimize resource allocation, and enhance overall competitiveness, providing strong support for the sustainable development of enterprises.

[0214] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0215] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0216] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0217] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0218] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A production management method based on barcode recognition, characterized in that: include: Mark each process on the production line with a barcode, and scan the barcode to record the start and end time of each process; Calculate the actual working hours for each process; Compare the working hours quota with the actual working hours to obtain working hours difference data; According to the working time difference data, the abnormal state of each process is judged. When the judgment result is abnormal, the abnormal state is recorded and an alarm is issued; At the same time, the working hour quota is adjusted based on historical data and working hour difference data.

2. The production management method based on barcode recognition according to claim 1, characterized in that: When barcodes are used to identify each process on the production line, and the barcodes are scanned to record the start and end time of each process, it includes: Uniquely encode each process on the production line to form a process barcode; At the beginning and end of a process, the process barcode is associated with the process operation information by scanning the process barcode; Process operation information includes process name, operator, equipment status, material information and production quantity; At the beginning of the process, by scanning the process barcode, the process barcode is associated with the process name, operator, equipment status and material information; at the same time, the timing function is turned on to record the start time and end time of the process.

3. The production management method based on barcode recognition according to claim 2 is characterized in that: Calculate the actual working hours for each process, including: Calculate the difference between the start time and end time of each process to get the actual time of each process; Adjust the actual time according to the equipment's operating efficiency and the stability of the production environment to obtain the actual working hours; Among them, the operating efficiency of the equipment is evaluated based on historical operating data, and the stability of the production environment is evaluated based on historical production data; According to the operating efficiency of the equipment and the stability of the production environment, the actual time is adjusted to obtain the calculation formula for the actual working hours: Ta is the actual working hours; Tm is the actual time; E is the equipment operation efficiency coefficient, ranging from 0 to 1; S is the stability coefficient of the production environment, ranging from 0 to 1.

4. The production management method based on barcode recognition according to claim 3 is characterized in that: Evaluate equipment operating efficiency based on historical operating data, including: Collect historical operation data on the production line, including equipment operation time, number of failures and maintenance records; Conduct statistical analysis on historical operation data to calculate the average operation time, mean time between failures and mean repair time of equipment; According to the statistical analysis results, the operating efficiency of the equipment is evaluated and the equipment operating efficiency coefficient E is obtained. The calculation formula is: Among them, Mf is the mean time between failures; Mr is the mean maintenance time; Tt is the total operating time; Tf is the total maintenance time; n is the number of failures; and E is the operating efficiency coefficient.

5. The production management method based on barcode recognition according to claim 3 is characterized in that: Assess the stability of the production environment based on historical production data, including: Collect historical production data on the production line, including production efficiency, production quality, number of production interruptions and equipment failure rate of each process; Conduct statistical analysis on historical production data to calculate the average value of process production efficiency, the qualified rate of process production quality, the average number of production interruptions and the average value of equipment failure rate; According to the statistical analysis results, the stability of the production environment is evaluated and the production environment stability coefficient S is obtained. The calculation formula is: Among them, S is the production environment stability coefficient; A is the average value of the process production efficiency; Q is the qualified rate of the process production quality; D is the average number of production interruptions; F is the average value of the equipment failure rate; a is the total production efficiency of the process; Na is the number of processes; q is the total number of qualified products; Nq is the number of products produced; d is the total number of production interruptions; Nd is the statistical period; f is the total number of equipment failures; Nf is the total equipment operating time.

6. The production management method based on barcode recognition according to claim 3 is characterized in that: According to the working time difference data, the abnormal status of each process is determined, including: Determine the time variance threshold, where: Upper threshold = working hours quota + allowable error; Lower limit threshold = working hours quota - allowable error; Calculates the time variance, where: Working hours difference = actual working hours - working hours quota; When the actual working hours are not less than the lower threshold and not greater than the upper threshold, the process status is determined to be normal; When the actual working hours are greater than the upper limit threshold, the process is judged to have exceeded the working hours, and an alarm for overspending will be issued; When the actual working hours are less than the lower limit threshold, it is determined that the process working hours are insufficient, and an alarm prompts for insufficient working hours.

7. The production management method based on barcode recognition according to claim 6, characterized in that: When adjusting the working hours quota based on historical data and working hours difference data, it includes: Collect the actual working time data and working time quota data of each process within a certain time range, and record the working time difference of each process; Calculating statistical values ​​of working time differences, and evaluating the accuracy of working time quotas based on the statistical values ​​of working time differences; When the working time quota is inaccurate, adjust the working time quota based on the statistical value of the working time difference.

8. The production management method based on barcode recognition according to claim 7, characterized in that: When calculating the statistical value of working time variance and evaluating the accuracy of working time quota based on the statistical value of working time variance, the following shall be included: Collect the actual working time data of each process in multiple production cycles, calculate the difference between each actual working time data and the current working time quota, and obtain the working time difference of each process in multiple production cycles; calculate the average working time difference of all processes, and calculate the standard deviation of the actual working time of all processes; The mean value and the standard deviation are calculated according to the following formula; Among them, Ba is the average value of working time difference; Z is the number of data points; B is the working time difference; C is the standard deviation of working time difference; The steps to assess the accuracy of time quotas based on the mean and standard deviation of time variances include: When the average value of the working time difference is close to zero, the working time quota is determined to be accurate; When the average value of the working time difference deviates significantly from zero, it is determined that there is a systematic deviation in the working time quota; When the standard deviation of working time differences is small, it is judged that the dispersion of working time quota is low and working time is stable; When the standard deviation of working hours is large, it is judged that the dispersion of working hours quota is high and working hours are unstable; Among them, a mean value threshold of working time differences is pre-set, when the mean value of working time differences is less than the mean value threshold, it is determined that the mean value is close to zero; when the mean value of working time differences is not less than the mean value threshold, it is determined that the mean value deviates significantly from zero; at the same time, a standard deviation threshold of working time differences is pre-set, when the standard deviation of working time differences is less than the standard deviation threshold, it is determined that the standard deviation of working time differences is small; when the standard deviation of working time differences is not less than the standard deviation threshold, it is determined that the standard deviation of working time differences is large; When there is a systematic deviation in the working time quota and the working hours are unstable, the accuracy of the working time quota is determined to be inaccurate; at this time, the working time quota is adjusted according to the average value and standard deviation of the working time difference.

9. The production management method based on barcode recognition according to claim 8, characterized in that: When there is a systematic deviation in the working hours quota and the working hours are unstable, the accuracy of the working hours quota is judged to be inaccurate; At this time, according to the average and standard deviation of the working time difference, the working time quota is adjusted, including: Calculate the adjusted working hours quota according to the following calculation formula: Among them, Tn is the adjusted working hours quota; To is the original working hours quota; is the average value of working time difference; σ d is the standard deviation; k and m are adjustment coefficients, k is used to adjust the average value of working time differences, and m is used to adjust the impact of standard deviation.

10. A production management system based on barcode recognition, used to implement the production management method based on barcode recognition according to any one of claims 1 to 9, characterized in that: The production management system comprises: Barcode scanning module, used to scan barcode information during the production process; A data storage module, used to store scanned barcode information and production data; The data processing module is used to calculate the production stability coefficient, process production efficiency, production quality qualification rate, production interruption times and equipment failure rate based on the stored production data, and to determine the abnormal state of the working state of each process based on the working time difference data, and to adjust the working time quota based on the historical data and the working time difference data; The alarm prompt module is used to issue an alarm prompt when the process working hours are overspent or insufficient; The user interaction module is used to display the calculation results of production indicators and the abnormal status determination results.

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