Manufacturing process optimization method based on MES system

Abstract

The invention relates to the technical field of manufacturing process optimization, and discloses a manufacturing process optimization method based on an MES (Manufacturing Execution System), which comprises the steps of data acquisition, data analysis and resource allocation, and is characterized in that reasonable resource allocation is realized by accurately calculating an equipment utilization rate and a human demand index and combining comprehensive data acquisition and accurate index analysis; equipment scheduling optimization performs task allocation according to the utilization rate, and equipment idling or overfatigue is avoided; personnel allocation optimization ensures that the number of employees is matched with production tasks; the operation state of equipment is determined and the working condition of personnel is mastered, so that problems can be timely processed and personnel management can be performed; the method has flexible optimization measures and can cope with different production conditions, preset parameters can be adjusted, and adaptability is high; in a word, the method realizes comprehensive optimization of the production process, improves the production efficiency, reasonably distributes resources, and strengthens the production management.

Description

Technical Field

[0001] The present invention relates to the technical field of manufacturing process optimization, and in particular to a manufacturing process optimization method based on a MES system. Background Art

[0002] With the continuous development of the manufacturing industry, improving production efficiency and optimizing manufacturing processes have become important goals pursued by enterprises. In modern manufacturing, the Manufacturing Execution System (MES) plays a vital role. However, there are still some problems in the current manufacturing process, which need to be further optimized to enhance the competitiveness of enterprises.

[0003] On the one hand, in the production process, enterprises often find it difficult to accurately grasp key information such as equipment operation status, production progress, and personnel work conditions. The lack of effective collection and analysis of these data makes it impossible for enterprises to reasonably allocate production resources, resulting in problems such as low equipment utilization and unreasonable manpower allocation. For example, if the actual operating time, failure time, and optimal operating times of the equipment are not clear, the equipment utilization cannot be accurately calculated, and it is difficult to effectively schedule and optimize the equipment. At the same time, the lack of understanding of the work conditions of personnel also makes it difficult for enterprises to determine reasonable manpower demand indicators and achieve optimal allocation of personnel. On the other hand, traditional manufacturing process optimization methods often rely on experience and subjective judgment, and lack scientific data analysis and quantitative indicators. This makes the optimization results inaccurate and unreliable, and difficult to adapt to complex and changing production environments. For example, in terms of equipment scheduling and personnel allocation, there are no clear indicators and methods, which are prone to problems such as resource waste or production bottlenecks.

[0004] In summary, in order to solve the problems existing in the existing manufacturing process and improve production efficiency and resource utilization, a manufacturing process optimization method based on the MES system is needed. Summary of the invention

[0005] The purpose of the present invention is to provide a manufacturing process optimization method based on an MES system, which solves the technical problems raised in the background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The manufacturing process optimization method based on the MES system includes the following steps:

[0008] Step 1: Data Collection

[0009] During the production process, collect relevant data corresponding to equipment operation status, production progress, and personnel work status;

[0010] Step 2: Data Analysis

[0011] Combine the MES system to analyze the collected relevant data, and obtain equipment utilization and manpower demand indicators based on the analysis results;

[0012] Step 3: Resource allocation

[0013] Based on the equipment utilization and manpower demand indicators obtained from the indicator analysis, production resources are optimally allocated. The optimal allocation includes equipment scheduling optimization and personnel allocation optimization.

[0014] As a further embodiment of the present invention:

[0015] The equipment operation status includes:

[0016] Equipment running time: record the running time, downtime and startup time of each operation of manufacturing equipment;

[0017] Equipment failure times: the number of failures of manufacturing equipment within a specified period of time, and the corresponding failure duration of each failure;

[0018] The fault duration refers to the time difference from when the manufacturing equipment fails to the time when the manufacturing equipment is repaired;

[0019] The production schedule includes: production task volume, which indicates the number of production tasks that need to be completed;

[0020] The work status of personnel includes:

[0021] Employee working time period: refers to the time difference between each employee's working time node and off-get off work time node;

[0022] Employee's historical completion amount: record the employee's task completion amount in each previous working period.

[0023] As a further solution of the present invention: the index analysis method is as follows:

[0024] Step 1. Equipment utilization analysis

[0025] Step 1.1. Calculate the total running time

[0026] In a specified period, the operation time of each operation of the manufacturing equipment is extracted, and then the sum of the operation time of multiple operations of the manufacturing equipment in the specified period is calculated, and the value is recorded as the total operation time;

[0027] Step 1.2. Calculate the total duration of the fault

[0028] Extract the failure time of each failure of manufacturing equipment within a specified period,

[0029] Then the sum of the failure times of multiple failures of the manufacturing equipment within a specified period is calculated and recorded as the total failure time;

[0030] Step 1.3. Extract single run parameters

[0031] Extract the maximum running time and minimum downtime pre-set for a single operation of the manufacturing equipment;

[0032] Step 1.4. Calculate the optimal number of runs

[0033] Get the duration of the specified period;

[0034] Then through:

[0035] Optimal number of operations = cycle time / (maximum operating time + minimum downtime), calculate the optimal number of operations of the manufacturing equipment within a specified cycle;

[0036] Step 1.5. Calculate the optimal theoretical running time

[0037] The optimal theoretical operating time of the manufacturing equipment within a specified period is calculated by: optimal theoretical operating time = maximum operating time × optimal operating times;

[0038] Step 1.6. Calculate equipment utilization

[0039] The equipment utilization rate of the manufacturing equipment in a specified period is calculated by: equipment utilization rate = total operation time / (optimal theoretical operation time - total failure time) × 100%;

[0040] Step 2. Human resource demand indicator analysis

[0041] Step 2.1. Employee information extraction

[0042] Select an employee, obtain all the working time periods of the employee in a specified historical period, and mark the number of working time periods in the historical period as n, and mark the working time period as G i , i=1, 2, ... n,

[0043] At the same time, obtain the employee's historical completion amount and mark it as U i , i=1, 2, ... n;

[0044] Step 2.2. Calculate employee productivity

[0045] pass Calculate the employee's productivity X;

[0046] Step 2.3. Calculate the per capita production efficiency

[0047] Calculate the productivity of all employees according to Step 2.1 and Step 2.2;

[0048] Then take the average of all employees’ productivity and label it as per capita productivity;

[0049] Step 2.4. Obtain production task volume

[0050] Get the production task volume within the specified period;

[0051] Step 2.5. Calculate the working time of the cycle

[0052] Extract the pre-set standard working time for the employee and the number of standard working hours in the specified period, then calculate the product of the corresponding duration of the standard working time and the number of standard working hours in the specified period, and record the value as the period working time;

[0053] Step 2.6. Calculate the manpower demand index

[0054] The human resource demand index within a specified period is calculated by: human resource demand index = production task volume / (per capita production efficiency × cycle working time).

[0055] As a further solution of the present invention: wherein, the preset maximum operating time can ensure that the equipment has sufficient rest and maintenance time after running for a period of time, and the preset minimum downtime can ensure that the equipment has sufficient cooling, inspection or maintenance time after stopping operation.

[0056] As a further solution of the present invention: the maximum operating time and the minimum downtime are set by the manufacturer of the manufacturing equipment and marked in the equipment manual.

[0057] As a further solution of the present invention: wherein, standard working hours refer to the standard working hours that employees should follow under normal working conditions.

[0058] As a further solution of the present invention: the optimized allocation method is as follows:

[0059] Step A: Equipment scheduling optimization

[0060] Step A.1. Obtain the equipment utilization rate of all manufacturing equipment involved in the task execution in a specified period, and mark the equipment utilization rate of each manufacturing equipment in the specified period as Kr, r = 1, 2, ... v, v represents the number of all manufacturing equipment involved in the task execution;

[0061] StepA.2, calculate the average value of all Kr and record it as the analysis utilization KF;

[0062] Next, the utilization threshold Ka preset by the analysis utilization KF is compared:

[0063] If KF≤Ka, it means that the workload of all manufacturing equipment involved in task execution is moderate;

[0064] Then, the equipment utilization rate Kr corresponding to each manufacturing equipment involved in the task execution is compared with the utilization threshold Ka, and the result is then displayed to the relevant management personnel. The relevant management personnel allocate the production tasks of the corresponding manufacturing equipment according to the comparison result;

[0065] Specifically, the unfinished tasks of the corresponding manufacturing equipment when Kr>Ka are assigned to the corresponding manufacturing equipment when Kr≤Ka, and the unfinished tasks are executed;

[0066] If KF>Ka, it means that the workload of all manufacturing equipment involved in the task execution exceeds the standard;

[0067] Then, the number of manufacturing equipment participating in the task execution is increased, and then the equipment utilization Kr corresponding to each manufacturing equipment participating in the task execution is compared with the utilization threshold Ka, and then the results are displayed to the relevant managers. The relevant managers allocate the production tasks of the corresponding manufacturing equipment according to the comparison results;

[0068] Specifically: assign the unfinished tasks of the corresponding manufacturing equipment when Kr>Ka to the corresponding manufacturing equipment when Kr≤Ka, and to the newly added manufacturing equipment participating in the task execution, and execute the unfinished tasks, and then display the results to relevant managers.

[0069] Step B: Optimize staff allocation

[0070] Step B.1. Obtain the number of all employees participating in the task execution within the specified period and record it as the personnel participation quantity RC;

[0071] Step B.2, mark the manpower demand indicator as RZ;

[0072] RZ is then compared with RC with a preset weighting factor β:

[0073] If RZ-β≤RC≤RZ+β, it means that the number of employees currently involved in task execution is moderate;

[0074] If RC<RZ-β, it means that the number of employees currently participating in the task execution is insufficient, and it is necessary to increase the number of employees participating in the task execution or arrange for the current employees participating in the task execution to work overtime, and then show the results to relevant managers;

[0075] If RC>RZ+β, it means that there are too many employees currently participating in the task execution. The number of employees participating in the task execution can be appropriately reduced, and they can be assigned to other tasks or trained, and then the results can be presented to relevant managers.

[0076] As a further solution of the present invention: when RC<RZ-β, Calculate the additional number of employees involved in the task execution, Z1, and then present the result to the relevant managers;

[0077] In the formula, The result value of RZ-β-RC is rounded up to obtain Z1.

[0078] As a further solution of the present invention: when RC>RZ+β, Calculate the reduction in the number of employees involved in the task execution, Z2, and then present the result to the relevant managers;

[0079] In the formula, The result value of RC-RZ-β is rounded down to obtain Z2.

[0080] Beneficial effects of the present invention:

[0081] Equipment utilization optimization: By accurately calculating equipment utilization, we can clearly understand the operating status of manufacturing equipment within a specified period. We optimize equipment scheduling based on equipment utilization. When the equipment workload is moderate or exceeds the standard, we rationally allocate production tasks and assign unfinished tasks to equipment with low utilization or new equipment, so that equipment resources can be used more effectively, avoiding equipment idleness or overwork, thereby improving overall production efficiency.

[0082] Clear indicators of manpower demand: Calculate manpower demand indicators, combine factors such as per capita production efficiency, production task volume and cycle working time, and accurately grasp the number of manpower required within a specified cycle. Through personnel allocation optimization, ensure that the number of employees matches the production tasks, avoid slow production progress due to insufficient personnel or waste of resources due to excessive personnel, improve the utilization efficiency of human resources, and thus improve production efficiency.

[0083] Reasonable allocation of equipment resources: Equipment scheduling optimization allocates tasks based on the comparison results of equipment utilization and utilization threshold. When the equipment workload is moderate, the unfinished tasks of high-utilization equipment are allocated to low-utilization equipment; when the workload exceeds the standard, the number of equipment is increased and tasks are allocated. This method allows equipment resources to be reasonably allocated in different production situations, improving the overall operating efficiency of the equipment.

[0084] Scientific allocation of human resources: Optimization of personnel allocation compares the human resource demand index with the number of employees involved in the task execution to determine whether the number of employees is appropriate, insufficient or excessive. Make corresponding adjustments according to different situations to ensure the scientific allocation of human resources. When the number of employees is insufficient, increase the number of employees; when the number of employees is too large, make adjustments, so that human resources are closely integrated with production tasks, improving production efficiency while reducing labor costs.

[0085] Comprehensive data collection: During the production process, relevant data such as equipment operation status, production progress, and personnel work status are collected to provide a rich information basis for subsequent analysis and decision-making. These data cover equipment operation time, number of failures, production task volume, employee work time period, and historical completion volume, and can fully reflect the actual situation in the production process.

[0086] Accurate indicator analysis: Combined with the MES system, the collected data is analyzed to obtain equipment utilization and manpower demand indicators. The calculation method of equipment utilization is scientific and rigorous, taking into account factors such as the operating time of manufacturing equipment, total fault time, maximum operating time, minimum downtime and optimal operating times, and can accurately reflect the actual use of the equipment. The calculation of manpower demand indicators combines factors such as employee production efficiency, production task volume and cycle working time, providing a reliable basis for the rational allocation of human resources.

[0087] Clarify the equipment operating status: Detailed records of the equipment operating status, including equipment operating time, downtime, startup time, and number of failures, etc., will help to promptly identify equipment problems, take appropriate repair and maintenance measures, reduce equipment failure rates, and improve equipment reliability and stability, thereby ensuring smooth production.

[0088] Understand the work status of employees: Recording employees' working time periods and historical completion amounts can help you understand their work efficiency and workload, and provide a basis for personnel management. At the same time, by calculating per capita production efficiency, you can evaluate the overall work performance of employees, provide a reference for performance appraisal and training, and improve employees' work enthusiasm and work quality.

[0089] Flexible optimization method: This manufacturing process optimization method can be adjusted according to different production situations. Whether it is a change in equipment workload or a fluctuation in manpower demand, it can be reasonably responded to through corresponding optimization measures. For example, when the equipment workload exceeds the standard, the number of equipment is increased, and when the number of employees is insufficient or excessive, personnel adjustments are made to ensure that the production process always maintains efficient operation.

[0090] Adjustable preset parameters: Some parameters in the method, such as utilization threshold, weighting factor, etc., can be adjusted according to actual production conditions. This makes the optimization method more adaptable and flexible, and can meet the needs of different enterprises and different production environments.

[0091] In summary, the manufacturing process optimization method based on the MES system achieves comprehensive optimization of the production process through data collection, indicator analysis, resource allocation, etc. This method improves production efficiency, rationally allocates resources, strengthens production management, adapts to different production situations, and provides strong support for the production and manufacturing of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The present invention will be further described below in conjunction with the accompanying drawings.

[0093] Figure 1 It is a flow chart of the manufacturing process optimization method based on the MES system of the present invention.

[0094] Figure 2 It is a flow chart of data analysis in the manufacturing process optimization method based on the MES system of the present invention. DETAILED DESCRIPTION

[0095] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0096] Embodiment 1

[0097] See also Figure 1 and Figure 2 As shown, the present invention is a manufacturing process optimization method based on the MES system, comprising the following steps:

[0098] Step 1: Data Collection

[0099] During the production process, collect relevant data corresponding to the equipment operation status and production progress;

[0100] in,

[0101] The equipment operation status includes:

[0102] Equipment running time: record the running time, downtime and startup time of each operation of manufacturing equipment;

[0103] Equipment failure times: the number of failures of manufacturing equipment within a specified period of time, and the corresponding failure duration of each failure;

[0104] The fault duration refers to the time difference from when the manufacturing equipment fails to the time when the manufacturing equipment is repaired;

[0105] The production schedule includes: production task volume, which indicates the number of production tasks that need to be completed;

[0106] Step 2: Data Analysis

[0107] Combine the MES system to analyze the collected relevant data, and obtain the equipment utilization rate based on the analysis results;

[0108] The indicator analysis method is as follows:

[0109] Step 1.1. Extract the running time of each operation of the manufacturing equipment within the specified period, and then calculate the sum of the running time of multiple operations of the manufacturing equipment within the specified period, and record the value as the total running time;

[0110] Step 1.2: Extract the failure time of each failure of the manufacturing equipment within the specified period.

[0111] Then the sum of the failure times of multiple failures of the manufacturing equipment within a specified period is calculated and recorded as the total failure time;

[0112] Step 1.3, extract the maximum running time and minimum downtime pre-set for a single operation of the manufacturing equipment;

[0113] Among them, the preset maximum operating time can ensure that the equipment has enough rest and maintenance time after running for a period of time, thereby reducing the risk of equipment failure; the preset minimum downtime can ensure that the equipment has enough cooling, inspection or maintenance time after stopping operation, further improving the reliability and stability of the equipment; these measures help remind users to use manufacturing equipment reasonably, thereby reducing the failure rate of manufacturing equipment, extending the service life of the equipment, and improving production efficiency. The maximum operating time and minimum downtime are set by the manufacturer of the manufacturing equipment and marked in the equipment instruction manual;

[0114] Step 1.4, get the cycle duration of the specified cycle;

[0115] Then through:

[0116] Optimal number of operations = cycle time / (maximum operating time + minimum downtime), calculate the optimal number of operations of the manufacturing equipment within a specified cycle;

[0117] Step 1.5, calculate the optimal theoretical operating time of the manufacturing equipment within a specified period by: optimal theoretical operating time = maximum operating time × optimal operating times;

[0118] Step 1.6, calculate the equipment utilization rate of the manufacturing equipment within the specified period by: equipment utilization rate = total operation time / (optimal theoretical operation time - total failure time) × 100%;

[0119] Step 3: Resource allocation

[0120] Optimize equipment scheduling of production resources based on equipment utilization and manpower demand indicators obtained through indicator analysis;

[0121] The equipment scheduling optimization method is as follows:

[0122] Step A.1. Obtain the equipment utilization rate of all manufacturing equipment involved in the task execution in a specified period, and mark the equipment utilization rate of each manufacturing equipment in the specified period as Kr, r = 1, 2, ... v, v represents the number of all manufacturing equipment involved in the task execution;

[0123] StepA.2, calculate the average value of all Kr and record it as the analysis utilization KF;

[0124] Next, the utilization threshold Ka preset by the analysis utilization KF is compared:

[0125] If KF≤Ka, it means that the workload of all manufacturing equipment involved in task execution is moderate;

[0126] Then, the equipment utilization Kr corresponding to each manufacturing equipment participating in the task execution is compared with the utilization threshold Ka, and the production tasks of the corresponding manufacturing equipment are allocated according to the comparison result;

[0127] Specifically, the unfinished tasks of the corresponding manufacturing equipment when Kr>Ka are assigned to the corresponding manufacturing equipment when Kr≤Ka, and the unfinished tasks are executed;

[0128] If KF>Ka, it means that the workload of all manufacturing equipment involved in the task execution exceeds the standard;

[0129] The number of manufacturing equipment participating in the task execution is then increased, and then the equipment utilization Kr corresponding to each manufacturing equipment participating in the task execution is compared with the utilization threshold Ka, and the production tasks of the corresponding manufacturing equipment are allocated according to the comparison results;

[0130] Specifically: the unfinished tasks of the corresponding manufacturing equipment when Kr>Ka are allocated to the corresponding manufacturing equipment when Kr≤Ka, and to the newly added manufacturing equipment participating in the task execution, and the unfinished tasks are executed.

[0131] This embodiment can fully understand the actual situation of manufacturing equipment in the production process by collecting data related to equipment operating status and production progress, and provide an accurate data basis for subsequent analysis and optimization; it adopts a specific indicator analysis method to calculate equipment utilization, which can quantify the efficiency of equipment use and provide a scientific basis for the rational allocation of production resources; it optimizes equipment scheduling according to equipment utilization, which can improve the overall utilization efficiency of equipment, reduce the occurrence rate of equipment failures, and extend the service life of equipment while ensuring the smooth progress of production tasks, thereby improving production efficiency.

[0132] Embodiment 2

[0133] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from that of the first embodiment only in that:

[0134] The data collection step is still in the production process, collecting relevant data corresponding to the work situation of the personnel;

[0135] in,

[0136] The work status of personnel includes:

[0137] Employee working time period: refers to the time difference between each employee's working time node and off-get off work time node;

[0138] Employee's historical completion amount: record the employee's task completion amount in each working period in the previous period;

[0139] The data analysis step also combines the MES system to analyze the indicators of the collected relevant data, and obtains the manpower demand indicators based on the analysis results;

[0140] The indicator analysis method is as follows:

[0141] Step 2.1. Select an employee, obtain all the working time periods of the employee in a specified historical period, and mark the number of working time periods in the historical period as n, and mark the working time periods as G. i , i=1, 2, ... n,

[0142] At the same time, obtain the employee's historical completion amount and mark it as U i , i=1, 2, ... n;

[0143] Step 2.2, through Calculate the employee's productivity X;

[0144] Step 2.3, calculate the production efficiency of all employees according to Step 2.1 and Step 2.2;

[0145] Then, the productivity of all employees is labeled as Xj, where j = 1, 2, ... m, and m represents the number of employees;

[0146] Then through Calculate the average productivity XP among all employees;

[0147] Step 2.4, obtain the production task volume within the specified period;

[0148] Step 2.5, extract the pre-set standard working hours for employees, and extract the number of standard working hours in the specified period, then calculate the product of the corresponding length of the standard working hours and the number of standard working hours in the specified period, and record the value as the period working time;

[0149] Among them, standard working hours refer to the standard working hours that employees should follow under normal working conditions;

[0150] Step 2.6, calculate the manpower demand index within the specified period by: manpower demand index = production task volume / (per capita production efficiency × cycle working time);

[0151] The resource allocation step also optimizes the personnel allocation of production resources based on the human resource demand indicators obtained from the indicator analysis;

[0152] The optimization method of personnel allocation is as follows:

[0153] Step B.1. Obtain the number of all employees participating in the task execution within the specified period and record it as the personnel participation quantity RC;

[0154] Step B.2, mark the manpower demand indicator as RZ,

[0155] RZ is then compared with RC with a preset weighting factor β:

[0156] If RZ-β≤RC≤RZ+β, it means that the number of employees currently involved in task execution is moderate;

[0157] If RC<RZ-β, it means that the number of employees currently participating in the task execution is insufficient, and it is necessary to increase the number of employees participating in the task execution or arrange for the current employees participating in the task execution to work overtime;

[0158] At the same time, through Calculate the additional number of employees involved in task execution Z1;

[0159] In the formula, The result value of RZ-β-RC is rounded up to get Z1;

[0160] If RC>RZ+β, it means that the number of employees currently involved in the task execution is too large, and the number of employees involved in the task execution can be appropriately reduced and they can be assigned to other tasks or trained;

[0161] At the same time, through Calculate the reduction in the number of employees involved in task execution, Z2;

[0162] In the formula, The result value of RC-RZ-β is rounded down to obtain Z2.

[0163] This embodiment adds the collection of data related to personnel work conditions in the data collection step, so that the data in the production process is more comprehensive, which is conducive to more comprehensive allocation of production resources; by analyzing the work conditions of personnel to obtain human resource demand indicators, human resources can be reasonably planned to avoid waste or shortage of human resources; optimizing personnel allocation based on human resource demand indicators can improve employee work efficiency and ensure that production tasks are completed on time, while also providing employees with more reasonable work arrangements.

[0164] Embodiment 3

[0165] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0166] This embodiment combines the solutions of embodiment one and embodiment two for implementation, which can optimize the management of equipment and personnel at the same time and realize comprehensive and reasonable allocation of production resources; comprehensively consider equipment utilization and manpower demand indicators, better coordinate the cooperation between equipment and personnel, and improve the efficiency and stability of the entire production system; provide enterprises with a more complete manufacturing process optimization method, which helps enterprises reduce production costs and improve production quality and competitiveness.

[0167] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0168] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The manufacturing process optimization method based on the MES system is characterized by: The following steps are involved: Step 1: Data Collection During the production process, collect relevant data corresponding to equipment operation status, production progress, and personnel work status; Step 2: Data Analysis Combine the MES system to analyze the collected relevant data, and obtain equipment utilization and manpower demand indicators based on the analysis results; Step 3: Resource allocation Based on the equipment utilization and manpower demand indicators obtained from the indicator analysis, production resources are optimally allocated. The optimal allocation includes equipment scheduling optimization and personnel allocation optimization.

2. The manufacturing process optimization method based on the MES system according to claim 1 is characterized in that: The equipment utilization rate in the indicator analysis is derived as follows: Step 1.

1. Extract the running time of each operation of the manufacturing equipment within the specified period, and then calculate the sum of the running time of multiple operations of the manufacturing equipment within the specified period, and record the value as the total running time; Step 1.2: Extract the failure time of each failure of the manufacturing equipment within the specified period. Then the sum of the failure times of multiple failures of the manufacturing equipment within a specified period is calculated, and its value is recorded as the total failure time; Step 1.3, extract the maximum running time and minimum downtime pre-set for a single operation of the manufacturing equipment; Step 1.4, get the cycle duration of the specified cycle; Then through: Optimal number of operations = cycle time / (maximum operating time + minimum downtime), calculate the optimal number of operations of the manufacturing equipment within a specified cycle; Step 1.5, calculate the optimal theoretical operating time of the manufacturing equipment within a specified period by: optimal theoretical operating time = maximum operating time × optimal operating times; Step 1.6, calculate the equipment utilization rate of the manufacturing equipment within a specified period through: equipment utilization rate = total operating time / (optimal theoretical operating time-total fault time)×100%.

3. The manufacturing process optimization method based on the MES system according to claim 1 is characterized in that: The method of obtaining the human resource demand index in the index analysis is as follows: Step 2.

1. Select an employee, obtain all the working time periods of the employee in a specified historical period, and mark the number of working time periods in the historical period as n, and mark the working time periods as G. i , i=1, 2, ... n, At the same time, obtain the employee's historical completion amount and mark it as U i , i=1, 2, ... n; Step 2.2, through Calculate the employee's productivity X; Step 2.3, calculate the production efficiency of all employees according to Step 2.1 and Step 2.2; Then take the average of all employees’ productivity and label it as per capita productivity; Step 2.4, obtain the production task volume within the specified period; Step 2.5, extract the pre-set standard working hours for employees, and extract the number of standard working hours in the specified period, then calculate the product of the corresponding length of the standard working hours and the number of standard working hours in the specified period, and record the value as the period working time; Step 2.6, calculate the manpower demand index within the specified period through: manpower demand index = production task volume / (per capita production efficiency × cycle working time).

4. The manufacturing process optimization method based on the MES system according to claim 2 is characterized in that: in, The maximum operating time and minimum downtime are set by the manufacturer of the equipment and marked in the equipment instruction manual.

5. The manufacturing process optimization method based on the MES system according to claim 3 is characterized in that: in, Standard working hours refer to the standard working hours that employees should follow under normal working conditions.

6. The manufacturing process optimization method based on the MES system according to claim 1 is characterized in that: in, The equipment operation status includes: Equipment running time: record the running time, downtime and startup time of each operation of manufacturing equipment; Equipment failure times: the number of failures of manufacturing equipment within a specified period of time, and the corresponding failure duration of each failure; The fault duration refers to the time difference from when the manufacturing equipment fails to the time when the manufacturing equipment is repaired; The production schedule includes: production task volume, which indicates the number of production tasks that need to be completed; The work status of personnel includes: Employee working time period: refers to the time difference between each employee's working time node and off-get off work time node; Employee's historical completion amount: record the employee's task completion amount in each previous working period.

7. The manufacturing process optimization method based on the MES system according to claim 2 is characterized in that: The equipment scheduling optimization method in the optimal allocation is as follows: Step A.

1. Obtain the equipment utilization rate of all manufacturing equipment involved in the task execution in a specified period, and mark the equipment utilization rate of each manufacturing equipment in the specified period as Kr, r = 1, 2, ... v, v represents the number of all manufacturing equipment involved in the task execution; StepA.2, calculate the average value of all Kr and record it as the analysis utilization KF; Next, the utilization threshold Ka preset by the analysis utilization KF is compared: If KF≤Ka, it means that the workload of all manufacturing equipment involved in task execution is moderate; Then, the equipment utilization Kr corresponding to each manufacturing equipment participating in the task execution is compared with the utilization threshold Ka, and the production tasks of the corresponding manufacturing equipment are allocated according to the comparison result; If KF>Ka, it means that the workload of all manufacturing equipment involved in the task execution exceeds the standard; The number of manufacturing equipment participating in the task execution is increased accordingly, and then the equipment utilization rate Kr corresponding to each manufacturing equipment participating in the task execution is compared with the utilization rate threshold Ka. According to the comparison result, the production tasks of the corresponding manufacturing equipment are allocated.

8. The manufacturing process optimization method based on the MES system according to claim 7 is characterized in that: When KF≤Ka, the allocation method of production tasks is: assign the unfinished tasks of the corresponding manufacturing equipment when Kr>Ka to the corresponding manufacturing equipment when Kr≤Ka, and execute the unfinished tasks; When KF>Ka, the production tasks are allocated as follows: the unfinished tasks of the corresponding manufacturing equipment when Kr>Ka are allocated to the corresponding manufacturing equipment when Kr≤Ka, and to the newly added manufacturing equipment participating in the task execution, and the unfinished tasks are executed.

9. The manufacturing process optimization method based on the MES system according to claim 3 is characterized in that: The optimization methods for personnel allocation in optimal allocation are as follows: Step B.

1. Obtain the number of all employees participating in the task execution within the specified period and record it as the personnel participation quantity RC; Step B.2, mark the manpower demand indicator as RZ, RZ is then compared with RC with a preset weighting factor β: If RZ-β≤RC≤RZ+β, it means that the number of employees currently involved in task execution is moderate; If RC<RZ-β, it means that the number of employees currently participating in task execution is insufficient; If RC>RZ+β, it means that there are too many employees currently participating in task execution.

10. The manufacturing process optimization method based on the MES system according to claim 9 is characterized in that: When RC<RZ-β, Calculate the additional number of employees involved in task execution Z1; In the formula, The result value of RZ-β-RC is rounded up to get Z1; When RC>RZ+β, Calculate the reduction in the number of employees involved in task execution, Z2; In the formula, The result value of RC-RZ-β is rounded down to obtain Z2.

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