Industrial smart management method based on mes

Abstract

To provide an industrial smart management method based on an MES (ManufacturingExecutionSystem) for customizing a ManufacturingExecutionSystem suitable for a company.SOLUTION: The method includes obtaining historical production performance data of an enterprise and analyzing the historical production performance data to determine a production process, a production scale, and a product characteristic, analyzing the product characteristic to determine a key control node in the production process, obtaining an enterprise management requirement and determining an actual production requirement and a personnel management requirement based on the enterprise management requirement, the key control node, and the production scale, and developing a management module and integrating a manufacturing execution system based on these requirements. In this way, the manufacturing stage can be determined more accurately, thereby realizing efficient utilization of the manufacturing execution system, optimization of human resources, reduction of trial and error costs, efficient optimization of the production process, and improvement of production efficiency and product quality.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present application relates to the technical field of industrial management, and in particular to an industrial smart management method based on MES. [Background technology]

[0002] The Manufacturing Execution System (MES) is a very important information management system in the modern manufacturing industry. It can respond to and report on real-time events through information transmission, and by giving appropriate instructions and processing using current accurate data, it improves production efficiency and quality, reduces costs, and strengthens market competitiveness.

[0003] However, because manufacturing execution systems are large-scale, have many modules, and have complex logical relationships, it is often difficult for companies to fully understand their functions and operations when introducing them.Some companies have difficulty selecting an MES that is truly suitable for their company because they lack awareness of the system when selecting a model. Summary of the Invention [Problem to be solved by the invention]

[0004] This application provides an industrial smart management method based on MES to solve the above problems and customize a manufacturing execution system suitable for an enterprise. [Means for solving the problem]

[0005] In a first aspect, the present application provides an industrial smart management method based on MES, acquiring past production performance data of the enterprise, analyzing the past production performance data, and identifying the production process, production scale, and product characteristics; analyzing the product characteristics and identifying key control nodes in the production process; obtaining enterprise management needs, and determining actual production needs and personnel management needs according to the enterprise management needs, the key control node and the production scale; Developing a management module and integrating it into a manufacturing execution system based on the actual production needs and the personnel management needs; The above method, comprising:

[0006] Alternatively, the step of analyzing the past production performance data and identifying a production process, a production scale, and product characteristics comprises: analyzing the past production performance data, identifying data characteristics of each past production performance data, classifying the past production performance data based on the data characteristics, and obtaining a manufacturing stage based on the classification result; screening the manufacturing steps to obtain a production process; identifying production data based on the classification result, and identifying a production amount per unit time based on the production data; determining the production scale based on the production volume per unit time and a predetermined enterprise plan volume; Identifying test data based on the classification results; determining a product pass rate based on the inspection data; identifying product ingredients based on the production process and the production data; and identifying the product characteristics based on the product ingredients and the product acceptance rate.

[0007] Alternatively, the step of analyzing the product characteristics and identifying key control nodes in the production process comprises: A step of analyzing the classified past production performance data and identifying the unit production time and the number of defective products for each production process; determining a production defect rate per unit time based on the number of defective products and the product characteristics; acquiring a production task volume for each production process, and identifying the production difficulty of each production process based on the production task volume, the unit production time, and the production defect rate per unit time; and sorting the production difficulty of each production process and identifying a key control node based on the sorting result.

[0008] Alternatively, the step of specifying the production difficulty of each production process based on the production task amount, the unit production time, and the production defect rate per unit time refers to the following formula:

[0009] Number 1 JPEG2026020157000002.jpg22148 [In the formula, Di represents the production difficulty, Ti represents the production task volume of the i-th production process, ti represents the unit production time of the i-th production process, and Ri represents the production defect rate per unit time of the i-th production process.]

[0010] Alternatively, the step of obtaining the enterprise management needs includes: obtaining enterprise architecture information; analyzing the enterprise architecture information to identify employee types; Identifying job responsibilities for each employee type based on the employee types; transmitting a needs acquisition signal to a manager corresponding to each employee type based on the job responsibilities, and receiving management needs transmitted from the corresponding manager; and deriving enterprise management needs based on the job responsibilities and management needs submitted by the relevant managers.

[0011] Alternatively, the step of identifying actual production needs and personnel management needs based on the enterprise management needs, the key control nodes, and the production scale includes: Identifying a unit production amount based on the production scale; determining material consumption and equipment energy consumption per unit time based on the unit production volume and the production process; determining the actual production needs based on the material consumption and the energy consumption of the equipment; Identifying staffing needs for each production position based on the key control nodes; Identifying skill needs for each production position based on the scale of production; and identifying the workforce management needs based on the workforce needs and the skill needs.

[0012] Alternatively, the step of developing a management module and integrating a manufacturing execution system based on the actual production needs and the workforce management needs comprises: building an MES simulation system based on the actual production needs; Operate the MES simulation system based on the personnel management needs, and identify the smoothness of operation based on the operation results; determining whether there is a problem with the current operation based on the smoothness of the operation; If there is a problem, identifying and analyzing a stagnant node based on the operation result, and identifying an operation problem; Developing a management module and integrating it with a manufacturing execution system based on the actual production needs, the workforce management needs, and the operational issues.

[0013] Alternatively, the step of identifying the smoothness of the operation based on the operation results includes: determining a throughput and an average response time during the operation process based on the operation results; acquiring and analyzing construction data of the MES simulation system, and identifying material data and part data of the MES simulation system based on the data analysis results; determining a contact area of ​​a moving part of the MES simulation system and a relative velocity of the moving part of the MES simulation system based on the part data; determining a coefficient of friction between build materials and a mass of the MES simulation system based on the material data; comprehensively analyzing the part data and the material data to identify stiffness coefficients for the MES simulation system; and determining the smoothness of the operation based on the contact area, the relative velocity, the friction coefficient, the stiffness coefficient, and the mass, by referring to the following formula:

[0014] Number 2 JPEG2026020157000003.jpg19145 [where C is the smoothness of operation, T is the throughput, A is the average response time, k is the stiffness coefficient, m is the mass, H is the contact area, μ is the friction coefficient, v is the relative velocity of the moving parts of the MES simulation system, p is the medium density of the operating environment of the MES simulation system, c is the medium damping coefficient of the operating environment, a is the influence coefficient of k and m on damping, β is the influence coefficient of μ, H, and v on damping, and γ is the influence coefficient of p, c, and H on damping.]

[0015] Alternatively, the step of determining material consumption and equipment energy consumption per unit time based on the unit production amount and the production process includes the following steps: Identifying raw materials and equipment data for production based on the production process. A step of determining the amount of material consumed per unit time based on the raw materials for production, by referring to the following formula:

[0016] Number 3 JPEG2026020157000004.jpg22139 [In the formula, Mt represents the material consumption per unit time, n represents the quantity of the production raw materials, P represents the unit production volume, and Mi represents the unit consumption of the i-th type of production raw materials.] determining equipment usage parameters based on the equipment data; determining the energy consumption of the equipment per unit time based on the equipment usage parameters by referring to the following formula:

[0017] Number 4 JPEG2026020157000005.jpg19124 [In the formula, Te represents the energy consumption of the equipment per unit time, m represents the number of pieces of equipment, Wj represents the power of the jth piece of equipment, and Tj represents the unit execution time of the jth piece of equipment.]

[0018] In a second aspect, the present application provides an industrial smart management system based on MES, a data analysis module for acquiring past production performance data of the enterprise, analyzing the past production performance data, and identifying the production process, production scale, and product characteristics; a node identification module for analyzing the product characteristics and identifying key control nodes in the production process; a needs analysis module for obtaining enterprise management needs and identifying actual production needs and personnel management needs according to the enterprise management needs, the key control nodes and the production scale; a system development module for developing a management module based on the actual production needs and the personnel management needs and integrating it into a manufacturing execution system; The system as described above.

[0019] Alternatively, the data analysis module is specifically used to: Analyzing the past production performance data, identifying data characteristics of each past production performance data, classifying the past production performance data based on the data characteristics, and obtaining a manufacturing stage based on the classification result; Selecting the manufacturing steps to obtain a production process; Identifying production data based on the classification result, and identifying a production amount per unit time based on the production data; Identifying the production scale based on the production volume per unit time and a predetermined enterprise plan volume; Identifying inspection data based on the classification result, and identifying a product pass rate based on the inspection data; Identifying product ingredients based on the production process and the production data; The product characteristics are identified based on the product ingredients and the product acceptance rate.

[0020] Alternatively, the node identification module is specifically used to: After classification, past production performance data is analyzed to identify the unit production time and number of defective products for each production process. Identifying a production defect rate per unit time based on the number of defective products and the product characteristics; acquiring a production task volume for each production process, and identifying the production difficulty of each production process based on the production task volume, the unit production time, and the production defect rate per unit time; The production difficulty of each production process is sorted, and key control nodes are identified based on the sorting results.

[0021] Alternatively, when the node identification module identifies the production difficulty of each production process based on the production task volume, the unit production time, and the production defect rate per unit time, it is specifically used for the following purposes:

[0022] Number 5 JPEG2026020157000006.jpg26157 [In the formula, Di represents the production difficulty, Ti represents the production task volume of the i-th production process, ti represents the unit production time of the i-th production process, and Ri represents the production defect rate per unit time of the i-th production process.]

[0023] Alternatively, the needs analysis module is specifically used to: Capture enterprise architecture information, analyzing the enterprise architecture information to identify employee types; Identifying the job responsibilities of each employee type based on said employee types; Sending a needs acquisition signal to a manager corresponding to each employee type based on the job responsibilities, and receiving management needs transmitted from the corresponding manager; Based on the job responsibilities and the management needs sent by the relevant managers, enterprise management needs are derived.

[0024] Alternatively, the needs analysis module is specifically used to: Determine the unit production volume based on the production scale; Identifying material consumption and equipment energy consumption per unit time based on the unit production volume and the production process; Identifying the actual production needs based on the material consumption and the energy consumption of the equipment; Identifying staffing needs for each production position based on the key control nodes; Identifying the skill needs for each production position based on said production scale; The workforce management needs are identified based on the personnel needs and the skill needs.

[0025] Alternatively, the system development module is specifically used to: Based on the actual production needs, we build an MES simulation system. Operate the MES simulation system based on the personnel management needs, and identify the smoothness of operation based on the operation results; Determine whether there is a problem with the current operation based on the smoothness of the operation; If there is a problem, identify and analyze the stagnation node based on the operation result, and identify the operation problem; Based on the actual production needs, the personnel management needs and the operational issues, a management module is developed and integrated into the manufacturing execution system.

[0026] Alternatively, the system development module is specifically used to: Based on the operation results, a throughput and an average response time during the operation are identified; Acquire and analyze construction data of the MES simulation system, and based on the data analysis results, identify material data and part data of the MES simulation system; determining a contact area of ​​a moving part of the MES simulation system and a relative velocity of the moving part of the MES simulation system based on the part data; determining a coefficient of friction between build materials and a mass of the MES simulation system based on the materials data; Comprehensively analyzing the part data and the material data to identify stiffness coefficients of the MES simulation system; Based on the contact area, the relative velocity, the friction coefficient, the stiffness coefficient and the mass, the smoothness of operation is determined by referring to the following formula:

[0027] Number 6 JPEG2026020157000007.jpg18145 [where C is the smoothness of operation, T is the throughput, A is the average response time, k is the stiffness coefficient, m is the mass, H is the contact area, μ is the friction coefficient, v is the relative velocity of the moving parts of the MES simulation system, p is the medium density of the operating environment of the MES simulation system, c is the medium damping coefficient of the operating environment, a is the influence coefficient of k and m on damping, β is the influence coefficient of μ, H, and v on damping, and γ is the influence coefficient of p, c, and H on damping.]

[0028] Alternatively, the needs analysis module is specifically used to: Identifying raw materials and equipment data for production based on the production process; Based on the raw materials for production, the material consumption per unit time is determined by referring to the following formula:

[0029] Number 7 JPEG2026020157000008.jpg17133 [In the formula, Mt represents the material consumption per unit time, n represents the quantity of the production raw materials, P represents the unit production volume, and Mi represents the unit consumption of the i-th type of production raw materials.] Identifying equipment usage parameters based on the equipment data; Based on the equipment usage parameters, the energy consumption of the equipment per unit time is determined by referring to the following formula:

[0030] Number 8 JPEG2026020157000009.jpg21133 [In the formula, Te represents the energy consumption of the equipment per unit time, m represents the number of pieces of equipment, Wj represents the power of the jth piece of equipment, and Tj represents the unit execution time of the jth piece of equipment.]

[0031] In order to clearly describe the embodiments of the present application or the technical means in the prior art, the accompanying drawings that need to be used to depict the embodiments or the prior art will be briefly described below. The accompanying drawings depicted below are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these accompanying drawings without any creative effort. [Brief explanation of the drawings]

[0032] [Figure 1] FIG. 1 is a schematic diagram illustrating an application scenario provided in an embodiment of the present application. [Figure 2] 1 is a flowchart of an industrial smart management method based on MES provided in an embodiment of the present application; [Figure 3] FIG. 1 is a schematic configuration diagram of an industrial smart management system based on MES provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0033] In order to clarify the purpose, technical means and advantages of the present application, the technical means in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application, but it goes without saying that the described embodiments are only some of the embodiments of the present application and do not include all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments in the present application without any creative activity fall within the scope of protection of the present application.

[0034] As used herein, the term "and / or" includes any and all combinations of one or more associated listed elements, for example, A and / or B means that A exists alone, A and B exist simultaneously, or B exists alone. Also, as used herein, the symbol " / " generally means that the associated elements before and after it are in an "or" relationship, unless otherwise specified.

[0035] Hereinafter, embodiments of the present application will be specifically described with reference to the drawings of the specification.

[0036] Manufacturing execution systems are often large-scale, have many modules, and have complex logical relationships, making it difficult for companies to fully understand their functions and operations when they first introduce them. Some companies lack a clear understanding of the system when selecting a model, making it difficult to select a truly suitable MES for their company. Therefore, how to identify the appropriate manufacturing execution system based on a company's own characteristics is the key to more efficiently achieving smart industrial management.

[0037] Based on this, this application provides an MES-based intelligent industrial management method that deeply analyzes an enterprise's past production performance data and more accurately understands its existing production processes, production scale, and product characteristics, thereby providing data support for identifying the enterprise's production needs. Identifying key control nodes enables more effective monitoring and management during the production process, ensuring product quality and production efficiency. Based on the enterprise's management needs, key control nodes, and production scale, actual production needs and personnel management needs are identified, and the manufacturing stages are more accurately identified, improving the utilization rate of the manufacturing execution system, ensuring sufficient human resources on the production line, and avoiding human resource waste. By developing a management module based on actual production needs and personnel management needs and integrating the manufacturing execution system, a specialized manufacturing execution system can be created for the enterprise, reducing trial and error costs, more efficiently optimizing the production process, improving production efficiency, improving product quality, optimizing personnel management, and improving the enterprise's overall operating efficiency, thereby strengthening the enterprise's market competitiveness.

[0038] 1 is a schematic diagram illustrating an application scenario provided in one embodiment of the present application. When an enterprise needs to build a manufacturing execution system, it can use the method provided in the present application to build a manufacturing execution system suitable for the enterprise's own characteristics, improve management efficiency, and reduce the cost of trial and error.

[0039] Specifically, the method provided in this application is applied to an arbitrary server, and the server interacts with an enterprise's production management system, obtains and analyzes the past production performance data and enterprise management needs in the enterprise's production management system, accurately grasps the enterprise's actual production needs and personnel management needs, develops a management module, specializes a dedicated manufacturing execution system for the enterprise, reduces the cost of trial and error, more efficiently optimizes the production process, increases production efficiency, improves product quality, optimizes personnel management, and improves the enterprise's overall operating efficiency, thereby strengthening the enterprise's market competitiveness.

[0040] For specific implementation methods, please refer to the following embodiments.

[0041] 2 is a flowchart of an industrial smart management method based on MES provided in an embodiment of the present application. The method of this embodiment can be used for the server in the above scenario. As shown in FIG. 2, the method includes the following steps:

[0042] S201: A step of obtaining past production performance data of an enterprise, analyzing the past production performance data, and identifying a production process, production scale, and product characteristics.

[0043] The past production performance data can be understood as all data generated during production in a company's factory in the past period, and can be obtained from the company's existing production management system.

[0044] Specifically, a process analysis model is constructed and past production performance data is input into the process analysis model, thereby identifying the manufacturing stages of the factory and the dependencies between each stage, and obtaining the corresponding production process.

[0045] Data mining technology is used to analyze past production performance data and identify production rules for each production process, including the unit production volume of each production process, the production cycle of each production process, downtime, etc. The production scale of the enterprise is obtained by evaluation based on the above production rules.

[0046] Past production performance data is analyzed to identify the number and causes of rejected products that occurred in each production process in each production cycle, and product characteristics are identified based on the number and causes of rejected products.

[0047] S202: Analyzing product characteristics and identifying key control nodes in the production process.

[0048] Key control nodes can be understood as nodes that should be given importance, and these nodes may be nodes that have a significant impact on the success or failure of the product, or nodes that have a significant impact on the operation of the equipment itself.

[0049] Specifically, after obtaining the product characteristics, the locations where the rejected products occur are identified based on the causes of the rejected products obtained above, the locations where the rejected products occur are tallied, and the number of rejected products that occurred at each location is identified, whereby a weighted calculation is performed for each location according to the number of rejected products, and important control points are identified.

[0050] S203: obtaining enterprise management needs, and determining actual production needs and personnel management needs according to the enterprise management needs, key control nodes and production scale;

[0051] Actual production needs can be understood as the needs that actually need to be managed in the production process at an enterprise's factory. These actual production needs are obtained by comprehensively analyzing the enterprise's management needs, key control nodes and production scale, and may differ from the production needs stated by the enterprise's personnel.

[0052] The personnel management needs can be understood as the demands submitted by the company regarding personnel management, and are obtained by analyzing the past production performance data, which may differ from the personnel management needs stated by the company's stakeholders.

[0053] Specifically, when an enterprise needs to build a manufacturing execution system, it can independently submit a construction request, which includes the enterprise's management needs. Based on the production process, production scale, and product characteristics obtained from the analysis of the past production performance data, the enterprise's production capacity is evaluated, and in combination with the enterprise's production capacity, current market demand, and key control nodes, the enterprise's management needs are verified, and differences and similarities are identified based on the verification results, and the actual production needs of the enterprise are determined based on these differences and similarities.

[0054] Based on the above production process, clarify the positions existing within the enterprise and the job responsibilities of the corresponding positions, and based on the work characteristics of the key control nodes, identify the special job responsibilities required for the positions of these nodes, and based on the above content, identify the personnel management needs.

[0055] S204: Developing a management module and integrating it into the manufacturing execution system based on actual production needs and the personnel management needs.

[0056] Specifically, we identify the integration goals based on the actual production needs and personnel management needs, determine the company's implementation costs based on the above implementation requirements, formulate an integration plan by combining the implementation costs and integration goals, select appropriate technical tools and platforms, and develop a management module based on the above integration plan to integrate a manufacturing execution system that fully meets the company's actual situation.

[0057] The method provided in this embodiment enables in-depth analysis of a company's past production performance data, allowing for a more accurate understanding of the company's existing production processes, production scale, and product characteristics, thereby providing data support for identifying the company's production needs. Identifying key control nodes allows for more effective monitoring and management during the production process, ensuring product quality and production efficiency. Based on the company's management needs, key control nodes, and production scale, actual production needs and personnel management needs are identified, and the manufacturing stages are more accurately identified, improving the utilization rate of the manufacturing execution system. By developing a management module based on actual production needs and personnel management needs and integrating the manufacturing execution system, a specialized manufacturing execution system can be created for the company, reducing trial and error costs, more efficiently optimizing the production process, increasing production efficiency, improving product quality, optimizing personnel management, and improving the company's overall operating efficiency, thereby strengthening the company's market competitiveness.

[0058] In some embodiments, past production performance data is analyzed, data characteristics of each past production performance data are identified, the past production performance data is classified based on the data characteristics, the production stages are identified based on the classification results, the production stages are selected to identify the production process, production data is identified based on the classification results, a production volume per unit time is identified based on the production data, a production scale is identified based on the production volume per unit time and the predetermined enterprise plan volume, inspection data is identified based on the classification results, a product pass rate is identified based on the inspection data, product ingredients are identified based on the production process and production data, and product characteristics are identified based on the product ingredients and the product pass rate.

[0059] The manufacturing stages can be understood as all the processes involved during industrial production, and may include all the production processes involved in the production of a product, as well as the purchasing stages of raw materials and the subsequent transportation stages.

[0060] The predetermined enterprise planned volume can be understood as the production volume per unit time that is specified before the enterprise starts production, based on the evaluation of factors such as the product characteristics, the enterprise's production equipment, and the equipment performance, and this is the production volume under ideal conditions.

[0061] Inspection data can be understood as data corresponding to the stage of product inspection in the production process, and product pass rate can be understood as the product pass rate per unit time calculated by analyzing the inspection data, or the pass rate after all product inspections have been completed after this production.

[0062] Ingredients of a product can be understood as the raw materials required to produce said product.

[0063] Product characteristics can be understood as the properties that the product has.

[0064] Specifically, natural language processing technology is used to analyze past production performance data, identify data characteristics such as time, quantifiers, and materials, and identify the type of data to which the data corresponds based on these data characteristics. For example, time may refer to the usage time of the corresponding material or product, production time, or equipment startup or shutdown time. The specific type identified here can be more accurately identified by combining it with context information of the data, enabling more detailed data type classification.

[0065] After identifying the data type, all manufacturing steps of the product are identified, and all manufacturing steps are sorted out to identify the process of actually producing the product, such as the injection molding production process, and the purchase of raw materials for injection molding does not belong to the production process.

[0066] Based on the classification results obtained by the above classification, it is determined which data corresponds to the production process, and the production volume per unit time of the above product is determined based on the production data. At the same time, it is determined which data corresponds to the inspection process based on the classification results, and inspection data is obtained. The inspection data is used to determine the product pass rate when the above product was produced in the past.

[0067] Since there is a discrepancy between the production volume specified by a company itself and the actual production volume, the actual production scale of the company is derived by combining the actual production volume per unit time and the predetermined company planned volume planned by the company itself.

[0068] By identifying the raw materials used in each process based on production data and production processes, the product's ingredients are identified, and by combining the analysis results of the product's ingredients and product pass rate, the product's important characteristics (e.g., physical properties, chemical properties, lifespan, etc.) are identified.

[0069] The method provided in this embodiment analyzes and classifies past production data to more clearly identify manufacturing stages and processes. The production scale is determined based on the production volume per unit time and the company's predetermined planned volume, preventing the problem of a company's unclear self-identification resulting in the subsequent manufacturing execution system not matching the company's production scale and resulting in unnecessary cost waste. Analyzing product characteristics by combining product ingredients and product pass rates also helps to better understand the uniqueness of the product, making it easier to build manufacturing execution system functions for future products and improving the utilization rate of the manufacturing execution system.

[0070] In some embodiments, the classified past production performance data is analyzed, the unit production time and the number of defective products for each production process are identified, the production defect rate per unit time is identified based on the number of defective products and product characteristics, the production task volume for each production process is obtained, the production difficulty of each production process is identified based on the production task volume, unit production time, and production defect rate per unit time, the production difficulty of each production process is sorted, and key control nodes are identified based on the sorting results.

[0071] Unit production time can be understood as the time required to produce a product.

[0072] The number of defective products can be understood as the number of products that are judged to be unacceptable out of all the products produced.

[0073] The production defect rate can be understood as the proportion of rejected products among all products produced per unit time.

[0074] Specifically, the past production performance data after classification is analyzed to check whether data of the data types "reject" and "production volume" exists. If there is data of the data type, the number of products judged as "reject" in the relevant time period is tallied based on the reject type data to obtain the number of defective products in the relevant time period. The production volume type data is used to calculate the average production value for the relevant time period, and the unit production time required to produce products in each production process is derived.

[0075] Calculate the production defect rate within a unit time (per hour, per day) based on the number of defective products and the corresponding production volume. Calculation formula: Defective product rate = number of defective products / production volume x 100%.

[0076] The production task volume for each production process, i.e., the planned total production volume, is obtained from the production plan. This production plan can be obtained from order demand. The production difficulty of each production process is comprehensively evaluated by combining the unit production time, production defect rate per unit time, and production task volume. All production processes are sorted in order of high or low production difficulty. Based on the sorting results, several production processes with the highest production difficulty are identified as key control nodes.

[0077] The method provided in this embodiment ensures objectivity and accuracy of the analysis by conducting analysis based on classified past production data. By comprehensively considering multiple factors, such as production task volume, unit production time, and production defect rate per unit time, the difficulty of the production process can be quantified, facilitating comparisons between different production processes and enabling clearer and more accurate identification of production bottlenecks. The resulting manufacturing execution system can be better adapted to the actual production process and key control nodes, reducing cost waste and improving system utilization and management levels.

[0078] In some embodiments, the production difficulty of each production process is determined based on the production task amount, the unit production time, and the production defect rate per unit time, by referring to the following formula (1).

[0079] Number 9 JPEG2026020157000010.jpg19120 [In the formula, Di represents the production difficulty, Ti represents the production task volume of the i-th production process, ti represents the unit production time of the i-th production process, and Ri represents the production defect rate per unit time of the i-th production process.]

[0080] The means provided in this embodiment comprehensively considers multiple factors such as the amount of production tasks, unit production time, and production defect rate per unit time, and designs a formula for calculating the production difficulty of each production process, which more comprehensively reflects the actual situation of the production process, enables an objective comparison of the difficulty between different production processes, avoids deviations caused by subjective judgment, and ensures that the evaluation of production difficulty is more scientific and accurate.

[0081] In some embodiments, enterprise architecture information is acquired, the enterprise architecture information is analyzed to identify employee types, job responsibilities of each employee type are identified based on the employee types, a needs acquisition signal is sent to a manager corresponding to each employee type based on the job responsibilities, management needs sent from the corresponding manager are received, and enterprise management needs are derived based on the job responsibilities and the management needs sent from the corresponding manager.

[0082] Enterprise architecture information can be understood as data relating to enterprise architecture, such as departmental arrangements, job classifications, employee hierarchies, and the like.

[0083] The needs acquisition signal can be understood as a signal sent to each manager to gather requirements for the manufacturing execution system.

[0084] Specifically, enterprise architecture information is obtained through routes such as the company's official website, internal documents, organizational charts, employee manuals, etc., and the enterprise architecture information is analyzed to identify the company's job classifications and employee types.

[0085] According to the employee type and the department setup and employee hierarchy in the architecture information, the job responsibilities of the position of the employee of the corresponding type are identified, and the manager of the corresponding department is identified based on the job responsibilities, and a needs acquisition signal is sent to this manager. After receiving the signal, the manager of the corresponding department receives the management needs compiled by the manager, and obtains the enterprise management needs by integrating the job responsibilities and the management needs sent by the corresponding manager.

[0086] The method provided in this embodiment starts with obtaining enterprise architecture information, then goes through a series of systematic analyses to finally obtain enterprise management needs, ensuring the systematic and comprehensiveness of the information and avoiding omissions and biases. First, the enterprise architecture information is analyzed to identify employee types. Then, the job responsibilities and management needs submitted by the relevant managers are comprehensively considered to ensure the comprehensiveness and practicality of the management needs.

[0087] In some embodiments, unit production volume is determined based on the production scale, material consumption and equipment energy consumption per unit time are determined based on the unit production volume and the production process, actual production needs are determined based on the material consumption and equipment energy consumption, personnel needs for each production position are determined based on key control nodes, skill needs for each production position are determined based on the production scale, and personnel management needs are determined based on the personnel needs and skill needs.

[0088] Actual production needs can be understood as the actual needs throughout the production process.

[0089] Personnel needs can be understood as the characteristics, traits, and personalities that the personnel required at the relevant key control node should possess.

[0090] Skill needs can be understood as the skills that workers need to possess in the production of the above products.

[0091] Specifically, the production capacity of the company is evaluated based on the company's production scale, and the unit production volume for each production cycle or period is determined. When the production volume is known based on the unit production volume, the average amount of materials consumed to produce these products is calculated to obtain the material consumption volume. In a specific implementation method, the total production volume per unit time and the waste situation of materials are comprehensively calculated.

[0092] Equipment operation data is acquired, the amount of energy consumed during product production is obtained from the equipment operation data, and the equipment energy consumption per unit time is calculated based on the total energy consumption.

[0093] Based on the material consumption and equipment energy consumption obtained above, mathematical analysis methods are used to calculate which aspects of the manufacturing stage the company should manage after actual production starts, such as equipment maintenance cycle management, material renewal or purchasing management, etc.

[0094] Based on past production performance data, the complexity and workload of each key control node are evaluated, the number of personnel required for each key control node is calculated, and personnel needs are derived.In addition, the specialized skills that workers at each key control node should acquire are identified according to the complexity of that node, and the skill needs for each key control node are clarified.Similarly, the skill needs for other production processes can be calculated based on the complexity of the relevant process.

[0095] Based on the personnel needs and skill needs, clarify the personnel management goals, such as improving production efficiency and reducing costs, and identify the personnel management needs. For example, refer to Table (1) to create a personnel management plan.

[0096] Table 1 JPEG2026020157000011.jpg51170

[0097] The method provided in this embodiment identifies unit production volume based on production scale, ensuring that production capacity matches market demand. By identifying material consumption and equipment energy consumption per unit time based on unit production volume and production process, enterprises can optimize resource allocation, reduce waste, and improve resource utilization efficiency with the help of the established manufacturing execution system. Identifying personnel needs for each production position based on key control nodes ensures that key positions receive sufficient human resource support, improving the stability and efficiency of the production process. Identifying personnel management needs based on personnel needs and skill needs helps promote enterprise management, reduce system construction costs, and improve automation management efficiency.

[0098] In some embodiments, an MES simulation system is constructed based on actual production needs, the MES simulation system is operated based on personnel management needs, the smoothness of operation is determined based on the operation results, whether there are any problems with the current operation is determined based on the smoothness of operation, and if there are any problems, stagnation nodes are identified and analyzed based on the operation results, and the operational problems are identified. A management module is developed based on the actual production needs, personnel management needs, and operational problems, and the manufacturing execution system is integrated.

[0099] Specifically, the actual production needs are analyzed, and several contents such as the production scale, product characteristics, production process, key control nodes, and skill requirements disclosed in the above embodiments are obtained, and an appropriate simulation tool is selected to build an MES simulation system for simulating the operation process of the manufacturing execution system using the above data.

[0100] By running and simulating the manufacturing execution system in a simulation environment, various activities and decision-making processes in the entire product production cycle are simulated, key indicators of the simulation process, such as production efficiency, material consumption, and equipment energy consumption, are identified, and the smoothness of the operation of the manufacturing execution system is evaluated based on the key indicators.

[0101] The smoothness of operation is used to determine whether there is a bottleneck at a certain stage. If there is, the bottleneck node can be identified using methods such as simulation playback or log analysis. The bottleneck node is then analyzed in depth to identify the cause of the bottleneck, such as insufficient data processing capacity or uneven resource allocation. In specific implementation, uncertainties and dynamic change factors in the actual production environment, such as equipment failures, material shortages, and personnel changes, are considered, and these factors are added to the simulation environment to evaluate the processing ability of the manufacturing execution system when faced with a series of problems in the actual production environment. A comprehensive evaluation is then performed to determine the smoothness of operation. A management module is then developed based on actual production needs, personnel management needs, and operational issues discovered during the simulation to integrate the manufacturing execution system.

[0102] The means provided in this embodiment allows an MES simulation system to be constructed using actual production needs, and determines whether the initially constructed system has any defects that will affect the operation of the system. If any defects are found, they can be addressed immediately. In addition, by optimizing the constructed MES simulation system based on actual production needs and personnel management needs, the system can support enterprise management more quickly and efficiently, and improve overall production efficiency.

[0103] In some embodiments, the throughput and average response time during the operation process are determined based on the operation results; construction data of the MES simulation system is acquired and analyzed; material data and part data of the MES simulation system are determined based on the data analysis results; the contact area of ​​the moving parts of the MES simulation system and the relative speed of the moving parts of the MES simulation system are determined based on the part data; the friction coefficient between the construction materials and the mass of the MES simulation system are determined based on the material data; and the stiffness coefficient of the MES simulation system is determined by comprehensively analyzing the part data and material data; Based on the contact area, relative velocity, friction coefficient, stiffness coefficient and mass, the smoothness of operation is determined by referring to the following formula (2).

[0104] Number 10 JPEG2026020157000012.jpg21152 [Where C is the smoothness of operation, T is the throughput, A is the average response time, k is the stiffness coefficient, m is the mass, H is the contact area, μ is the friction coefficient, v is the relative speed of the moving parts of the MES simulation system, p is the medium density of the operating environment of the MES simulation system, c is the medium damping coefficient of the operating environment, a is the influence coefficient of k and m on damping, β is the influence coefficient of μ, H, and v on damping, and γ is the influence coefficient of p, c, and H on damping.]

[0105] Throughput can be understood as the amount of data successfully transferred by a network, device, port, virtual circuit, or other facility within a unit time, and can reflect the data processing capability of a manufacturing execution system.

[0106] The average response time can be understood as the average time it takes to process a request from the start of queuing to the end of execution after a specific request operation is executed through the manufacturing execution system, and can reflect the speed and efficiency of request processing in the manufacturing execution system.

[0107] Build data can be understood as all data necessary to build a manufacturing execution system, and can include hardware-related data and software-related data.

[0108] Material data can be understood as data corresponding to the materials of the hardware required to build a manufacturing execution system, and can include attributes such as the type, density, and hardness of the material.

[0109] The part data can be understood as hardware-related data required to build a manufacturing execution system, and may include the size, shape, motion trajectory, etc. of each part.

[0110] Moving parts can be understood as parts that physically move during operation of the manufacturing execution system.

[0111] Specifically, after obtaining the operation results, the total number of tasks processed by the MES during this operation process is calculated from the operation results and used as the throughput.The processing time for each task is calculated, and the sum of all processing times is divided by the total number of tasks to calculate the average response time.

[0112] Relevant construction data is extracted from the construction process of the MES simulation system, analyzed, and the materials and parts used in the construction process are identified and corresponding data is obtained. Geometric calculation methods are used to obtain the contact area between each moving part and other moving parts, and then the relative speed of the moving parts is obtained from the relative motion relationship between the moving parts. Based on the material data obtained above, the friction coefficient between these materials is searched or calculated. The friction coefficient between these materials can be obtained by testing or by utilizing existing knowledge in materials science.

[0113] After obtaining material data and component data, the attribute information of each material or component is obtained to calculate the corresponding mass, which is then summed to determine the mass of the MES simulation system.The component and material data are comprehensively analyzed, and the stiffness coefficient of the MES simulation system is calculated using engineering mechanics formulas or simulation software.

[0114] After obtaining the contact area, relative velocity, friction coefficient, stiffness coefficient and mass by the above method, these parameters are quantified and equation (2) is designed to calculate the smoothness of operation.

[0115] The method provided in this embodiment comprehensively considers multiple factors and designs a formula to more accurately reflect the smoothness of actual operation of the MES simulation system, avoiding bias in evaluation based on a single indicator. By quantifying contact area, relative velocity, friction coefficient, stiffness coefficient, and mass, the evaluation results of operation smoothness are more accurate and objective, providing an accurate data base for determining whether there are problems with the simulation of the manufacturing execution system.

[0116] In some embodiments, production raw materials and equipment data are identified based on the production process, and the material consumption per unit time is identified based on the production raw materials by referring to the following formula (3).

[0117] Number 11 JPEG2026020157000013.jpg16139 [In the formula, Mt represents the material consumption per unit time, n represents the quantity of raw materials for production, P represents the unit production volume, and Mi represents the unit consumption of the i-th type of raw material for production.]

[0118] Based on the equipment data, equipment usage parameters are identified, and based on the equipment usage parameters, the energy consumption of the equipment per unit time is identified by referring to the following equation (4).

[0119] Number 12 JPEG2026020157000014.jpg22134 [In the formula, Te represents the energy consumption of the equipment per unit time, m represents the number of pieces of equipment, Wj represents the power of the jth piece of equipment, and Tj represents the unit execution time of the jth piece of equipment.]

[0120] The method provided in this embodiment takes into account the unit consumption and unit production volume of all production raw materials, and designs a formula to accurately calculate material consumption per unit time. This improves the reliability and practicality of the calculation results, contributing to precise management and optimization of the production process. Designing a formula taking into account the power and unit execution time of all equipment allows for accurate calculation of equipment energy consumption per unit time, contributing to the evaluation of the energy consumption and operating costs of the entire equipment.

[0121] FIG. 3 is a schematic configuration diagram of an MES-based industrial smart management system provided in one embodiment of the present application. As shown in FIG. 3, the MES-based industrial smart management system 300 of this embodiment includes a data analysis module 301, a node identification module 302, a needs analysis module 303, and a system development module 304.

[0122] The data analysis module 301 is used to acquire the enterprise's past production performance data, analyze the past production performance data, and identify the production process, production scale, and product characteristics; a node identification module 302 for analyzing the product characteristics and identifying key control nodes in the production process; The needs analysis module 303 is used to obtain enterprise management needs, and identify actual production needs and personnel management needs according to the enterprise management needs, the key control nodes and the production scale; The system development module 304 is used to develop a management module and integrate a manufacturing execution system based on the actual production needs and the personnel management needs.

[0123] Alternatively, the data analysis module 301 is specifically used to: Analyzing the past production performance data, identifying data characteristics of each past production performance data, classifying the past production performance data based on the data characteristics, and obtaining a manufacturing stage based on the classification result; Selecting the manufacturing steps to obtain a production process; Identifying production data based on the classification result, and identifying a production amount per unit time based on the production data; Identifying the production scale based on the production volume per unit time and a predetermined enterprise plan volume; Identifying inspection data based on the classification result, and identifying a product pass rate based on the inspection data; Identifying product ingredients based on the production process and the production data; The product characteristics are identified based on the product ingredients and the product acceptance rate.

[0124] Alternatively, the node identification module 302 may be specifically used to: After classification, past production performance data is analyzed to identify the unit production time and number of defective products for each production process. Identifying a production defect rate per unit time based on the number of defective products and the product characteristics; acquiring a production task volume for each production process, and identifying the production difficulty of each production process based on the production task volume, the unit production time, and the production defect rate per unit time; The production difficulty of each production process is sorted, and key control nodes are identified based on the sorting results.

[0125] Alternatively, when the node identification module 302 identifies the production difficulty of each production process based on the production task volume, the unit production time, and the production defect rate per unit time, it is specifically used for the following purposes:

[0126] Number 13 JPEG2026020157000015.jpg17130 [In the formula, Di represents the production difficulty, Ti represents the production task volume of the i-th production process, ti represents the unit production time of the i-th production process, and Ri represents the production defect rate per unit time of the i-th production process.]

[0127] Alternatively, the needs analysis module 303 may be specifically used to: Capture enterprise architecture information, analyzing the enterprise architecture information to identify employee types; Identifying the job responsibilities of each employee type based on said employee types; Sending a needs acquisition signal to a manager corresponding to each employee type based on the job responsibilities, and receiving management needs transmitted from the corresponding manager; Based on the job responsibilities and the management needs sent by the relevant managers, enterprise management needs are derived.

[0128] Alternatively, the needs analysis module 303 may be specifically used to: Determine the unit production volume based on the production scale; Identifying material consumption and equipment energy consumption per unit time based on the unit production volume and the production process; Identifying the actual production needs based on the material consumption and the energy consumption of the equipment; Identifying staffing needs for each production position based on the key control nodes; Identifying the skill needs for each production position based on said production scale; The workforce management needs are identified based on the personnel needs and the skill needs.

[0129] Alternatively, the system development module 304 may be specifically used to: Based on the actual production needs, we build an MES simulation system. Operate the MES simulation system based on the personnel management needs, and identify the smoothness of operation based on the operation results; Determine whether there is a problem with the current operation based on the smoothness of the operation; If there is a problem, identify and analyze the stagnation node based on the operation result, and identify the operation problem; Based on the actual production needs, the personnel management needs and the operation problems, a management module is developed and integrated into the manufacturing execution system.

[0130] Alternatively, the system development module 304 may be specifically used to: Based on the operation results, a throughput and an average response time during the operation are identified; Acquire and analyze construction data of the MES simulation system, and based on the data analysis results, identify material data and part data of the MES simulation system; determining a contact area of ​​a moving part of the MES simulation system and a relative velocity of the moving part of the MES simulation system based on the part data; determining a coefficient of friction between build materials and a mass of the MES simulation system based on the materials data; Comprehensively analyzing the part data and the material data to identify stiffness coefficients of the MES simulation system; Based on the contact area, the relative velocity, the friction coefficient, the stiffness coefficient and the mass, the smoothness of operation is determined by referring to the following formula:

[0131] Number 14 JPEG2026020157000016.jpg19132 [where C is the smoothness of operation, T is the throughput, A is the average response time, k is the stiffness coefficient, m is the mass, H is the contact area, μ is the friction coefficient, v is the relative velocity of the moving parts of the MES simulation system, p is the medium density of the operating environment of the MES simulation system, c is the medium damping coefficient of the operating environment, a is the influence coefficient of k and m on damping, β is the influence coefficient of μ, H, and v on damping, and γ is the influence coefficient of p, c, and H on damping.]

[0132] Alternatively, the needs analysis module 303 may be specifically used to: Identifying raw materials and equipment data for production based on the production process; Based on the raw materials for production, the material consumption per unit time is determined by referring to the following formula:

[0133] Number 15 JPEG2026020157000017.jpg19130 [In the formula, Mt represents the material consumption per unit time, n represents the quantity of the production raw materials, P represents the unit production volume, and Mi represents the unit consumption of the i-th type of production raw materials.] Identifying equipment usage parameters based on the equipment data; Based on the equipment usage parameters, the energy consumption of the equipment per unit time is determined by referring to the following formula:

[0134] Number 16 JPEG2026020157000018.jpg21122 [In the formula, Te represents the energy consumption of the equipment per unit time, m represents the number of pieces of equipment, Wj represents the power of the jth piece of equipment, and Tj represents the unit execution time of the jth piece of equipment.]

[0135] The system of this embodiment can also be used to implement the method of any of the above embodiments, and the implementation principles and technical effects are similar, so detailed descriptions thereof will be omitted here.

Claims

1. An industrial smart management method based on MES, comprising: acquiring past production performance data of the enterprise, analyzing the past production performance data, and identifying the production process, production scale, and product characteristics; A step of analyzing the product characteristics and identifying key control nodes used to characterize nodes that should be considered important in the production process, including at least one of nodes that have a significant impact on the success or failure of the product and nodes that have a significant impact on the operation of the equipment itself; obtaining enterprise management needs, and determining actual production needs and personnel management needs according to the enterprise management needs, the key control node and the production scale; developing a management module and integrating it into a manufacturing execution system based on the actual production needs and the workforce management needs; The step of analyzing the past production performance data and identifying a production process, a production scale, and product characteristics includes: analyzing the past production performance data, identifying data characteristics of each past production performance data, classifying the past production performance data based on the data characteristics, and obtaining a manufacturing stage based on the classification result; screening the manufacturing steps to obtain a production process; identifying production data based on the classification result, and identifying a production amount per unit time based on the production data; determining the production scale based on the production volume per unit time and a predetermined enterprise plan volume; Identifying test data based on the classification results; determining a product pass rate based on the inspection data; identifying product ingredients based on the production process and the production data; and identifying the product characteristics based on the product ingredients and the product acceptance rate; The step of analyzing the product characteristics and identifying key control nodes in the production process comprises: A step of analyzing the past production performance data after classification and identifying the unit production time and the number of defective products for each production process; determining a production defect rate per unit time based on the number of defective products and the product characteristics; acquiring a production task volume for each production process, and identifying the production difficulty of each production process based on the production task volume, the unit production time, and the production defect rate per unit time; Sorting the production difficulty of each production process and identifying a key control node based on the sorting result; The method is characterized in that the step of specifying the production difficulty of each production process based on the production task amount, the unit production time, and the production defect rate per unit time refers to the following formula: Number 1 [In the formula, Di represents the production difficulty, Ti represents the production task amount of the i-th production process, ti represents the unit production time of the i-th production process, and Ri represents the production defect rate per unit time of the i-th production process.]

2. The step of obtaining enterprise management needs includes: obtaining enterprise architecture information; analyzing the enterprise architecture information to identify employee types; Identifying job responsibilities for each employee type based on the employee types; transmitting a needs acquisition signal to a manager corresponding to each employee type based on the job responsibilities, and receiving management needs transmitted from the corresponding manager; Deriving enterprise management needs based on the job responsibilities and management needs sent by the relevant managers; 2. The method of claim 1, comprising:

3. The step of identifying actual production needs and personnel management needs based on the enterprise management needs, the key control node, and the production scale includes: Identifying a unit production amount based on the production scale; determining material consumption and equipment energy consumption per unit time based on the unit production volume and the production process; determining the actual production needs based on the material consumption and the energy consumption of the equipment; Identifying staffing needs for each production position based on the key control nodes; Identifying skill needs for each production position based on the scale of production; identifying the workforce management needs based on the workforce needs and the skill needs; 2. The method of claim 1, comprising:

4. developing a management module and integrating it with a manufacturing execution system based on the actual production needs and the personnel management needs; building an MES simulation system based on the actual production needs; Operate the MES simulation system based on the personnel management needs, and identify the smoothness of operation based on the operation results; determining whether there is a problem with the current operation based on the smoothness of the operation; If there is a problem, identifying and analyzing a stagnant node based on the operation result, and identifying an operation problem; developing a management module and integrating it with a manufacturing execution system based on the actual production needs, the workforce management needs, and the operational issues; 2. The method of claim 1, comprising:

5. The step of determining the smoothness of the operation based on the operation result includes: determining a throughput and an average response time during the operation process based on the operation results; acquiring and analyzing construction data of the MES simulation system, and identifying material data and part data of the MES simulation system based on the data analysis results; determining a contact area of ​​a moving part of the MES simulation system and a relative velocity of the moving part of the MES simulation system based on the part data; determining a coefficient of friction between build materials and a mass of the MES simulation system based on the materials data; collectively analyzing the part data and the material data to identify stiffness coefficients for the MES simulation system; determining the smoothness of the operation based on the contact area, the relative velocity, the friction coefficient, the stiffness coefficient, and the mass, by referring to the following formula:

5. The method of claim 4, comprising: Number 2 where C is the smoothness of operation, T is the throughput, A is the average response time, k is the stiffness coefficient, m is the mass, H is the contact area, μ is the friction coefficient, v is the relative velocity of the moving parts of the MES simulation system, p is the medium density of the operating environment of the MES simulation system, c is the medium damping coefficient of the operating environment, a is the influence coefficient of k and m on damping, β is the influence coefficient of μ, H, and v on damping, and γ is the influence coefficient of p, c, and H on damping.

6. 4. The method according to claim 3, wherein the step of identifying material consumption and facility energy consumption per unit time based on the unit production volume and the production process includes the following steps: Identifying raw materials and equipment data for production based on the production process. A step of determining the amount of material consumed per unit time based on the raw materials for production, by referring to the following formula: Number 3 [In the formula, Mt represents the material consumption per unit time, n represents the quantity of the production raw materials, P represents the unit production amount, and Mi represents the unit consumption amount of the i-th type of production raw material.] determining equipment usage parameters based on the equipment data; determining the energy consumption of the equipment per unit time based on the equipment usage parameters by referring to the following formula: Number 4 [In the formula, Te represents the energy consumption of the equipment per unit time, m represents the number of equipment, Wj represents the power of the jth equipment, and Tj represents the unit execution time of the jth equipment.]

7. An industrial smart management system based on MES, which is used in the method according to any one of claims 1 to 6, a data analysis module for acquiring past production performance data of the enterprise, analyzing the past production performance data, and identifying the production process, production scale, and product characteristics; a node identification module for analyzing the product characteristics and identifying key control nodes in the production process; a needs analysis module for obtaining enterprise management needs and identifying actual production needs and personnel management needs according to the enterprise management needs, the key control nodes and the production scale; a system development module for developing a management module based on the actual production needs and the personnel management needs and integrating it into a manufacturing execution system; A system comprising:

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