Production order management method and system based on artificial intelligence

Abstract

The invention belongs to the field of production management, relates to a data analysis technology, and aims to solve the problem that production tasks cannot be distributed according to production characteristics of a production line in the prior art, in particular to a production order management method and system based on artificial intelligence. The order management platform is in communication connection with a production line management module, a unified analysis module, a production management module, a supplementary distribution module and a database; the production line management module is used for managing and analyzing the production line of the enterprise: generating a continuous management period, marking the production line of the enterprise as a management object, marking a product type covered by the management object as a production object of the management object, and forming a production set of the management object by all the production objects of the management object; according to the invention, the production line of an enterprise can be managed and analyzed, production parameter statistics is carried out according to the recent production state of the production line, and data support is provided for product type distribution in the production line and a production order.

Description

Technical Field

[0001] The present invention belongs to the field of production management and relates to data analysis technology, specifically to a production order management method and system based on artificial intelligence. Background Art

[0002] Production order management refers to the process of converting customer orders into production instructions and ensuring that products are delivered on time and meet predetermined quality standards by planning, tracking and managing the entire production process. It covers order receipt, planning and scheduling, material procurement, production execution, quality control and delivery. An enterprise's production orders are usually many orders gathered together, and the products that a production line can cover are generally diverse. Existing technology cannot allocate production tasks based on the production characteristics of the production line, and complex production plans cannot be simplified. Ultimately, production tasks can only be allocated based on the single factor of the production line's output, resulting in the inability to take into account both the output and the pass rate of the product.

[0003] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0004] The purpose of the present invention is to provide a production order management method and system based on artificial intelligence, which is used to solve the problem that the existing technology cannot allocate production tasks according to the production characteristics of the production line; The technical problem to be solved by the present invention is: how to provide an artificial intelligence-based production order management method and system that can allocate production tasks according to the production characteristics of the production line.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An artificial intelligence-based production order management system includes an order management platform, wherein the order management platform is communicatively connected to a production line management module, a unified analysis module, a production management module, a supplementary allocation module, and a database; The production line management module is used to manage and analyze the production line of the enterprise: generate a continuous management cycle, mark the production line of the enterprise as a management object, mark the product type covered by the management object as the production object of the management object, and form a production set of the management object by all the production objects of the management object. At the beginning of the management cycle, the production parameters of the production set of the management object are obtained, and the management object is marked as a unified object or a divergent object according to the production parameters; The unified analysis module is used to perform priority production analysis on the unified objects of the enterprise and mark the priority objects of the unified objects, and all the priority objects of the unified objects constitute a priority set of the unified objects; The production management module is used to manage and analyze the production plan of the enterprise: at the start of each production day, task data is retrieved, the task data includes product type LXi and product quantity SLi, i=1, 2, ..., n, n is a positive integer, the unified object containing product type LXi in the priority set is marked as the allocation production line, the allocation production line with the largest value of the priority coefficient YX corresponding to the product type LXi is marked as the matching production line of the product type LXi, the product of the efficiency data of the matching production line and the estimated production time of the production day is marked as the estimated production value of the matching production line, and the product quantity SLi of the product type LXi is updated according to the estimated production value; The supplementary allocation module is used to perform production line allocation analysis on the product types that have not been allocated and obtain an initial task allocation plan, and send the initial task allocation plan to the mobile phone terminal of the manager through the order management platform.

[0006] Furthermore, the specific process of marking a management object as a unified object or a divergent object includes: the production parameters include efficiency data and defect data, the efficiency data is the ratio of the production quantity of the production object completed by the management object in the previous management cycle to the corresponding production time, and the defect data is the defect rate of the production task of the production object performed by the management object in the previous management cycle; the production parameters are processed to obtain a unified coefficient of the management object, a unified threshold is retrieved through the database, and the unified coefficient of the management object is compared with the unified threshold: if the unified coefficient is less than the unified threshold, it is determined that the management object has a unified production feature, and the corresponding management object is marked as a unified object; if the unified coefficient is greater than or equal to the unified threshold, it is determined that the management object does not have a unified feature, and the corresponding management object is marked as a divergent object.

[0007] Furthermore, the process of obtaining the unified coefficient of the management object includes: arranging all elements of the production set according to the order of efficiency data from large to small to obtain the efficiency sequence of the management object, arranging all elements of the production set according to the order of defect data from small to large to obtain the defect sequence of the management object, marking the absolute value of the difference between the sequence number of the production object in the efficiency sequence and the sequence number in the defect sequence as the unified value of the production object, and summing and averaging the unified values ​​of all production objects in the production set of the management object to obtain the unified coefficient of the management object.

[0008] Furthermore, the specific process of marking the priority objects of the unified object includes: calculating the production parameters to obtain the priority coefficient YX of the production objects in the production set of the unified object; arranging all the production objects in the production set of the unified object in descending order according to the value of the priority coefficient YX to obtain a unified sequence of the unified object, and intercepting the top L1 production objects in the unified sequence and marking them as priority objects of the unified object.

[0009] Furthermore, the specific process of updating the product quantity SLi of the product type LXi includes: comparing the expected production value with the product quantity SLi corresponding to the product type LXi: if the expected production value is less than the production quantity SLi, then marking the difference between the production quantity SLi and the expected production value as the supplementary quantity BCi, and replacing the product quantity SLi with the supplementary quantity BCi; if the expected production value is greater than or equal to the production quantity SLi, then marking the product quantity SLi corresponding to the product type LXi as zero.

[0010] Furthermore, the specific process of the supplementary allocation module performing production line allocation analysis on the product types that have not completed allocation includes: marking the unified objects that have not completed matching with the product type LXi as divergent objects, marking the product type LXi with a product quantity SLi not zero as the analysis type FXi, marking the divergent objects containing the analysis type FXi in the production set as preliminary screening objects, summing up and averaging the efficiency data corresponding to the analysis type FXi in the production parameters of all preliminary screening objects to obtain the efficiency performance value, marking the ratio of the product quantity SLi of the analysis type FXi to the efficiency performance value as the duration performance value, marking the matching production lines of the analysis type FXi by the duration performance value; and forming an initial task allocation plan by all product types LXi and the corresponding matching production lines.

[0011] Furthermore, the specific process of marking the matching production line of the analysis type FXi includes: retrieving the duration performance threshold through the database, and comparing the duration performance value with the duration performance threshold: if the duration performance value is less than the duration performance threshold, then the serial number of the analysis type FXi in the residual sequence of the initial screening object is marked as the residual matching value of the initial screening object, and the initial screening object with the smallest residual matching value is marked as the matching production line of the analysis type FXi; if the duration performance value is greater than or equal to the duration performance threshold, then the serial number of the analysis type FXi in the efficiency sequence of the initial screening object is marked as the efficiency matching value of the initial screening object, and the initial screening object with the smallest efficiency matching value is marked as the matching production line of the analysis type FXi.

[0012] A production order management method based on artificial intelligence, comprising the following steps: Step 1: Conduct management analysis on the enterprise's production line: generate a continuous management cycle, mark the enterprise's production line as a management object, obtain the unified coefficient of the management object, and mark the management object as a unified object or a divergent object through the unified coefficient; Step 2: Perform a priority production analysis on the unified object of the enterprise and obtain the priority coefficient YX of all production objects in the production set of the unified object, and mark the priority objects of the unified object by the priority coefficient YX; Step 3: Manage and analyze the production plan of the enterprise: retrieve the task data at the start of each production day, mark the product of the efficiency data of the matching production line and the estimated production time of the production day as the estimated production value of the matching production line, and mark the product quantity SLi corresponding to the product type LXi by the estimated production value; Step 4: Perform production line allocation analysis on the product types that have not been allocated and obtain an initial task allocation plan, which is then sent to the manager's mobile terminal via the order management platform.

[0013] The present invention has the following beneficial effects: 1. The production line management module can be used to manage and analyze the production line of the enterprise, perform production parameter statistics based on the recent production status of the production line, and then calculate the uniform coefficient of the management object and mark the management object differently based on the uniform coefficient, providing data support for the allocation of product types in the production line and production order; 2. The unified analysis module can be used to conduct priority production analysis on the unified objects of the enterprise, prioritize product type allocation for unified objects with unified production characteristics, and screen the priority objects of the unified objects through the priority coefficient. The priority objects are the product types that are suitable for priority matching with the unified objects, thereby improving the degree of fit between the production line and the product type; 3. The production management module can be used to manage and analyze the production plan of the enterprise, allocate production tasks to the unified production line at the start of each production day, and update the number of products after allocation based on the number of products corresponding to the product type, providing data support for the task allocation process of divergent production lines; 4. The supplementary allocation module can be used to analyze the production line allocation of product types that have not been fully allocated. The basis for production line allocation can be analyzed according to the duration performance value of the analysis type. When production pressure is high, production efficiency is given priority to ensure that orders are completed on time. When production pressure is low, qualified rate is given priority to reduce raw material consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0017] Embodiment 1: Figure 1 As shown, an artificial intelligence-based production order management system includes an order management platform, which is communicatively connected to a production line management module, a unified analysis module, a production management module, a supplementary allocation module and a database.

[0018] The production line management module is used to manage and analyze the production lines of the enterprise: generate continuous management cycles, mark the production lines of the enterprise as management objects, mark the product types covered by the management objects as the production objects of the management objects, and form the production set of the management objects with all the production objects of the management objects. At the beginning of the management cycle, the production parameters of the production set of the management objects are obtained. The production parameters include efficiency data and defect data. The efficiency data is the ratio of the production quantity of the production objects completed by the management object in the previous management cycle to the corresponding production time. The defect data is the defect rate of the production tasks of the production objects performed by the management object in the previous management cycle. Arrange all elements of the production set in descending order according to the efficiency data to obtain the efficiency sequence of the management object, and arrange all elements of the production set in descending order according to the defect data to obtain the defect sequence of the management object. Sequence, mark the absolute value of the difference between the serial number of the production object in the efficiency sequence and the serial number in the defect sequence as the unified value of the production object, sum and average the unified values ​​of all production objects in the production set of the management object to obtain the unified coefficient of the management object, call the unified threshold through the database, and compare the unified coefficient of the management object with the unified threshold: if the unified coefficient is less than the unified threshold, it is determined that the management object has a unified production feature, and the corresponding management object is marked as a unified object; if the unified coefficient is greater than or equal to the unified threshold, it is determined that the management object does not have a unified feature, and the corresponding management object is marked as a divergent object; perform production parameter statistics based on the recent production status of the production line, and then calculate the unified coefficient of the management object and mark the management object differently according to the unified coefficient, so as to provide data support for the allocation of product types in production lines and production orders.

[0019] The unified analysis module is used to perform priority production analysis on the unified objects of the enterprise: the priority coefficient YX of the production objects in the production set of the unified object is obtained through the formula YX=k1×XL-k2×CC×100, where k1 and k2 are both proportional coefficients, and k1>k2>1, and XL and CC are the values ​​of the efficiency data and defective data of the production object respectively; all production objects in the production set of the unified object are arranged in descending order according to the value of the priority coefficient YX to obtain a unified sequence of the unified object, and the top L1 production objects in the unified sequence are intercepted and marked as the priority objects of the unified object. All the priority objects of the unified object constitute the priority set of the unified object; the unified objects with unified production characteristics are preferentially assigned to product types, and the priority objects of the unified objects are screened by the priority coefficient. The priority objects are the product types that are suitable for priority matching with the unified objects, thereby improving the degree of fit between the production line and the product type.

[0020] The production management module is used to manage and analyze the production plan of the enterprise: at the start of each production day, the task data is retrieved. The task data includes the product type LXi and the product quantity SLi, i=1, 2, ..., n, where n is a positive integer. The unified object in the priority set containing the product type LXi is marked as the assigned production line. The assigned production line with the largest priority coefficient YX value corresponding to the product type LXi is marked as the matching production line of the product type LXi. The product of the efficiency data of the matching production line and the estimated production time of the production day is marked as the estimated production value of the matching production line. The estimated production value and the product type LX are combined. i is compared with the product quantity SLi corresponding to it: if the expected production value is less than the production quantity SLi, the difference between the production quantity SLi and the expected production value is marked as the supplementary quantity BCi, and the supplementary quantity BCi is used to replace the product quantity SLi; if the expected production value is greater than or equal to the production quantity SLi, the product quantity SLi corresponding to the product type LXi is marked as zero; at the starting production time of each production day, the production task allocation of the unified production line is carried out, and the product quantity after the allocation is completed is updated in combination with the product quantity corresponding to the product type, providing data support for the task allocation process of the divergent production lines.

[0021] The supplementary allocation module is used to perform production line allocation analysis on product types that have not been fully allocated: mark the unified objects that have not been matched with the product type LXi as divergent objects, mark the product type LXi with a product quantity SLi not zero as analysis type FXi, mark the divergent objects containing the analysis type FXi in the production set as preliminary screening objects, sum and average the efficiency data corresponding to the analysis type FXi in the production parameters of all preliminary screening objects to obtain the efficiency performance value, mark the ratio of the product quantity SLi of the analysis type FXi to the efficiency performance value as the duration performance value, retrieve the duration performance threshold through the database, and compare the duration performance value with the duration performance threshold: if the duration performance value is less than the duration performance threshold, the analysis type FXi in the residual sequence of the preliminary screening object is added. The serial number is marked as the defective matching value of the initial screening object, and the initial screening object with the smallest defective matching value is marked as the matching production line of the analysis type FXi; if the duration performance value is greater than or equal to the duration performance threshold, the serial number of the analysis type FXi in the efficiency sequence of the initial screening object is marked as the efficiency matching value of the initial screening object, and the initial screening object with the smallest efficiency matching value is marked as the matching production line of the analysis type FXi; the initial task allocation plan is composed of all product types LXi and the corresponding matching production lines, and the initial task allocation plan is sent to the mobile terminal of the manager through the order management platform; the production line allocation basis is analyzed according to the duration performance value of the analysis type, and when the production pressure is high, the production efficiency is given priority to ensure that the order is completed on time, and when the production pressure is low, the qualified rate is given priority to reduce raw material consumption.

[0022] Embodiment 2: Figure 2 As shown, a production order management method based on artificial intelligence includes the following steps: Step 1: Conduct management analysis on the enterprise's production line: generate a continuous management cycle, mark the enterprise's production line as a management object, obtain the unified coefficient of the management object, and mark the management object as a unified object or a divergent object through the unified coefficient; Step 2: Perform a priority production analysis on the unified object of the enterprise and obtain the priority coefficient YX of all production objects in the production set of the unified object, and mark the priority objects of the unified object by the priority coefficient YX; Step 3: Manage and analyze the production plan of the enterprise: retrieve the task data at the start of each production day, mark the product of the efficiency data of the matching production line and the estimated production time of the production day as the estimated production value of the matching production line, and mark the product quantity SLi corresponding to the product type LXi by the estimated production value; Step 4: Perform production line allocation analysis on the product types that have not been allocated and obtain an initial task allocation plan, which is then sent to the manager's mobile terminal via the order management platform.

[0023] A production order management method and system based on artificial intelligence, when working, generates a continuous management cycle, marks the production line of an enterprise as a management object, obtains the unified coefficient of the management object, and marks the management object as a unified object or a divergent object through the unified coefficient; performs priority production analysis on the unified object of the enterprise and obtains the priority coefficient YX of all production objects in the production set of the unified object, and marks the priority object of the unified object through the priority coefficient YX; retrieves task data at the starting production time of each production day, marks the product of the efficiency data of the matching production line and the estimated production time of the production day as the estimated production value of the matching production line, and marks the product quantity SLi corresponding to the product type LXi through the estimated production value; performs production line allocation analysis on the product type that has not completed the allocation and obtains the initial task allocation plan, and sends the initial task allocation plan to the mobile phone terminal of the manager through the order management platform.

[0024] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0025] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; for example: formula YX=k1×XL-k2×CC×100; technicians in this field collect multiple groups of sample data and set corresponding priority coefficients for each group of sample data; substitute the set priority coefficients and the collected sample data into the formula, any two formulas constitute a set of two-variable linear equations, screen the calculated coefficients and take the average, and obtain the values ​​of k1 and k2 as 5.34 and 3.75 respectively; The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the technical personnel in this field preliminarily set the corresponding priority coefficient for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the priority coefficient is proportional to the value of the efficiency data.

[0026] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0027] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A production order management system based on artificial intelligence, characterized in that: It includes an order management platform, which is communicatively connected to a production line management module, a unified analysis module, a production management module, a supplementary distribution module and a database; The production line management module is used to manage and analyze the production line of the enterprise: generate a continuous management cycle, mark the production line of the enterprise as a management object, mark the product type covered by the management object as the production object of the management object, and form a production set of the management object by all the production objects of the management object. At the beginning of the management cycle, the production parameters of the production set of the management object are obtained, and the management object is marked as a unified object or a divergent object according to the production parameters; The unified analysis module is used to perform priority production analysis on the unified objects of the enterprise and mark the priority objects of the unified objects, and all the priority objects of the unified objects constitute a priority set of the unified objects; The production management module is used to manage and analyze the production plan of the enterprise: at the start of each production day, task data is retrieved, the task data includes product type LXi and product quantity SLi, i=1, 2, ..., n, n is a positive integer, the unified object containing product type LXi in the priority set is marked as the allocation production line, the allocation production line with the largest value of the priority coefficient YX corresponding to the product type LXi is marked as the matching production line of the product type LXi, the product of the efficiency data of the matching production line and the estimated production time of the production day is marked as the estimated production value of the matching production line, and the product quantity SLi of the product type LXi is updated according to the estimated production value; The supplementary allocation module is used to perform production line allocation analysis on the product types that have not been allocated and obtain an initial task allocation plan, and send the initial task allocation plan to the mobile phone terminal of the manager through the order management platform.

2. The production order management system based on artificial intelligence according to claim 1 is characterized in that: The specific process of marking a management object as a unified object or a divergent object includes: the production parameters include efficiency data and defect data, the efficiency data is the ratio of the production quantity of the production object completed by the management object in the previous management cycle to the corresponding production time, and the defect data is the defect rate of the production task of the production object performed by the management object in the previous management cycle; the production parameters are processed to obtain the unified coefficient of the management object, the unified threshold is retrieved through the database, and the unified coefficient of the management object is compared with the unified threshold: if the unified coefficient is less than the unified threshold, it is determined that the management object has unified production characteristics, and the corresponding management object is marked as a unified object; if the unified coefficient is greater than or equal to the unified threshold, it is determined that the management object does not have unified characteristics, and the corresponding management object is marked as a divergent object.

3. The production order management system based on artificial intelligence according to claim 2 is characterized in that: The process of obtaining the unified coefficient of the management object includes: arranging all elements of the production set according to the efficiency data from large to small to obtain the efficiency sequence of the management object, arranging all elements of the production set according to the defect data from small to large to obtain the defect sequence of the management object, marking the absolute value of the difference between the sequence number of the production object in the efficiency sequence and the sequence number in the defect sequence as the unified value of the production object, and summing and averaging the unified values ​​of all production objects in the production set of the management object to obtain the unified coefficient of the management object.

4. The production order management system based on artificial intelligence according to claim 3 is characterized in that: The specific process of marking the priority objects of the unified object includes: calculating the production parameters to obtain the priority coefficient YX of the production objects in the production set of the unified object; arranging all the production objects in the production set of the unified object in descending order according to the value of the priority coefficient YX to obtain a unified sequence of the unified object, intercepting the top L1 production objects in the unified sequence and marking them as the priority objects of the unified object.

5. The artificial intelligence-based production order management system according to claim 4, characterized in that: The specific process of updating the product quantity SLi of product type LXi includes: comparing the expected production value with the product quantity SLi corresponding to product type LXi: if the expected production value is less than the production quantity SLi, then marking the difference between the production quantity SLi and the expected production value as the supplementary quantity BCi, and replacing the product quantity SLi with the supplementary quantity BCi; if the expected production value is greater than or equal to the production quantity SLi, then marking the product quantity SLi corresponding to product type LXi as zero.

6. The artificial intelligence-based production order management system according to claim 5, characterized in that: The specific process of the supplementary allocation module to perform production line allocation analysis on the product types that have not completed allocation includes: marking the unified objects that have not completed matching with the product type LXi as divergent objects, marking the product type LXi with a product quantity SLi not zero as the analysis type FXi, marking the divergent objects containing the analysis type FXi in the production set as preliminary screening objects, summing up and averaging the efficiency data corresponding to the analysis type FXi in the production parameters of all preliminary screening objects to obtain the efficiency performance value, marking the ratio of the product quantity SLi of the analysis type FXi to the efficiency performance value as the duration performance value, marking the matching production lines of the analysis type FXi by the duration performance value; and forming an initial task allocation plan by all product types LXi and the corresponding matching production lines.

7. The artificial intelligence-based production order management system according to claim 6, characterized in that: The specific process of marking the matching production line of the analysis type FXi includes: retrieving the duration performance threshold through the database, and comparing the duration performance value with the duration performance threshold: if the duration performance value is less than the duration performance threshold, then the serial number of the analysis type FXi in the residual sequence of the initial screening object is marked as the residual matching value of the initial screening object, and the initial screening object with the smallest residual matching value is marked as the matching production line of the analysis type FXi; if the duration performance value is greater than or equal to the duration performance threshold, then the serial number of the analysis type FXi in the efficiency sequence of the initial screening object is marked as the efficiency matching value of the initial screening object, and the initial screening object with the smallest efficiency matching value is marked as the matching production line of the analysis type FXi.

8. A production order management method based on artificial intelligence, characterized in that: The following steps are involved: Step 1: Conduct management analysis on the enterprise's production line: generate a continuous management cycle, mark the enterprise's production line as a management object, obtain the unified coefficient of the management object, and mark the management object as a unified object or a divergent object through the unified coefficient; Step 2: Perform a priority production analysis on the unified object of the enterprise and obtain the priority coefficient YX of all production objects in the production set of the unified object, and mark the priority objects of the unified object by the priority coefficient YX; Step 3: Manage and analyze the production plan of the enterprise: retrieve the task data at the start of each production day, mark the product of the efficiency data of the matching production line and the estimated production time of the production day as the estimated production value of the matching production line, and mark the product quantity SLi corresponding to the product type LXi by the estimated production value; Step 4: Perform production line allocation analysis on the product types that have not been allocated and obtain an initial task allocation plan, which is then sent to the manager's mobile terminal via the order management platform.

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