An AI-based industrial automation production scheduling system and method

Abstract

This invention discloses an artificial intelligence-based industrial automation production scheduling system and method, belonging to the field of automated production scheduling technology. The invention includes the following steps: obtaining historical production records of each production line from a database and analyzing the maximum capacity of each production line; obtaining real-time production records of each production line and, combined with the maximum capacity of each production line, analyzing the expected completion date and surplus characteristics of each production line; analyzing whether urgent orders can be placed in place based on the products and quantities of urgent orders, combined with the surplus characteristics of each production line in the first production line set; if urgent orders can be placed in place, analyzing the placed orders and their allocation characteristics based on the urgent delivery date of the urgent orders, combined with the surplus characteristics of each production line in the first production line set. This invention achieves accurate assessment of production line capacity, improves the accuracy and real-time performance of scheduling, and flexibly responds to urgent production needs, shortens delivery cycles, and improves customer satisfaction and market competitiveness.

Description

Technical Field

[0001] This invention belongs to the field of automated production scheduling technology, specifically an industrial automated production scheduling system and method based on artificial intelligence. Background Technology

[0002] With the development of society and the improvement of technology, the degree of automation in industrial production is getting higher and higher, and more and more automated equipment is being applied to the production process, making the process flow more complete. However, more problems have also arisen, the most prominent of which is the production scheduling problem.

[0003] Traditional scheduling methods rely on manual planning and adjustment of orders. Due to the increase in order volume from automated production, customers may request expedited orders. Manual scheduling is used to implement rush orders. However, manual scheduling makes it difficult to accurately control production demand, resulting in delayed delivery of rushed orders, reducing customer trust and affecting the company's development.

[0004] Therefore, there is an urgent need for an artificial intelligence-based industrial automation production scheduling system to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide an artificial intelligence-based industrial automation production scheduling system to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] An artificial intelligence-based industrial automation production scheduling method, comprising the following steps:

[0008] S1. Obtain historical production records for each production line from the database, filter the historical production records to obtain a first set of records, and classify the first set of records according to the product to obtain several product production sets; analyze the maximum capacity of each production line based on the product production sets.

[0009] S2. Obtain real-time production records of each production line from the database, match the order information of the corresponding orders for each production line, and analyze the expected completion date and surplus characteristics of each production line in combination with the maximum capacity of each production line.

[0010] S3. Based on the products of the rush orders, select the first production line set; based on the surplus characteristics of each production line in the first production line set and the rush order quantity, analyze whether the rush orders can be inserted.

[0011] S4. If an urgent order cannot be inserted, send an "Installation Not Allowed" message; if an urgent order can be inserted, calculate the urgent production volume of each production line based on the urgent delivery date of the urgent order and the surplus characteristics of each production line in the first production line set, and analyze the inserted order and the allocation characteristics of each inserted order to schedule production.

[0012] According to the above technical solution, step S1 includes:

[0013] S1-1. Each time the production line runs, a historical production record is generated and stored in the database; the historical production record includes the product, production duration, production quantity, and average load of the main equipment;

[0014] Based on the average load of the main equipment, a load threshold is set, and historical production records in which the average load of the main equipment is less than or equal to the load threshold are filtered out to form the first record set.

[0015] S1-2. Based on the product type, divide the historical production records in the first record set into different product production sets;

[0016] S1-3. Take a certain product production set as the target set; extract any historical production record from the target set, calculate the ratio of production quantity to production time in the historical production record, and take it as the capacity of the historical production record; compare the capacity of all historical production records of the same production line in the target set, select the maximum value, and take it as the maximum capacity of the production line to produce the corresponding product.

[0017] By screening historical production records and conducting capacity analysis, outlier data can be effectively eliminated, ensuring the reliability and accuracy of the data. Using the maximum capacity of the production line as a reference for production capacity provides scientific data support for subsequent scheduling decisions, helping to improve the accuracy and rationality of scheduling.

[0018] According to the above technical solution, step S2 includes:

[0019] S2-1. When the production line is running, it generates real-time production records and stores them in the database; the real-time production records include the order number, production duration, and production quantity.

[0020] S2-2. Match the order information of the corresponding order on the production line according to the order number. The order information includes the order number, product, order quantity, completed quantity, and agreed delivery date.

[0021] S2-3. Based on the real-time production records of a certain production line, calculate the ratio of the production time to the production quantity, which is taken as the real-time output value of the production line; calculate the order quantity of the corresponding order for the production line minus the completed quantity to obtain the remaining production quantity of the production line; calculate the ratio of the remaining production quantity of the production line to the real-time output value to obtain the remaining production time of the production line; add the timestamp corresponding to the real-time production record of the production line to the remaining production time of the production line to obtain the expected completion date of the production line.

[0022] Obtain the time length from the estimated completion date of the production line to the agreed delivery date of the order, as the surplus time of the production line; extract the maximum production capacity of the production line to produce the corresponding product, as the surplus capacity of the production line; calculate the value of multiplying the surplus time of the production line by the surplus capacity to obtain the surplus production volume of the production line; use the surplus capacity and surplus production volume of the production line as the surplus characteristics of the production line.

[0023] Real-time monitoring of production line status, calculation of estimated completion dates and surplus capacity, and timely warnings of potential production delays. By analyzing real-time output and surplus capacity of the production line, the system dynamically assesses the production line's load status, providing accurate data support for determining the feasibility of order insertion and improving the flexibility and real-time nature of production scheduling.

[0024] According to the above technical solution, step S3 includes:

[0025] S3-1. Obtain expedited orders from the database, wherein the expedited orders include products, expedited quantities, and expedited delivery dates; based on the timestamps corresponding to the expedited orders, obtain the real-time production records of the production line and their corresponding surplus characteristics from the database;

[0026] S3-2. Assign production lines whose products in the real-time production records are the same as those in the expedited orders, and whose agreed delivery dates are after the expedited delivery dates, to the first production line set. Calculate the sum of the surplus production quantities of all production lines in the first production line set based on their surplus production quantities, and compare it with the expedited quantity of the expedited order. If the sum of the surplus production quantities of all production lines in the first production line set is greater than the expedited quantity of the expedited order, the expedited order can be placed; if the sum of the surplus production quantities of all production lines in the first production line set is less than or equal to the expedited quantity of the expedited order, the expedited order cannot be placed.

[0027] By combining the expedited volume and delivery date of rush orders, a precise set of production lines meeting the criteria is selected, and their suitability for order fulfillment is assessed. This step enables the rational allocation of production capacity in emergency situations, rapidly responding to urgent customer needs, effectively improving the flexibility of production planning, and reducing production conflicts and resource waste caused by improper scheduling.

[0028] According to the above technical solution, step S4 includes:

[0029] S4-1. If an urgent order cannot be inserted, send an "Installation Not Allowed" message; if an urgent order can be inserted, obtain the time length from the timestamp of the urgent order to the urgent delivery date of the urgent order, and use it as the urgent production time; use the surplus production volume of each production line in the first production line set as the urgent production volume of each production line.

[0030] S4-2. Sort the production lines in the first production line set from largest to smallest according to the size of their surplus capacity. Mark the production lines in turn according to the sorted production lines and calculate the sum of the rush production quantities of all marked production lines. Stop marking when the sum of the rush production quantities of all marked production lines is greater than the rush quantity of the rush order.

[0031] The order corresponding to the marked production line is designated as the inserted order; the expedited production time of the marked production line is designated as the allocated production time of the inserted order; the expedited production quantity of the marked production line is designated as the allocated production quantity of the inserted order; and the allocated production time and allocated production quantity are designated as the allocation characteristics of the inserted order.

[0032] S4-3. When producing expedited orders for inserted orders, the excess capacity of the production line corresponding to the inserted order is used as the capacity standard, and the allocated production time of the inserted order is used as the time standard. The product of the capacity standard and the time standard is calculated as the standard production quantity of the inserted order. The difference between the standard production quantity of the inserted order and the allocated production quantity of the inserted order is calculated and recorded as the first production difference of the inserted order.

[0033] If the first production difference of all inserted orders is greater than or equal to zero, then the first production difference will be included in the completed quantity of the corresponding inserted order.

[0034] If the first production difference of an inserted order is less than zero, the inserted order is marked as an insufficient order. The absolute value of the first production difference of the insufficient order is taken, and the sum of the absolute values ​​of the first production difference of all insufficient orders is calculated as the insufficient quantity.

[0035] Extract inserted orders with a first production difference greater than or equal to zero to form a sufficient order set; sort the inserted orders in the sufficient order set from largest to smallest according to the surplus capacity of the production line corresponding to the inserted orders; extract the inserted orders in the sufficient order set in order of sorting; calculate the sum of the first production difference corresponding to the extracted inserted orders, and record it as the compensation amount; stop extraction when the compensation amount is greater than or equal to the shortage amount;

[0036] The products produced from the extracted inserted orders will be used as a supplement to the rush orders, with the supplement quantity being the first production difference corresponding to the inserted orders;

[0037] The products produced from the extracted inserted orders are used to supplement the expedited orders. The products produced after the expedited orders are delivered are used to supplement the extracted inserted orders, with the supplement quantity being the quantity corresponding to the insufficient orders.

[0038] By allocating tasks and adjusting order insertions, we ensure the timely delivery of urgent orders and avoid disrupting the normal delivery of existing orders. Based on the ranking of surplus capacity of each production line, we achieve optimal allocation of urgent production tasks, maximize the utilization rate of production line resources, improve overall production efficiency, and optimize the balance and stability of scheduling.

[0039] An artificial intelligence-based industrial automation production scheduling system includes a data acquisition module, a data analysis module, a scheduling decision module, and a scheduling execution module.

[0040] The data acquisition module collects and stores historical and real-time production records for each production line, providing basic data support for subsequent data analysis and scheduling decision modules. The data analysis module filters and analyzes historical and real-time production records, calculating the capacity, expected completion date, and surplus characteristics of each production line. The scheduling decision module filters the production line set based on the product, quantity, and delivery date of urgent orders, and, combined with the surplus characteristics of each production line in the set, determines the feasibility of inserting urgent orders and provides feedback on the results. The scheduling execution module, when urgent orders can be inserted, analyzes the inserted orders and their allocation characteristics based on the urgent delivery date and the surplus characteristics of each production line in the first production line set, ensuring timely delivery of urgent orders and reducing production conflicts.

[0041] According to the above technical solution, the data acquisition module includes a historical data unit and a real-time data unit;

[0042] The historical data unit is used to collect and store historical production records for each production line, including products, production duration, production volume, and average load of main equipment; the real-time data unit is used to collect and store real-time production records for each production line, including order number, production duration, and production volume.

[0043] According to the above technical solution, the data analysis module includes a capacity calculation unit and a surplus analysis unit;

[0044] The capacity calculation unit is used to filter product production sets based on the historical production records of each production line, and to calculate and analyze the product production sets to obtain the maximum production capacity of the corresponding products of the production line; the surplus analysis unit is used to calculate the expected completion date, surplus duration and surplus production volume of each production line based on real-time production records and historical production records.

[0045] According to the above technical solution, the scheduling decision module includes a production line screening unit and an order insertion evaluation unit;

[0046] The production line screening unit is used to select a first set of production lines based on the products and delivery dates of expedited orders, combined with real-time production records and surplus characteristics. The order insertion evaluation unit is used to analyze the surplus production of each production line in the first set of production lines, analyze the expedited quantity of expedited orders, determine whether expedited orders can be inserted, and send the judgment result.

[0047] According to the above technical solution, the scheduling execution module includes a task allocation unit and an order insertion adjustment unit;

[0048] The task allocation unit is used to sort the production lines according to their surplus capacity, calculate the expedited production volume that each production line can execute, mark the production lines to be inserted according to the sorting, and match the inserted orders and their allocated production volumes. The order insertion adjustment unit is used to adjust the production of the inserted orders and their allocated production volumes to ensure that the insertion of orders will not affect the normal delivery of orders.

[0049] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0050] This invention achieves precise assessment of production line capacity by screening and analyzing historical production records and combining them with real-time production data to dynamically adjust production line plans, thereby improving the accuracy and real-time nature of scheduling. Simultaneously, through an expedited order insertion evaluation mechanism, this invention assesses the feasibility of inserting expedited orders in real time based on the expedited quantity, delivery date, and production line surplus characteristics, flexibly responding to urgent production needs, shortening delivery cycles, and improving customer satisfaction and market competitiveness. Secondly, by prioritizing and allocating surplus capacity across production lines, this invention achieves optimal allocation of expedited orders, maximizing production line resource utilization and optimizing the balance and stability of scheduling. Furthermore, by monitoring production line status in real time, providing timely warnings of potential production delays, and combining intelligent scheduling with autonomous optimization strategies, this invention effectively reduces scheduling conflicts and risks, improving the stability and practicality of the scheduling system. Attached Figure Description

[0051] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0052] Figure 1 This is a flowchart illustrating an artificial intelligence-based industrial automation production scheduling method according to the present invention.

[0053] Figure 2 This is a schematic diagram of the structure of an artificial intelligence-based industrial automation production scheduling system according to the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by visitors of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figure 1 An artificial intelligence-based industrial automation production scheduling method includes the following steps:

[0056] S1. Obtain historical production records for each production line from the database, filter the historical production records to obtain a first set of records, and classify the first set of records according to the product to obtain several product production sets; analyze the maximum capacity of each production line based on the product production sets.

[0057] According to the above technical solution, step S1 includes:

[0058] S1-1. Each time the production line runs, a historical production record is generated and stored in the database; the historical production record includes the product, production duration, production quantity, and average load of the main equipment;

[0059] Based on the average load of the main equipment, a load threshold is set, and historical production records in which the average load of the main equipment is less than or equal to the load threshold are filtered out to form the first record set.

[0060] S1-2. Based on the product type, divide the historical production records in the first record set into different product production sets;

[0061] S1-3. Take a certain product production set as the target set; extract any historical production record from the target set, calculate the ratio of production quantity to production time in the historical production record, and take it as the capacity of the historical production record; compare the capacity of all historical production records of the same production line in the target set, select the maximum value, and take it as the maximum capacity of the production line to produce the corresponding product.

[0062] By screening historical production records and conducting capacity analysis, outlier data can be effectively eliminated, ensuring the reliability and accuracy of the data. Using the maximum capacity of the production line as a reference for production capacity provides scientific data support for subsequent scheduling decisions, helping to improve the accuracy and rationality of scheduling.

[0063] S2. Obtain real-time production records of each production line from the database, match the order information of the corresponding orders for each production line, and analyze the expected completion date and surplus characteristics of each production line in combination with the maximum capacity of each production line.

[0064] According to the above technical solution, step S2 includes:

[0065] S2-1. When the production line is running, it generates real-time production records and stores them in the database; the real-time production records include the order number, production duration, and production quantity.

[0066] S2-2. Match the order information of the corresponding order on the production line according to the order number. The order information includes the order number, product, order quantity, completed quantity, and agreed delivery date.

[0067] S2-3. Based on the real-time production records of a certain production line, calculate the ratio of the production time to the production quantity, which is taken as the real-time output value of the production line; calculate the order quantity of the corresponding order for the production line minus the completed quantity to obtain the remaining production quantity of the production line; calculate the ratio of the remaining production quantity of the production line to the real-time output value to obtain the remaining production time of the production line; add the timestamp corresponding to the real-time production record of the production line to the remaining production time of the production line to obtain the expected completion date of the production line.

[0068] Obtain the time length from the estimated completion date of the production line to the agreed delivery date of the order, as the surplus time of the production line; extract the maximum production capacity of the production line to produce the corresponding product, as the surplus capacity of the production line; calculate the value of multiplying the surplus time of the production line by the surplus capacity to obtain the surplus production volume of the production line; use the surplus capacity and surplus production volume of the production line as the surplus characteristics of the production line.

[0069] Real-time monitoring of production line status, calculation of estimated completion dates and surplus capacity, and timely warnings of potential production delays. By analyzing real-time output and surplus capacity of the production line, the system dynamically assesses the production line's load status, providing accurate data support for determining the feasibility of order insertion and improving the flexibility and real-time nature of production scheduling.

[0070] S3. Based on the products of the rush orders, select the first production line set; based on the surplus characteristics of each production line in the first production line set and the rush order quantity, analyze whether the rush orders can be inserted.

[0071] According to the above technical solution, step S3 includes:

[0072] S3-1. Obtain expedited orders from the database, wherein the expedited orders include products, expedited quantities, and expedited delivery dates; based on the timestamps corresponding to the expedited orders, obtain the real-time production records of the production line and their corresponding surplus characteristics from the database;

[0073] S3-2. Assign production lines whose products in the real-time production records are the same as those in the expedited orders, and whose agreed delivery dates are after the expedited delivery dates, to the first production line set. Calculate the sum of the surplus production quantities of all production lines in the first production line set based on their surplus production quantities, and compare it with the expedited quantity of the expedited order. If the sum of the surplus production quantities of all production lines in the first production line set is greater than the expedited quantity of the expedited order, the expedited order can be placed; if the sum of the surplus production quantities of all production lines in the first production line set is less than or equal to the expedited quantity of the expedited order, the expedited order cannot be placed.

[0074] By combining the expedited volume and delivery date of rush orders, a precise set of production lines meeting the criteria is selected, and their suitability for order insertion is assessed. This step enables the rational allocation of production capacity in emergency situations, quickly responding to customers' urgent needs, effectively improving the flexibility of production planning, and reducing production conflicts and resource waste caused by improper scheduling.

[0075] For example:

[0076] The expedited order is for product A, with an expedited quantity of 300, and an expedited delivery date of 8 days later.

[0077] In the real-time production record of production line 1, product A has a surplus production quantity of 200 units, and the agreed delivery date for order 1 is 10 days later; in the real-time production record of production line 2, product A has a surplus production quantity of 150 units, and the agreed delivery date for order 2 is 10 days later; in the real-time production record of production line 3, product B has a surplus production quantity of 250 units, and the agreed delivery date for order 3 is 9 days later.

[0078] Based on the products of the rush orders, production line 3 is excluded; based on the rush delivery date, the agreed delivery date of order 1 is after the rush delivery date, and the agreed delivery date of order 2 is filtered to be after the rush delivery date, so the first production line set is production line 1 and production line 2;

[0079] The sum of the surplus production of all production lines in the first production line set is 200 + 150 = 350. Since the sum of the surplus production of all production lines in the first production line set, 350, is greater than the expedited quantity of the expedited order, 300, the expedited order can be placed.

[0080] S4. If an urgent order cannot be placed, send an order placement failure message; if an urgent order can be placed, calculate the urgent production volume of each production line based on the urgent delivery date of the urgent order and the surplus characteristics of each production line in the first production line set, and analyze the placement order and the allocation characteristics of each placement order to schedule production.

[0081] According to the above technical solution, step S4 includes:

[0082] S4-1. If an urgent order cannot be inserted, send an "Installation Not Allowed" message; if an urgent order can be inserted, obtain the time length from the timestamp of the urgent order to the urgent delivery date of the urgent order, and use it as the urgent production time; use the surplus production volume of each production line in the first production line set as the urgent production volume of each production line.

[0083] S4-2. Sort the production lines in the first production line set from largest to smallest according to the size of their surplus capacity. Mark the production lines in turn according to the sorted production lines and calculate the sum of the rush production quantities of all marked production lines. Stop marking when the sum of the rush production quantities of all marked production lines is greater than the rush quantity of the rush order.

[0084] The order corresponding to the marked production line is designated as the inserted order; the expedited production time of the marked production line is designated as the allocated production time of the inserted order; the expedited production quantity of the marked production line is designated as the allocated production quantity of the inserted order; and the allocated production time and allocated production quantity are designated as the allocation characteristics of the inserted order.

[0085] S4-3. When producing expedited orders for inserted orders, the excess capacity of the production line corresponding to the inserted order is used as the capacity standard, and the allocated production time of the inserted order is used as the time standard. The product of the capacity standard and the time standard is calculated as the standard production quantity of the inserted order. The difference between the standard production quantity of the inserted order and the allocated production quantity of the inserted order is calculated and recorded as the first production difference of the inserted order.

[0086] If the first production difference of all inserted orders is greater than or equal to zero, then the first production difference will be included in the completed quantity of the corresponding inserted order.

[0087] If the first production difference of an inserted order is less than zero, the inserted order is marked as an insufficient order. The absolute value of the first production difference of the insufficient order is taken, and the sum of the absolute values ​​of the first production difference of all insufficient orders is calculated as the insufficient quantity.

[0088] Extract inserted orders with a first production difference greater than or equal to zero to form a sufficient order set; sort the inserted orders in the sufficient order set from largest to smallest according to the surplus capacity of the production line corresponding to the inserted orders; extract the inserted orders in the sufficient order set in order of sorting; calculate the sum of the first production difference corresponding to the extracted inserted orders, and record it as the compensation amount; stop extraction when the compensation amount is greater than or equal to the shortage amount;

[0089] The products produced from the extracted inserted orders will be used as a supplement to the rush orders, with the supplement quantity being the first production difference corresponding to the inserted orders;

[0090] The products produced from the extracted inserted orders are used to supplement the rush orders. The products produced after the rush orders are delivered are used to supplement the extracted inserted orders. The supplement quantity is the quantity of the shortage corresponding to the shortage order.

[0091] By allocating tasks and adjusting order insertions, we ensure the timely delivery of urgent orders and avoid disrupting the normal delivery of existing orders. Based on the ranking of surplus capacity of each production line, we achieve optimal allocation of urgent production tasks, maximize the utilization rate of production line resources, improve overall production efficiency, and optimize the balance and stability of scheduling.

[0092] Please see Figure 2 An artificial intelligence-based industrial automation production scheduling system, comprising a data acquisition module, a data analysis module, a scheduling decision module, and a scheduling execution module;

[0093] The data acquisition module collects and stores historical and real-time production records for each production line, providing basic data support for subsequent data analysis and scheduling decision modules. The data analysis module filters and analyzes historical and real-time production records, calculating the capacity, expected completion date, and surplus characteristics of each production line. The scheduling decision module filters the production line set based on the product, quantity, and delivery date of urgent orders, and, combined with the surplus characteristics of each production line in the set, determines the feasibility of inserting urgent orders and provides feedback on the results. The scheduling execution module, when urgent orders can be inserted, analyzes the inserted orders and their allocation characteristics based on the urgent delivery date and the surplus characteristics of each production line in the first production line set, ensuring timely delivery of urgent orders and reducing production conflicts.

[0094] According to the above technical solution, the data acquisition module includes a historical data unit and a real-time data unit;

[0095] The historical data unit is used to collect and store historical production records for each production line, including products, production duration, production volume, and average load of main equipment; the real-time data unit is used to collect and store real-time production records for each production line, including order number, production duration, and production volume.

[0096] According to the above technical solution, the data analysis module includes a capacity calculation unit and a surplus analysis unit;

[0097] The capacity calculation unit is used to filter product production sets based on the historical production records of each production line, and to calculate and analyze the product production sets to obtain the maximum production capacity of the corresponding products of the production line; the surplus analysis unit is used to calculate the expected completion date, surplus duration and surplus production volume of each production line based on real-time production records and historical production records.

[0098] According to the above technical solution, the scheduling decision module includes a production line screening unit and an order insertion evaluation unit;

[0099] The production line screening unit is used to select a first set of production lines based on the products and delivery dates of expedited orders, combined with real-time production records and surplus characteristics. The order insertion evaluation unit is used to analyze the surplus production of each production line in the first set of production lines, analyze the expedited quantity of expedited orders, determine whether expedited orders can be inserted, and send the judgment result.

[0100] According to the above technical solution, the scheduling execution module includes a task allocation unit and an order insertion adjustment unit;

[0101] The task allocation unit is used to sort the production lines according to their surplus capacity, calculate the expedited production volume that each production line can execute, mark the production lines to be inserted according to the sorting, and match the inserted orders and their allocated production volumes. The order insertion adjustment unit is used to adjust the production of the inserted orders and their allocated production volumes to ensure that the insertion of orders will not affect the normal delivery of orders.

[0102] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0103] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An artificial intelligence-based industrial automation production scheduling method, characterized by: The method comprises the following steps: S1, obtaining historical production records of each production line from the database, screening the historical production records, obtaining a first record set, and classifying the first record set according to products to obtain a plurality of product production sets; analyzing the maximum production capacity of each production line according to the product production set; S2, obtaining real-time production records of each production line from the database, matching order information of orders corresponding to each production line, combining the maximum production capacity of each production line, analyzing the predicted completion date and surplus characteristics of each production line; According to the real-time production record of a certain production line, the ratio of the production time to the production amount is calculated as the real-time production value of the production line; the value of the order quantity corresponding to the order of the production line minus the completed quantity is obtained as the remaining production quantity of the production line; the ratio of the remaining production quantity to the real-time production value of the production line is calculated as the remaining production time of the production line; the timestamp corresponding to the real-time production record of the production line is added to the remaining production time of the production line to obtain the predicted completion date of the production line; Obtain the time length from the predicted completion date of the production line to the agreed delivery date of the order as the surplus time length of the production line; extract the maximum production capacity of the product produced by the production line as the surplus production capacity of the production line; calculate the value of the surplus time length multiplied by the surplus production capacity of the production line to obtain the surplus production quantity of the production line; the surplus production capacity and the surplus production quantity of the production line are taken as the surplus characteristics of the production line; S3, according to the product of the urgent order, the first production line set is screened out; according to the surplus characteristics of each production line in the first production line set and the urgent quantity of the urgent order, whether the urgent order can be inserted is analyzed; The production lines with the same product in the real-time production record and the agreed delivery date of the corresponding order after the urgent delivery date are divided into the first production line set; S4, if the urgent order cannot be inserted, send the information that the order cannot be inserted; if the urgent order can be inserted, calculate the urgent production quantity of each production line according to the urgent delivery date of the urgent order combined with the surplus characteristics of each production line in the first production line set, and analyze the distribution characteristics of the inserted order and each inserted order to schedule production; The step S4 comprises: S4-1, if the urgent order cannot be inserted, send the information that the order cannot be inserted; if the urgent order can be inserted, obtain the time length from the timestamp corresponding to the urgent order to the urgent delivery date of the urgent order as the urgent production time length; take the surplus production quantity of each production line in the first production line set as the urgent production quantity of each production line; S4-2, according to the size of the surplus production capacity of each production line in the first production line set, sort the production lines of the first production line set from large to small; according to the sorted production lines, mark the production lines in turn, and count the sum of the urgent production quantities of all marked production lines, and stop marking when the sum of the urgent production quantities of all marked production lines is greater than the urgent quantity of the urgent order; The order corresponding to the marked production line is taken as the inserted order; the urgent production time length of the marked production line is taken as the distribution production time length of the inserted order; the urgent production quantity of the marked production line is taken as the distribution production quantity of the inserted order; the distribution production time length and the distribution production quantity are taken as the distribution characteristics of the inserted order; S4-3, when the inserted order is produced into the urgent order, taking the surplus production capacity of the production line corresponding to the inserted order as a production capacity standard, and taking the allocated production time length of the inserted order as a time length standard, calculating the product of the production capacity standard and the time length standard as a standard production quantity of the inserted order; calculating the difference between the standard production quantity of the inserted order and the allocated production quantity of the inserted order, and recording it as a first production difference value of the inserted order; If the first production difference value of all inserted orders is greater than or equal to zero, the first production difference value is counted into the completed quantity of the corresponding inserted order; If there is an inserted order whose first production difference value is less than zero, the inserted order is marked as an insufficient order, the absolute value of the first production difference value of the insufficient order is taken, and the sum of the absolute values of the first production difference values of all insufficient orders is taken as an insufficient quantity; Extracting the inserted order with the first production difference value greater than or equal to zero to form a sufficient order set; according to the size of the surplus production capacity of the production line corresponding to the inserted order, the inserted orders in the sufficient order set are sorted from large to small, the inserted orders in the sufficient order set are extracted in order according to the sorting, the sum of the first production difference values of the extracted inserted orders is calculated and recorded as a compensation quantity; when the compensation quantity is greater than or equal to the insufficient quantity, the extraction is stopped; The products produced by the extracted inserted orders are used as the supplement of the urgent order, and the supplement quantity is the first production difference value corresponding to the inserted order; After the products produced by the extracted inserted orders are used as the supplement of the urgent order, the products produced by the insufficient order after the delivery of the urgent order are used as the supplement of the extracted inserted order, and the supplement quantity is the insufficient quantity corresponding to the insufficient order.

2. The artificial intelligence-based industrial automation production scheduling method of claim 1, wherein: The step S1 comprises: S1-1, a production line generates a historical production record every time it runs and stores it in a database; the historical production record comprises a product, a production time length, a production quantity and a main equipment average load; According to the main equipment average load, a load threshold is set, and historical production records in which the main equipment average load is less than or equal to the load threshold are selected from the historical production records to form a first record set; S1-2, according to the type of the product, the historical production records in the first record set are divided into different product production sets; S1-3, taking a product production set as a target set; extracting any historical production record from the target set, calculating the ratio of the production quantity to the production time length in the historical production record as the production capacity of the historical production record; comparing the production capacities of all historical production records of the same production line in the target set, and selecting the maximum value as the maximum production capacity of the production line for producing the corresponding product.

3. The artificial intelligence-based industrial automation production scheduling method of claim 2, wherein: The step S2 comprises: S2-1, when a production line is running, a real-time production record of the production line is generated and stored in a database; the real-time production record comprises an order number, a produced time length and a produced quantity; S2-2, according to the order number, the order information of the order corresponding to the production line is matched, and the order information comprises an order number, a product, an order quantity, a completed quantity and a promised delivery date; S2-3, according to the real-time production record of a certain production line, calculate the ratio of the production duration to the production quantity as the real-time production value of the production line; calculate the difference between the order quantity of the order corresponding to the production line and the completed quantity to obtain the remaining production quantity of the production line; calculate the ratio of the remaining production quantity of the production line to the real-time production value to obtain the remaining production duration of the production line; add the timestamp corresponding to the real-time production record of the production line to the remaining production duration of the production line to obtain the predicted completion date of the production line; obtain the time length from the predicted completion date of the production line to the agreed delivery date of the order as the surplus duration of the production line; extract the maximum production capacity of the product produced by the production line as the surplus production capacity of the production line; calculate the product of the surplus duration of the production line and the surplus production capacity to obtain the surplus production quantity of the production line; take the surplus production capacity and the surplus production quantity of the production line as the surplus features of the production line.

4. The artificial intelligence-based industrial automation production scheduling method of claim 3, wherein: The step S3 comprises: S3-1, obtain the urgent order from the database, the urgent order comprising a product, an urgent quantity and an urgent delivery date; according to the timestamp corresponding to the urgent order, obtain the real-time production record of the production line and the corresponding surplus features from the database; S3-2, divide the production line whose product in the real-time production record is the same as that of the urgent order and whose agreed delivery date of the corresponding order is later than the urgent delivery date to the first production line set; according to the surplus production quantity of each production line in the first production line set, calculate the sum of the surplus production quantities of all production lines in the first production line set, and judge whether the sum is greater than the urgent quantity of the urgent order; if the sum of the surplus production quantities of all production lines in the first production line set is greater than the urgent quantity of the urgent order, the urgent order can be inserted; if the sum of the surplus production quantities of all production lines in the first production line set is less than or equal to the urgent quantity of the urgent order, the urgent order cannot be inserted.

5. An artificial intelligence-based industrial automation production scheduling system for implementing the artificial intelligence-based industrial automation production scheduling method of any one of claims 1-4, characterized in that: The system comprises a data acquisition module, a data analysis module, a scheduling decision module and a scheduling execution module; The data acquisition module is used to acquire and store the historical production record and the real-time production record of each production line, thereby providing basic data support for the subsequent data analysis module and the scheduling decision module; the data analysis module is used to filter and analyze the historical production record and the real-time production record, and calculate the production capacity, the predicted completion date and the surplus features of each production line; The scheduling decision module is used to filter the production line set according to the product, the urgent quantity and the urgent delivery date of the urgent order, and judge the feasibility of inserting the urgent order according to the surplus features of each production line in the production line set, and feed back the judgment result; the scheduling execution module is used to analyze the inserted order and the allocation features of each inserted order according to the urgent delivery date of the urgent order and the surplus features of each production line in the first production line set when the urgent order can be inserted.

6. The artificial intelligence-based industrial automation production scheduling system of claim 5, wherein: The data acquisition module comprises a historical data unit and a real-time data unit; The historical data unit is used to acquire and store the historical production record of each production line, comprising a product, a production duration, a production quantity and an average load of a main device; the real-time data unit is used to acquire and store the real-time production record of each production line, comprising an order number, a produced duration and a produced quantity.

7. The artificial intelligence-based industrial automation production scheduling system of claim 5, wherein: The data analysis module comprises a capacity calculation unit and a surplus analysis unit; The capacity calculation unit is configured to filter product production sets according to historical production records of each production line, and calculate and analyze the product production sets to obtain maximum production capacity of the production line for corresponding products; the surplus analysis unit is configured to calculate predicted completion dates, surplus time lengths and surplus production amounts of each production line according to real-time production records and historical production records.

8. The artificial intelligence-based industrial automation production scheduling system of claim 5, wherein: The scheduling decision module comprises a production line filtering unit and an order insertion evaluation unit; The production line filtering unit is configured to filter a first production line set according to products of urgent orders and urgent delivery dates, in combination with real-time production records and surplus characteristics; the order insertion evaluation unit is configured to analyze surplus production amounts of each production line in the first production line set, in combination with urgent amounts of the urgent orders, to determine whether the urgent orders can be inserted, and to send a determination result.

9. The artificial intelligence-based industrial automation production scheduling system of claim 5, wherein: The scheduling execution module comprises a task allocation unit and an order insertion adjustment unit; The task allocation unit is configured to calculate executable urgent production amounts of each production line according to surplus capacity sorting of each production line, and to mark inserted production lines, match inserted orders and their allocated production amounts; the order insertion adjustment unit is configured to adjust production according to the inserted orders and their allocated production amounts.

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