Intelligent factory resource scheduling method and device, medium and equipment

Abstract

The invention discloses an intelligent factory resource scheduling method and device, a medium and equipment, and relates to the field of intelligent production detection, and the method comprises the steps: obtaining historical order data, a finished product stock of a current period, and a raw material stock; according to the historical order data, a linear regression analysis method is adopted to predict the finished product demand, a linear regression analysis demand value is obtained, according to the production amount and the order amount in the current period and the finished product stock amount, the maximum stock gap is determined as a predicted threshold value, and an actual threshold value is determined according to the linear regression analysis demand value and the predicted threshold value; when the finished product stock amount or the raw material stock amount in the current period is lower than an actual threshold value and the lacking amount is larger than the minimum production amount and the minimum purchasing amount, a corresponding production task or purchasing task is triggered; and after the products are delivered out of the warehouse, the actual threshold value is dynamically adjusted by obtaining the maximum stock gap in the period or the minimum remaining stock quantity after the products are delivered out of the warehouse, and the material storage quantity is regulated and controlled.

Description

Technical Field

[0001] The present invention relates to the field of intelligent production detection, and in particular to an intelligent factory resource scheduling method, device, medium and equipment. Background Art

[0002] In the existing production management system, the storage quantity and production plan of materials usually rely on human decision-making. This decision-making method has many shortcomings, especially in the case of personnel changes, the new decision maker may not be able to quickly grasp the storage quantity required for different products and raw materials.

[0003] In addition, as time goes by, the demand for various materials changes. During the manual data entry process, employees may enter incorrect data due to fatigue or negligence, resulting in inaccurate control of material storage quantity, which often makes warehouse management face challenges. Summary of the invention

[0004] The present invention provides a method, device, medium and equipment for resource scheduling of an intelligent factory, which is used to solve the above-mentioned problem existing in the prior art, that is, how to improve the accuracy of material storage quantity control in the prior art. The present invention provides a method for resource scheduling of an intelligent factory, which includes: Obtain historical order data, finished product inventory for the current cycle, and raw material inventory; Based on historical order data, the linear regression analysis method is used to predict the demand for finished products in the future cycle and obtain the linear regression analysis demand value; According to the production volume and order volume in the current cycle, as well as the finished product inventory in the current cycle, the maximum inventory gap in the preset cycle is determined as the estimated threshold, and the actual threshold is determined by superimposing the linear regression analysis demand value and the estimated threshold; After receiving the order, the finished product inventory of the current cycle is compared with the actual threshold to determine whether the finished product inventory meets the actual threshold; if so, the product is shipped out; otherwise, the corresponding production task is triggered to produce the finished product; When executing a production task, the raw material inventory of the current cycle is compared with the actual threshold to determine whether the raw material inventory meets the actual threshold. If so, the raw materials are shipped out and finished product production is carried out until the product is shipped out. Otherwise, the raw material replenishment task is triggered to perform raw material inventory detection. When the inventory is less than the actual threshold and the difference is greater than the minimum purchase quantity, a purchase order is automatically generated to warehouse in and out the raw materials, produce the finished product, and ship the product out. After the product is shipped out, the actual threshold is dynamically adjusted by obtaining the maximum inventory gap in the next cycle or the minimum remaining inventory quantity after shipping out. The maximum inventory gap in the next cycle is added to the actual threshold, or the minimum remaining inventory quantity is subtracted. The dynamically adjusted actual threshold is used as the basis for production scheduling after receiving subsequent orders.

[0005] Optionally, the use of a linear regression analysis method to predict the demand for finished products in a future period specifically includes: A virtual column is established for 12 months, with time as the independent variable and period demand as the dependent variable. Linear regression is used to analyze historical data and predict demand in future periods. The specific calculation formula is: Y=a+b1X1+b2X2+...+b13X13+ε; Among them, X1 is the independent variable of time, X2 to X13 are dummy columns of 12 months as independent variables, Y is the dependent variable number of orders, ε is the random error, a and b1 to b13 are the intercepts and coefficients obtained by calculating the number of orders for 12 months using the least squares method.

[0006] Optionally, the determining the actual threshold value by superimposing the linear regression analysis demand value and the estimated threshold value specifically includes: The actual threshold is obtained using the following formula: S=XM+(1-M)Y; Among them, S is the actual threshold, X is the estimated threshold, Y is the linear regression analysis demand value, and M is the estimated threshold weight.

[0007] Optionally, the actual threshold is dynamically adjusted as shown in the following formula: ; Among them, X is the expected threshold, X0 is the current threshold, D is the number of orders, C is the actual production volume, and t is the cycle length.

[0008] The present invention provides a smart factory resource scheduling device, comprising: The acquisition module is used to obtain historical order data, finished product inventory and raw material inventory of the current cycle; The actual threshold determination module is used to predict the demand for finished products in the future cycle by using a linear regression analysis method based on historical order data, obtain a linear regression analysis demand value, determine the maximum inventory gap in the preset cycle as the estimated threshold based on the production volume and order volume in the current cycle, and the finished product inventory in the current cycle, and determine the actual threshold based on the superposition of the linear regression analysis demand value and the estimated threshold; Finished product scheduling module is used to compare the finished product inventory of the current cycle with the actual threshold after receiving the order to determine whether the finished product inventory meets the actual threshold; if so, the product is shipped out; otherwise, the corresponding production task is triggered to produce the finished product; The raw material scheduling module is used to compare the raw material inventory of the current cycle with the actual threshold when executing the production task to determine whether the raw material inventory meets the actual threshold; if so, the raw materials are shipped out and the finished product is produced until the product is shipped out; otherwise, the raw material replenishment task is triggered to perform raw material inventory detection. When the inventory is less than the actual threshold and the difference is greater than the minimum purchase quantity, a purchase order is automatically generated to store and ship the raw materials, produce the finished product, and ship the product out; The adjustment module is used to dynamically adjust the actual threshold after the product is shipped out by obtaining the maximum inventory gap in the next cycle or the minimum remaining inventory quantity after shipping out, adding the maximum inventory gap in the next cycle to the actual threshold, or subtracting the minimum remaining inventory quantity, and using the dynamically adjusted actual threshold as the basis for production scheduling judgment after receiving subsequent orders.

[0009] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned intelligent factory resource scheduling method is implemented.

[0010] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned intelligent factory resource scheduling method when executing the program.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a smart factory resource scheduling method, which analyzes historical order data by using linear regression analysis, and obtains the actual threshold value in combination with product production volume and order volume; when the finished product inventory or raw material inventory in the current cycle is lower than the actual threshold value, the corresponding production tasks and procurement tasks can be triggered to ensure continuous supply of the production line; by obtaining the inventory gap and the remaining inventory after delivery within a period of time, the actual threshold value is dynamically adjusted, and the dynamically adjusted actual threshold value is used as the basis for production scheduling judgment after subsequent order receipt to control the stock quantity, thereby improving the accuracy of material storage quantity control; in addition, when a larger value of the actual threshold value needs to be modified, the relevant personnel are notified to review and confirm to ensure the accuracy of the decision, thereby further ensuring the accuracy of the material storage quantity control. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0013] Figure 1 A flow chart of a smart factory resource scheduling method provided by an embodiment of the present invention; Figure 2 A raw material and product inventory scheduling flow chart of a smart factory resource scheduling method provided by an embodiment of the present invention; Figure 3 A schematic diagram of computer equipment for a smart factory resource scheduling method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0016] like Figure 1 and Figure 2 As shown, this embodiment shows a smart factory resource scheduling method, including: S1: Obtain historical order data, finished product inventory in the current cycle, and raw material inventory.

[0017] S2: Based on the historical order data, the linear regression analysis method is used to predict the demand for finished products in the future cycle and obtain the linear regression analysis demand value.

[0018] Generally, the demand for a product or raw material may change due to changes in time, season, etc.

[0019] Optionally, a linear regression analysis method is used to predict the demand for finished products in the future cycle, including: A virtual column is established for 12 months, with time as the independent variable and period demand as the dependent variable. Linear regression is used to analyze historical data and predict demand in future periods. The specific calculation formula is: Y=a+b1X1+b2X2+...+b13X13+ε; Among them, X1 is the independent variable of time, X2 to X13 are dummy columns of 12 months as independent variables, Y is the dependent variable number of orders, ε is the random error, a and b1 to b13 are the intercepts and coefficients obtained by calculating the number of orders for 12 months using the least squares method.

[0020] S3: Based on the production volume and order volume in the current cycle, as well as the finished product inventory in the current cycle, determine the maximum inventory gap in the preset cycle as the estimated threshold, and determine the actual threshold by superimposing the linear regression analysis demand value and the estimated threshold.

[0021] S4: After receiving the order, the finished product inventory of the current cycle is compared with the actual threshold to determine whether the finished product inventory meets the actual threshold; if so, the product is shipped out; otherwise, the corresponding production task is triggered to produce the finished product.

[0022] S5: When executing a production task, the raw material inventory of the current cycle is compared with the actual threshold to determine whether the raw material inventory meets the actual threshold; if so, the raw materials are shipped out and finished product production is carried out until the product is shipped out; otherwise, the raw material replenishment task is triggered to perform raw material inventory detection. When the inventory is less than the actual threshold and the difference is greater than the minimum purchase quantity, a purchase order is automatically generated, the raw materials are put in and shipped out, the finished product is produced, and the product is shipped out.

[0023] Exemplarily, a raw material replenishment task is triggered to perform raw material inventory detection. When the inventory is less than the actual threshold and the difference is greater than the minimum purchase quantity, a purchase order is automatically generated according to the supplier's quotation, the raw materials are put in and out of the warehouse, and the finished products are obtained and shipped out.

[0024] S6: After the product is shipped out, the actual threshold is dynamically adjusted by obtaining the maximum inventory gap in the next cycle or the minimum remaining inventory after shipping out. The maximum inventory gap in the next cycle is added to the actual threshold, or the minimum remaining inventory is subtracted. The dynamically adjusted actual threshold is used as the basis for production scheduling after receiving subsequent orders.

[0025] For example, an initial inventory threshold can be set for each product and raw material based on the expected shipment quantity. This threshold will serve as the benchmark for subsequent dynamic adjustments, and the minimum production quantity and minimum purchase quantity will be set. By regularly checking whether the inventory quantity of each material is lower than the set threshold, if it is found that the inventory quantity of a certain material is lower than its threshold and the missing quantity is greater than the minimum production quantity and the minimum purchase quantity, the corresponding production tasks and purchase orders will be automatically triggered to ensure continuous supply of the production line.

[0026] For example, when it is necessary to modify the actual threshold and inventory threshold with larger values, relevant personnel can be notified for review and confirmation to ensure the accuracy of the decision, thereby further ensuring the accuracy of material storage quantity control.

[0027] For example, suppose the initial threshold of a product A is 10. On the first day, the number of orders is 20, resulting in an inventory gap of 10; on the second day, 5 products A are produced, and the number of orders on that day is 10, which again increases the inventory gap to 15; on the third day, 5 products are produced again, but there are no new orders, and the inventory gap remains at 10. After three days of calculation, the maximum inventory gap in one period is 15, and the estimated threshold of product A can be set to 25 (10+15); Adjust the second cycle to: In the next cycle, if the new threshold of product A is 25, the order quantity on the first day is 5, and the remaining inventory is 20. On the second day, 5 products A are produced, and the order quantity is 5, then the remaining inventory on the second day is 20. On the third day, 5 products A are produced, and the order quantity is 0, then the remaining inventory on the third day is 25, and the minimum remaining inventory in this cycle is 20. The estimated threshold of product A is 5 (25-20). The specific calculation formula for the estimated threshold is as follows: ; Among them, X is the expected threshold, X0 is the current threshold, D is the number of orders, C is the actual production volume, and t is the cycle length.

[0028] Furthermore, the actual threshold is obtained using the following formula: S=XM+(1-M)Y; Among them, S is the actual threshold, X is the estimated threshold, Y is the linear regression analysis demand value, and M is the estimated threshold weight.

[0029] By taking the actual threshold as the basis for a single production or purchase quantity, and by comparing the difference between the actual demand of the current cycle and the actual threshold, the weight M can be gradually adjusted; a combination of short-term and long-term forecasting methods can be used to analyze demand and reduce errors, so as to improve the accuracy of material storage quantity control.

[0030] Exemplarily, the minimum production quantity and the minimum purchase quantity can also be determined when the actual threshold is obtained. When the finished product inventory or the raw material inventory in the current cycle is lower than the actual threshold, and the missing quantity is greater than the minimum production quantity and the minimum purchase quantity, the corresponding production tasks and purchase tasks are triggered to ensure continuous supply of the production line.

[0031] The above is a smart factory resource scheduling method provided by one or more embodiments of this specification. Based on the same idea, this specification also provides a corresponding smart factory resource scheduling device, including: The acquisition module is used to obtain historical order data, finished product inventory and raw material inventory of the current cycle; The actual threshold determination module is used to predict the demand for finished products in the future cycle by using a linear regression analysis method based on historical order data, obtain a linear regression analysis demand value, determine the maximum inventory gap in the preset cycle as the estimated threshold based on the production volume and order volume in the current cycle, and the finished product inventory in the current cycle, and determine the actual threshold based on the superposition of the linear regression analysis demand value and the estimated threshold; Finished product scheduling module is used to compare the finished product inventory of the current cycle with the actual threshold after receiving the order to determine whether the finished product inventory meets the actual threshold; if so, the product is shipped out; otherwise, the corresponding production task is triggered to produce the finished product; The raw material scheduling module is used to compare the raw material inventory of the current cycle with the actual threshold when executing the production task to determine whether the raw material inventory meets the actual threshold; if so, the raw materials are shipped out and the finished product is produced until the product is shipped out; otherwise, the raw material replenishment task is triggered to perform raw material inventory detection. When the inventory is less than the actual threshold and the difference is greater than the minimum purchase quantity, a purchase order is automatically generated to store and ship the raw materials, produce the finished product, and ship the product out; The adjustment module is used to dynamically adjust the actual threshold after the product is shipped out by obtaining the maximum inventory gap in the next cycle or the minimum remaining inventory quantity after shipping out, adding the maximum inventory gap in the next cycle to the actual threshold, or subtracting the minimum remaining inventory quantity, and using the dynamically adjusted actual threshold as the basis for production scheduling judgment after receiving subsequent orders.

[0032] For the specific definition of the intelligent factory resource scheduling device, please refer to the definition of the intelligent factory resource scheduling method above, which will not be repeated here. Each module in the above-mentioned intelligent factory resource scheduling device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0033] The present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program can be used to execute the smart factory resource scheduling method provided above.

[0034] The present invention also provides Figure 3 The structural diagram of the computer device shown in FIG. Figure 3As shown, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the smart factory resource scheduling method provided in the above embodiment.

[0035] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A smart factory resource scheduling method, characterized in that: include: Obtain historical order data, finished product inventory for the current cycle, and raw material inventory; Based on historical order data, the linear regression analysis method is used to predict the demand for finished products in the future cycle and obtain the linear regression analysis demand value; According to the production volume and order volume in the current cycle, as well as the finished product inventory in the current cycle, the maximum inventory gap in the preset cycle is determined as the estimated threshold, and the actual threshold is determined by superimposing the linear regression analysis demand value and the estimated threshold; After receiving the order, the finished product inventory of the current cycle is compared with the actual threshold to determine whether the finished product inventory meets the actual threshold; if so, the product is shipped out; otherwise, the corresponding production task is triggered to produce the finished product; When executing a production task, the raw material inventory in the current cycle is compared with the actual threshold to determine whether the raw material inventory meets the actual threshold; If yes, the raw materials will be shipped out and the finished products will be produced until the products are shipped out; otherwise, the raw material replenishment task will be triggered to check the raw material inventory. When the inventory is less than the actual threshold and the difference is greater than the minimum purchase quantity, a purchase order will be automatically generated, the raw materials will be put in and shipped out, the finished products will be produced, and the products will be shipped out; After the product is shipped out, the actual threshold is dynamically adjusted by obtaining the maximum inventory gap in the next cycle or the minimum remaining inventory quantity after shipping out. The maximum inventory gap in the next cycle is added to the actual threshold, or the minimum remaining inventory quantity is subtracted. The dynamically adjusted actual threshold is used as the basis for production scheduling after receiving subsequent orders.

2. The intelligent factory resource scheduling method according to claim 1, characterized in that: The linear regression analysis method is used to predict the demand for finished products in the future cycle, specifically including: A virtual column is established for 12 months, with time as the independent variable and period demand as the dependent variable. Linear regression is used to analyze historical data and predict demand in future periods. The specific calculation formula is: Y=a+b1X1+b2X2+...+b13X13+ε; Among them, X1 is the independent variable of time, X2 to X13 are dummy columns of 12 months as independent variables, Y is the dependent variable number of orders, ε is the random error, a and b1 to b13 are the intercepts and coefficients obtained by calculating the number of orders for 12 months using the least squares method.

3. The intelligent factory resource scheduling method according to claim 1, characterized in that: The determining of the actual threshold value by superposition of the linear regression analysis demand value and the estimated threshold value specifically includes: The actual threshold is obtained using the following formula: S=XM+(1-M)Y; Among them, S is the actual threshold, X is the estimated threshold, Y is the linear regression analysis demand value, and M is the estimated threshold weight.

4. The intelligent factory resource scheduling method according to claim 1, characterized in that: The actual threshold is dynamically adjusted as shown in the following formula: ； Among them, X is the expected threshold, X0 is the current threshold, D is the number of orders, C is the actual production volume, and t is the cycle length.

5. A smart factory resource scheduling device, characterized in that: include: The acquisition module is used to obtain historical order data, finished product inventory and raw material inventory of the current cycle; The actual threshold determination module is used to predict the demand for finished products in the future cycle by using a linear regression analysis method based on historical order data, obtain a linear regression analysis demand value, determine the maximum inventory gap in the preset cycle as the estimated threshold based on the production volume and order volume in the current cycle, and the finished product inventory in the current cycle, and determine the actual threshold based on the superposition of the linear regression analysis demand value and the estimated threshold; Finished product scheduling module is used to compare the finished product inventory of the current cycle with the actual threshold after receiving the order to determine whether the finished product inventory meets the actual threshold; if so, the product is shipped out; otherwise, the corresponding production task is triggered to produce the finished product; The raw material scheduling module is used to compare the raw material inventory of the current cycle with the actual threshold when executing the production task to determine whether the raw material inventory meets the actual threshold; If yes, the raw materials will be shipped out and the finished products will be produced until the products are shipped out; otherwise, the raw material replenishment task will be triggered to check the raw material inventory. When the inventory is less than the actual threshold and the difference is greater than the minimum purchase quantity, a purchase order will be automatically generated, the raw materials will be put in and shipped out, the finished products will be produced, and the products will be shipped out; The adjustment module is used to dynamically adjust the actual threshold after the product is shipped out by obtaining the maximum inventory gap in the next cycle or the minimum remaining inventory quantity after shipping out, adding the maximum inventory gap in the next cycle to the actual threshold, or subtracting the minimum remaining inventory quantity, and using the dynamically adjusted actual threshold as the basis for production scheduling judgment after receiving subsequent orders.

6. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the smart factory resource scheduling method described in any one of claims 1 to 4 is implemented.

7. A computer device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, it implements the intelligent factory resource scheduling method described in any one of claims 1 to 4.

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