Intelligent milk powder production scheduling and optimizing system and method

Abstract

The invention relates to the technical field of intelligent scheduling and optimization analysis, in particular to an intelligent milk powder production scheduling and optimization system and method, and the method comprises the steps: obtaining a milk powder production process, corresponding process data, an order quantity and a planned order delivery cycle, carrying out the data processing of the process data, and obtaining a scheduling result; constructing an input gate of milk powder production, determining an estimated cycle yield of the input gate, and constructing an output gate of milk powder production based on the estimated cycle yield, the order quantity and the planned order delivery cycle; and regarding the input gate and the output gate as event gates, performing in-gate data analysis on the event gates, and determining a scheduling optimization method of the event gates. According to the method, the input gate and the output gate are constructed to dynamically integrate process data, real-time energy consumption, milk powder parameter states and order demands are combined, a multi-threshold judgment and scheduling scheme hierarchical execution mechanism is utilized, and the problems of high raw material retention deterioration risk, frequent equipment overload shutdown, lagging order response and the like in traditional scheduling are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent scheduling and optimization analysis, and in particular to an intelligent milk powder production scheduling and optimization system and method. Background Art

[0002] Production scheduling is a management activity that optimizes production processes to ensure efficiency, quality and delivery time by coordinating resources such as manpower, equipment and materials. Resource scheduling is particularly critical in milk powder production, as the process is complex and involves multiple links such as sterilization, concentration, drying and packaging of raw milk, which require precise connection to avoid cross contamination or nutritional loss. Equipment needs to be cleaned and maintained regularly, and downtime directly affects production capacity, requiring rapid adjustment of production schedules.

[0003] A Chinese invention patent with publication number CN119472531A discloses a food production scheduling system based on order requirements, which includes: data acquisition module, marking module, determination module, adjustment module, selection module, transmission module and correction module. The present invention effectively reduces the loss caused by meat expiration through real-time data monitoring and analysis, ensures the safety and quality of food, and uses advanced sensors and intelligent data processing technology to obtain key indicators of glossiness, viscosity and water accumulation area of ​​meat in a timely manner, thereby evaluating the freshness and suitable processing status of meat in real time. The intelligent priority scheduling mechanism enables the production link to respond quickly to order requirements and ensures the timely delivery of orders.

[0004] Compared with the existing technology, in the milk powder production process, real-time energy consumption, milk powder parameter status and order demand are three important influencing factors, which reflect whether the enterprise has an optimized production structure, whether it has a high-quality production process and order delivery credibility. It plays a decisive role in the development prospects of current milk powder production enterprises. How to effectively solve the high risk of raw material retention and deterioration, frequent equipment overload and shutdown, and delayed order response in traditional scheduling has become an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to propose an intelligent milk powder production scheduling and optimization system and method in view of the problems existing in the background technology.

[0006] The technical solution of the present invention is an intelligent milk powder production scheduling and optimization method, comprising the following steps: Obtain the production process of milk powder, the corresponding process data, order quantity and planned order delivery cycle, perform data processing on the process data, build an input gate for milk powder production and determine the estimated cycle output of the input gate, and build an output gate for milk powder production based on the estimated cycle output, order quantity and planned order delivery cycle; The input gate and the output gate are both regarded as event gates, and the data inside the event gate is analyzed to determine the scheduling optimization method of the event gate. Based on the scheduling optimization method, the production line in the production process is scheduled and optimized.

[0007] Preferably, the process data includes process number, production line number, continuous operation time of the production line, total energy consumption of the production line, raw material parameters and production parameters of milk powder.

[0008] Preferably, processing the process data to construct an input gate for milk powder production includes: The determination of the cycle loss coefficient includes obtaining the raw material input and raw material output of the processing cycle, and calculating the cycle loss coefficient of the current input gate through the following formula : ; In the formula, It is the raw material input amount of the previous process in the processing cycle; is the raw material output of the current process in the processing cycle; j is the process number; n is the number of processing cycles; j and n are both positive integers.

[0009] Preferably, the raw material input Obtained through data analysis and calculation, including marking the previous process as the target process, obtaining the current processing process and marking it as the selected process, and calculating the raw material input using the input calculation model ; The input calculation model expression is: ; In the formula, is the linear output rate of raw materials in the target process; is the linear input rate of raw materials in the selected process; T is the processing cycle time; It is the residence time threshold of raw materials in the selected process.

[0010] Preferably, processing the process data to construct an input gate for milk powder production further includes: Determination of the mean value of processing deviation, including obtaining the real-time parameters and qualified parameter range of milk powder, and setting the median value of the qualified parameter range as the standard parameter of milk powder; The offset values ​​of the production parameters of milk powder in different production lines in the selected process are calculated by the following expression: : ; In the formula, is the real-time parameter of milk powder. It is the standard parameter of milk powder. The qualified parameter range is determined based on the production requirements of milk powder, and the median value of the qualified parameter range is obtained; and They are the lower limit of the qualified parameter range and the upper limit of the qualified parameter range respectively; is the weight coefficient of the milk powder parameter, which is obtained based on the big data test of the parameter; i is the parameter number of the milk powder, and i is a positive integer; By adjusting the offset values ​​of all production parameters of milk powder in the selected process Perform mean calculation to determine the mean of the machining offset of the selected process and mark it as ; The process number, production line number, continuous operation time of the production line, total energy consumption of the production line, and processing offset mean are recorded. and the periodic loss coefficient As the gate input factor, construct the input gate of the production process.

[0011] Preferably, determine the estimated cycle yield of the input gate This is achieved through the following cycle production estimation formula: ; Where Z is the total amount of raw materials.

[0012] Preferably, the method for constructing an output gate for milk powder production based on estimated cycle output, order volume, and planned order delivery cycle includes: Obtain historical order delivery cycles, compare historical order delivery cycles, and determine the shortest historical order delivery cycle Nmin and the longest historical order delivery cycle Nmax; Substitute the shortest delivery cycle Nmin and the longest delivery cycle Nmax of historical orders into the cycle output estimation formula for calculation, and obtain the estimated cycle output of the shortest cycle order and the longest cycle order cycle estimated output ; Estimated production based on the shortest cycle order cycle and the longest cycle order cycle estimated output Calculate the estimated period delivery order quantity threshold R, the estimated delivery order quantity threshold R satisfies: ; Calculate the ratio of the order quantity to the planned order delivery cycle to obtain the planned cycle delivery order quantity S; The planned cycle delivery order quantity S and the prepaid cycle delivery order quantity threshold R are compared. If the planned cycle delivery order quantity S is not less than the prepaid cycle delivery order quantity threshold R, a hold scheduling signal is generated; if the planned cycle delivery order quantity S is less than the prepaid cycle delivery order quantity threshold R, a demand scheduling signal is generated.

[0013] Preferably, the event gate is subjected to gate data analysis, including calculating the scheduling parameters of the input gate in the current processing cycle based on the input factors. ; ; In the formula, is the total energy consumption of the production line; is the standard production energy consumption; , and All are weight coefficients; is the process scheduling threshold; Scheduling parameters Perform data analysis, if the scheduling parameters is not greater than 0, the input gate is judged to be in a balanced state and a gate hold instruction is generated; if the scheduling parameter If it is greater than 0, the input gate is judged to be in an abnormal state and a gate scheduling instruction is generated.

[0014] Preferably, the scheduling optimization method of the event gate is determined as follows: Obtaining a scheduling signal of an output gate, not scheduling an input gate according to a gate holding instruction and a holding scheduling signal; implementing a first scheduling scheme on the input gate according to the gate scheduling instruction and the holding scheduling signal; A second scheduling scheme is implemented on the input gate according to the gate holding instruction and the demand scheduling signal; and a third scheduling scheme is implemented on the input gate according to the gate scheduling instruction and the demand scheduling signal.

[0015] The present invention also discloses an intelligent milk powder production scheduling and optimization system, which applies the above-mentioned intelligent milk powder production scheduling and optimization method, and specifically includes: The data acquisition and gate construction module is used to obtain the production process of milk powder, the corresponding process data, the order quantity and the planned order delivery cycle, perform data processing on the process data, construct the input gate of milk powder production and determine the estimated cycle output of the input gate, and construct the output gate of milk powder production based on the estimated cycle output, order quantity and planned order delivery cycle; The gate analysis and method construction module is used to regard both the input gate and the output gate as event gates, analyze the data within the event gate, determine the scheduling optimization method of the event gate, and optimize the scheduling of the production line in the production process based on the scheduling optimization method.

[0016] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects: (1) By constructing input gates and output gates to dynamically integrate process data, combined with real-time energy consumption, milk powder parameter status and order requirements, and using a multi-threshold judgment and scheduling scheme hierarchical execution mechanism, the problems of high risk of raw material retention and deterioration, frequent equipment overload and shutdown, and delayed order response in traditional scheduling were solved; (2) Reduce losses by accurately quantifying the efficiency of raw material circulation, balance production rhythm and quality control by using priority sorting and dynamic adjustment strategies, and achieve rapid response to abnormalities with the help of multi-level scheduling signals, thereby improving resource utilization and reducing energy consumption in complex processes, while ensuring the safety of milk powder and the stability of order delivery. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the first embodiment of the present invention. DETAILED DESCRIPTION

[0018] Embodiment 1, as Figure 1 As shown, the intelligent milk powder production scheduling and optimization method proposed by the present invention comprises the following steps: Obtain the production process of milk powder, the corresponding process data, order quantity and planned order delivery cycle, perform data processing on the process data, build the input gate of milk powder production and determine the estimated cycle output of the input gate, and build the output gate of milk powder production based on the estimated cycle output, order quantity and planned order delivery cycle; the process data includes process number, production line number, continuous operation time of the production line, total energy consumption of the production line, raw material parameters and production parameters of milk powder; Process the process data and construct the input gate for milk powder production, including: The determination of the cycle loss coefficient includes obtaining the raw material input and raw material output of the processing cycle, and calculating the cycle loss coefficient of the current input gate through the following formula : ; In the formula, It is the raw material input amount of the previous process in the processing cycle; is the raw material output of the current process in the processing cycle; j is the process number; n is the number of processing cycles; j and n are both positive integers; it should be noted that the raw material output of the first input gate is the raw material input of the second input gate, the raw material output of the second input gate is the raw material input of the third input gate, and so on; especially for the first input gate, its raw material input is the total amount of raw materials ordered; Among them, the raw material input Obtained through data analysis and calculation, including marking the previous process as the target process, obtaining the current processing process and marking it as the selected process, and calculating the raw material input using the input calculation model ; The input calculation model expression is: ; In the formula, is the linear output rate of raw materials in the target process; is the linear input rate of raw materials in the selected process; T is the processing cycle time; It is the retention time threshold of the raw materials in the selected process. It should be noted that the default processing mode of the target process and the selected process is linear continuous processing, that is, the processed raw materials are continuously output in the target process, and the processed raw materials are continuously input into the selected process. By default, the linear input and input rates corresponding to different production line numbers in the same process are the same. Explanation of the retention time threshold: if the raw materials output by the equipment in the target process exceed the retention time threshold and have not been transported to the equipment in the selected process for processing, the raw materials will deteriorate and become loss raw materials. Determination of the mean value of processing deviation, including obtaining the real-time parameters and qualified parameter range of milk powder, and setting the median value of the qualified parameter range as the standard parameter of milk powder; The offset values ​​of the production parameters of milk powder in different production lines in the selected process are calculated by the following expression: : ; In the formula, It is the real-time parameter of milk powder. It is the standard parameter of milk powder. The qualified parameter range is determined based on the production requirements of milk powder, and the median value of the qualified parameter range is obtained; and They are the lower limit of the qualified parameter range and the upper limit of the qualified parameter range respectively; is the weight coefficient of the milk powder parameter, which is obtained based on the big data test of the parameter; i is the parameter number of the milk powder, and i is a positive integer; By adjusting the offset values ​​of all production parameters of milk powder in the selected process Perform mean calculation to determine the mean of the machining offset of the selected process and mark it as ; The process number, production line number, continuous operation time of the production line, total energy consumption of the production line, and processing offset mean are recorded. and the periodic loss coefficient As gate input factors, construct the input gate of the production process; Determine the estimated cycle yield of the input gate This is achieved through the following cycle production estimation formula: ; Where Z is the total amount of raw materials; The method of constructing the output gate of milk powder production based on the estimated cycle output, order volume and planned order delivery cycle includes: Obtain historical order delivery cycles, compare historical order delivery cycles, and determine the shortest historical order delivery cycle Nmin and the longest historical order delivery cycle Nmax; Substitute the shortest delivery cycle Nmin and the longest delivery cycle Nmax of historical orders into the cycle output estimation formula for calculation, and obtain the estimated cycle output of the shortest cycle order and the longest cycle order cycle estimated output ; Estimated production based on the shortest cycle order cycle and the longest cycle order cycle estimated output Calculate the estimated period delivery order quantity threshold R, the estimated delivery order quantity threshold R satisfies: ; Calculate the ratio of the order quantity to the planned order delivery cycle to obtain the planned cycle delivery order quantity S; Compare the planned period delivery order quantity S and the prepaid period delivery order quantity threshold R. If the planned period delivery order quantity S is not less than the prepaid period delivery order quantity threshold R, a hold scheduling signal is generated; if the planned period delivery order quantity S is less than the prepaid period delivery order quantity threshold R, a demand scheduling signal is generated; Establish output gates for production processes based on scheduling signals; The input gate and the output gate are both regarded as event gates, the data inside the event gate is analyzed, the scheduling optimization method of the event gate is determined, and the scheduling optimization of the production line in the production process is performed based on the scheduling optimization method; Perform gate data analysis on the event gate, including calculating the scheduling parameters of the input gate in the current processing cycle based on the input factors ; ; In the formula, is the total energy consumption of the production line; is the standard production energy consumption; , and All are weight coefficients; is the process scheduling threshold; Scheduling parameters Perform data analysis, if the scheduling parameters is not greater than 0, the input gate is judged to be in a balanced state and a gate hold instruction is generated; if the scheduling parameter If it is greater than 0, the input gate is judged to be in an abnormal state and a gate scheduling instruction is generated; Determine the scheduling optimization method for the event gate as follows: Obtaining a scheduling signal of an output gate, not scheduling an input gate according to a gate holding instruction and a holding scheduling signal; implementing a first scheduling scheme on the input gate according to the gate scheduling instruction and the holding scheduling signal; A second scheduling scheme is implemented on the input gate according to the gate holding instruction and the demand scheduling signal; and a third scheduling scheme is implemented on the input gate according to the gate scheduling instruction and the demand scheduling signal.

[0019] Embodiment 2 is applied to the first scheduling scheme, the second scheduling scheme and the third scheduling scheme proposed in Embodiment 1, and a specific explanation is given to the first scheduling scheme, the second scheduling scheme and the third scheduling scheme: The first scheduling scheme includes marking the input gate as the target gate and performing the first scheduling operation on the target gate: Perform traversal analysis on the production line of the target gate, compare the running time of the traversed target production line with the standard running cycle time of the corresponding production line, and determine whether the running time of the target production line is longer than the standard running cycle time of the production line, then generate a scheduling label. If the running time of the target production line is not longer than the standard running cycle time of the production line, no operation is performed, and the traversal is continued until the traversal analysis of the production line of the input gate is completed; Shut down the target production line for maintenance, increase the operating energy consumption of the remaining production lines, and trace the scheduling signal. If the scheduling signal has not changed, perform the following operations: Trace the scheduling instructions. If the scheduling instructions have not changed, traverse and analyze the next production line. The first scheduling operation is terminated based on the following circumstances: If the scheduling signal changes, terminating the first scheduling operation; If the scheduling instruction of the target gate changes, the first scheduling operation is terminated; If the production line of the target gate is traversed and analyzed, the first scheduling operation is terminated; The second scheduling scheme includes obtaining the input gate corresponding to the gate holding instruction and marking it as the target gate, obtaining the scheduling parameters of the target gate, and performing the second scheduling operation: The second scheduling operation includes sorting the scheduling parameters in descending order to obtain a scheduling priority sequence, manually intervening the corresponding input gate for the first element of the scheduling priority sequence, updating the scheduling parameters of the adjusted and verified target gate, and updating the scheduling priority sequence; The scheduling signal is traced back. If the scheduling signal has not changed, the scheduling parameters of the corresponding input gate of the first element of the scheduling priority sequence are continuously optimized. The second scheduling operation is terminated based on the following situations: The target gates are all manually intervened, terminating the second scheduling operation; The scheduling signal changes, terminating the second scheduling operation; The third scheduling scheme includes acquiring an input gate whose instruction is a gate scheduling instruction and marking it as a target gate, acquiring a scheduling parameter of the target gate, and preferentially performing the second scheduling operation; When the second scheduling operation is terminated, the scheduling signal is traced back, and if the scheduling signal has not changed, the first scheduling operation is performed; if the scheduling signal has changed, the first scheduling operation is not performed; When the first scheduling operation is executed and terminated, the scheduling signal is traced back for the second time; if the scheduling signal still does not change, the input gate corresponding to the instruction is set as the gate holding instruction and marked as the target gate, and the second scheduling operation is executed again.

[0020] Embodiment 3, an intelligent milk powder production scheduling and optimization system proposed by the present invention, which is applied to an intelligent milk powder production scheduling and optimization method proposed in embodiment 1, specifically includes: The data acquisition and gate construction module is used to obtain the production process of milk powder, the corresponding process data, the order quantity and the planned order delivery cycle, perform data processing on the process data, construct the input gate of milk powder production and determine the estimated cycle output of the input gate, and construct the output gate of milk powder production based on the estimated cycle output, order quantity and planned order delivery cycle; The gate analysis and method construction module is used to regard both the input gate and the output gate as event gates, analyze the data within the event gate, determine the scheduling optimization method of the event gate, and optimize the scheduling of the production line in the production process based on the scheduling optimization method.

[0021] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. An intelligent milk powder production scheduling and optimization method, characterized in that: The following steps are involved: Obtain the production process of milk powder, the corresponding process data, order quantity and planned order delivery cycle, perform data processing on the process data, build an input gate for milk powder production and determine the estimated cycle output of the input gate, and build an output gate for milk powder production based on the estimated cycle output, order quantity and planned order delivery cycle; The input gate and the output gate are both regarded as event gates, and the data inside the event gate is analyzed to determine the scheduling optimization method of the event gate. Based on the scheduling optimization method, the production line in the production process is scheduled and optimized.

2. The intelligent milk powder production scheduling and optimization method according to claim 1, characterized in that: The process data includes process number, production line number, continuous operation time of the production line, total energy consumption of the production line, raw material parameters and milk powder production parameters.

3. The intelligent milk powder production scheduling and optimization method according to claim 1, characterized in that: Process the process data and construct the input gates for milk powder production, including: The determination of the cycle loss coefficient includes obtaining the raw material input and raw material output of the processing cycle, and calculating the cycle loss coefficient of the current input gate through the following formula : ； In the formula, It is the raw material input amount of the previous process in the processing cycle; is the raw material output of the current process in the processing cycle; j is the process number; n is the number of processing cycles; j and n are both positive integers.

4. The intelligent milk powder production scheduling and optimization method according to claim 3, characterized in that: Raw material input Obtained through data analysis and calculation, including marking the previous process as the target process, obtaining the current processing process and marking it as the selected process, and calculating the raw material input using the input calculation model ; The input calculation model expression is: ； In the formula, is the linear output rate of raw materials in the target process; is the linear input rate of the raw materials in the selected process; T is the processing cycle time; It is the residence time threshold of raw materials in the selected process.

5. The intelligent milk powder production scheduling and optimization method according to claim 2, characterized in that: Data processing of process data and construction of input gates for milk powder production also include: Determination of the mean value of processing deviation, including obtaining the real-time parameters and qualified parameter range of milk powder, and setting the median value of the qualified parameter range as the standard parameter of milk powder; The offset values ​​of the production parameters of milk powder in different production lines in the selected process are calculated by the following expression: : ； In the formula, It is the real-time parameter of milk powder. It is the standard parameter of milk powder. The qualified parameter range is determined based on the production requirements of milk powder, and the median value of the qualified parameter range is obtained; and They are the lower limit of the qualified parameter range and the upper limit of the qualified parameter range respectively; is the weight coefficient of the milk powder parameter, which is obtained based on the big data test of the parameter; i is the parameter number of the milk powder, and i is a positive integer; By adjusting the offset values ​​of all production parameters of milk powder in the selected process Perform mean calculation to determine the mean of the machining offset of the selected process and mark it as ; The process number, production line number, continuous operation time of the production line, total energy consumption of the production line, and processing offset mean are recorded. and the periodic loss coefficient As the gate input factor, construct the input gate of the production process.

6. The intelligent milk powder production scheduling and optimization method according to claim 3, characterized in that: Determine the estimated cycle yield of the input gate This is achieved through the following cycle production estimation formula: ; Where Z is the total amount of raw materials.

7. The intelligent milk powder production scheduling and optimization method according to claim 6, characterized in that: The method of constructing the output gate of milk powder production based on the estimated cycle output, order volume and planned order delivery cycle includes: Obtain historical order delivery cycles, compare historical order delivery cycles, and determine the shortest historical order delivery cycle Nmin and the longest historical order delivery cycle Nmax; Substitute the shortest delivery cycle Nmin and the longest delivery cycle Nmax of historical orders into the cycle output estimation formula for calculation, and obtain the estimated cycle output of the shortest cycle order and the longest cycle order cycle estimated output ; Estimated production based on the shortest cycle order cycle and the longest cycle order cycle estimated output Calculate the estimated period delivery order quantity threshold R, the estimated delivery order quantity threshold R satisfies: ； Calculate the ratio of the order quantity to the planned order delivery cycle to obtain the planned cycle delivery order quantity S; The planned cycle delivery order quantity S and the prepaid cycle delivery order quantity threshold R are compared. If the planned cycle delivery order quantity S is not less than the prepaid cycle delivery order quantity threshold R, a hold scheduling signal is generated; if the planned cycle delivery order quantity S is less than the prepaid cycle delivery order quantity threshold R, a demand scheduling signal is generated.

8. The intelligent milk powder production scheduling and optimization method according to claim 7, characterized in that: Perform gate data analysis on the event gate, including calculating the scheduling parameters of the input gate in the current processing cycle based on the input factors ; ； In the formula, is the total energy consumption of the production line; is the standard production energy consumption; , and All are weight coefficients; is the process scheduling threshold; Scheduling parameters Perform data analysis, if the scheduling parameters is not greater than 0, the input gate is judged to be in a balanced state and a gate hold instruction is generated; if the scheduling parameter If it is greater than 0, the input gate is judged to be in an abnormal state and a gate scheduling instruction is generated.

9. The intelligent milk powder production scheduling and optimization method according to claim 8, characterized in that: Determine the scheduling optimization method for the event gate as follows: Obtaining a scheduling signal of an output gate, not scheduling an input gate according to a gate holding instruction and a holding scheduling signal; implementing a first scheduling scheme on the input gate according to the gate scheduling instruction and the holding scheduling signal; A second scheduling scheme is implemented on the input gate according to the gate holding instruction and the demand scheduling signal; and a third scheduling scheme is implemented on the input gate according to the gate scheduling instruction and the demand scheduling signal.

10. An intelligent milk powder production scheduling and optimization system, applied to the intelligent milk powder production scheduling and optimization method according to claims 1 to 9, characterized in that: Specifically include: The data acquisition and gate construction module is used to obtain the production process of milk powder, the corresponding process data, the order quantity and the planned order delivery cycle, perform data processing on the process data, construct the input gate of milk powder production and determine the estimated cycle output of the input gate, and construct the output gate of milk powder production based on the estimated cycle output, order quantity and planned order delivery cycle; The gate analysis and method construction module is used to regard both the input gate and the output gate as event gates, analyze the data within the event gate, determine the scheduling optimization method of the event gate, and optimize the scheduling of the production line in the production process based on the scheduling optimization method.

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