Dynamic parameter perception-based multi-wine-retort vinasse throwing and preparing scheduling method and system

Abstract

The invention relates to the field of white spirit brewing, and provides a multi-wine-retort vinasse distribution scheduling method and system based on dynamic parameter perception in order to improve the dynamic response capability in the production process, a delta value is calculated in real time based on key parameters such as an expansion coefficient, a scheduling strategy is dynamically adjusted, and the problem of plan deviation caused by vinasse discharging volume fluctuation is effectively solved. The production plan adaptability and the matching precision are improved; a delta threshold value is used for dynamically distributing a distribution losing task, so that the maximization of the utilization rate of the cellar and the minimization of the cache pressure are realized, bottleneck overstock and empty material waiting are prevented, and the rhythm stability of a production line is remarkably improved; the system supports task hot start, cold stop and emergency recovery strategies, has the capabilities of task automatic convergence, state safety switching and beat reconstruction, and effectively reduces human intervention and recovery time.

Description

Technical Field

[0001] This invention relates to the field of baijiu brewing, specifically a method and system for scheduling the multiple stills and lees based on dynamic parameter sensing. Background Technology

[0002] In existing solid-state brewing processes, typical brewing lines usually employ a batch fermentation and distillation process with multiple stills and fermentation pits. To achieve effective material balance and continuous, stable production rhythm, a static material ratio scheme is generally adopted in the design phase. For example, in a traditional design, 10 stills are typically configured, with 6 stills handling the continuous fermentation of leftover grains. Theoretically, this can fill all 10 fermentation pits after mixing with the grains from the continuous fermentation process. The remaining 4 stills handle the disposal of leftover grains, promptly processing any remaining materials to prevent material accumulation and disruption of the fermentation cycle.

[0003] However, in actual production processes, the production balancing model based on static design has the following significant problems:

[0004] (1) The material expansion coefficient fluctuates greatly, making it difficult to achieve theoretical material balance.

[0005] During the brewing process, the physical volume of the lees undergoes non-linear changes after processes such as steaming, cooling, and moistening. In particular, differences in the fermentation degree, residual moisture, and cooling effect between different batches of raw materials all lead to fluctuations in the expansion coefficient of the lees. Therefore, the design assumption of "6 continuous lees stills ≈ 10 fermentation pits" based on mass balance often results in insufficient or excessive materials in practice, leading to empty pits or overflowing buffer spaces, disrupting the production cycle.

[0006] (2) Fluctuations in production execution rhythm and insufficient response of static scheduling scheme.

[0007] The production site involves various types of logistics and process equipment, including overhead cranes, RGVs, buffer equipment, and distillation equipment. Factors such as equipment performance fluctuations, concurrent task conflicts, and logistics priority switching make it difficult to ensure that the production scheduling rhythm and process cycle time remain highly consistent. Especially during multi-still parallel operations, continuing to use a static scheduling scheme cannot adjust scheduling based on real-time equipment status and dynamic material changes. This can easily lead to problems such as empty fermentation pits accumulating, full fermentation pits not being transferred in a timely manner, stills waiting to be transferred, and fermentation loading machines waiting for materials, severely impacting production efficiency and cycle time stability. Summary of the Invention

[0008] To improve the dynamic response capability in the production process, this invention provides a method and system for scheduling the multiple stillings and lees distribution based on dynamic parameter perception.

[0009] The technical solution adopted by the present invention to solve the above problems is:

[0010] A multi-distillation still waste sorting method based on dynamic parameter awareness includes:

[0011] Step 1: Obtain key parameters of the brewing workshop's operating status in real time, including at least the status of the stills, the number of empty cellars, the material buffer amount, the estimated value of the expansion coefficient, and the status of the logistics equipment;

[0012] Step 2: Set scheduling thresholds, including upper and lower thresholds;

[0013] Step 3: Calculate the scheduling index value based on the collected data;

[0014] Step 4: Perform dynamic scheduling based on scheduling index values ​​and scheduling thresholds.

[0015] Furthermore, scheduling index values The calculation method is as follows: In the formula, k is the estimated value of the expansion coefficient; Z1 is the number of stills to be transferred in the lees discarding area; Z2 is the number of stills to be transferred in the lees mixing area; E1 is the number of available empty cellars; E2 is the number of full grain hoppers; E3 is the number of cellars that have been buffered and are waiting to be filled; and H is the target value of the system cycle time.

[0016] Furthermore, step 4 specifically means: Δ ≥ upper threshold: the system enters the priority state for discarding errors;

[0017] Δ≤ lower threshold: The system enters the priority state for slag matching;

[0018] Lower threshold < Δ < upper threshold: Dynamic scheduling of multiple targets based on device idle status and task queue length.

[0019] Furthermore, it also includes anomaly adjustment: anomaly determination is performed based on the collected data, and the scheduling strategy is adjusted according to the anomaly type.

[0020] Furthermore, it also includes task interruption recovery control: in the event of a task interruption, the recovery priority is determined based on the real-time scheduling index value and the cache status, and production is resumed according to the recovery priority.

[0021] Furthermore, it also includes flexible start-stop control: before starting or stopping, it determines whether the start-stop conditions are met. When the conditions are met, during the start-up phase, the scheduling order is dynamically determined based on the scheduling index value, and the corresponding equipment is activated first according to the scheduling order. During the shutdown phase, the materials that have been shipped out are gradually consumed according to the process flow and process order before stopping.

[0022] A multi-distillation still waste distribution scheduling system based on dynamic parameter awareness includes:

[0023] Dynamic data acquisition module: used to acquire key parameters of the brewing workshop operation status in real time, including at least the status of the stills, the number of empty cellars, the material buffer amount, the estimated value of the expansion coefficient, and the status of the logistics equipment;

[0024] Threshold setting module: Used to set scheduling thresholds, including upper and lower threshold limits;

[0025] Scheduling indicator value calculation module: Calculates scheduling indicator values ​​based on the collected data;

[0026] Scheduling decision module: performs dynamic scheduling control based on scheduling index values ​​and scheduling thresholds;

[0027] Scheduling execution module: Completes scheduling based on dynamic scheduling control.

[0028] Furthermore, it also includes an anomaly adjustment module: which determines anomalies based on the collected data and adjusts the scheduling strategy according to the anomaly type.

[0029] Furthermore, it also includes a task interruption recovery module: in the event of a task interruption, the recovery priority is determined based on the real-time scheduling index value and the cache status, and production is resumed according to the recovery priority.

[0030] Furthermore, it also includes a flexible start-stop control module: during the start-up phase, the scheduling order is dynamically determined based on the scheduling index value, and the corresponding equipment is activated first according to the scheduling order; during the shutdown phase, the materials that have been sent out of the warehouse are gradually consumed according to the process flow and process order before the shutdown is stopped.

[0031] The advantages of this invention compared to existing technologies are as follows: It calculates the Δ value in real time based on key parameters such as the expansion coefficient and dynamically adjusts the scheduling strategy, effectively addressing the planning deviation caused by fluctuations in the output volume of the waste material, thus improving the adaptability of production plans and the accuracy of proportioning; it dynamically allocates dropout tasks using the Δ threshold, maximizing the utilization rate of the fermentation pits and minimizing buffer pressure, preventing bottleneck accumulation and empty material waiting, and significantly improving the stability of the production line rhythm; the system supports hot start, cold stop, and emergency recovery strategies for tasks, and has the ability to automatically converge tasks, safely switch states, and rebuild cycle time, effectively reducing human intervention and recovery time; the system's algorithm logic and module interfaces are highly reconfigurable, and in addition to being applied to liquor brewing plants, it can also be extended to material scheduling scenarios such as grain processing, semi-solid fermentation, and bioreactors, possessing high industry versatility and commercial prospects. Attached Figure Description

[0032] Figure 1 A flowchart of a multi-distillation still waste sorting method based on dynamic parameter awareness;

[0033] Figure 2 Structure diagram of a multi-distillation still waste distribution scheduling system with dynamic parameter sensing;

[0034] Figure 3 A schematic diagram of a multi-distillation still waste sorting system module with dynamic parameter sensing. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0036] like Figure 1 As shown, the multi-distillation still waste sorting method based on dynamic parameter awareness includes:

[0037] Step 1: Obtain key parameters in the brewing workshop operation status in real time, including at least the status of stills, the number of empty cellars, the material buffer amount, the estimated value of the expansion coefficient, and the status of logistics equipment. The estimated value of the expansion coefficient is calculated based on the number of stills received at the cellar loading station and the number of cellars loaded.

[0038] Step 2: Set scheduling thresholds, including upper and lower thresholds; the scheduling thresholds are preset based on production time nodes (divided into just started, normal production, and about to stop) and differences in production batches, etc.

[0039] Step 3: Calculate the scheduling index value based on the collected data;

[0040] Step 4: Perform dynamic scheduling based on scheduling index values ​​and scheduling thresholds.

[0041] Specifically, scheduling index values The calculation method is as follows: In the formula, k is the estimated value of the expansion coefficient; Z1 is the number of stills to be transferred in the lees discarding area; Z2 is the number of stills to be transferred in the lees mixing area; E1 is the number of available empty cellars; E2 is the number of full grain hoppers; E3 is the number of cellars that have been buffered and are waiting to be filled; and H is the target value of the system cycle time.

[0042] If Δ ≥ the upper limit of the threshold: the system enters the priority state for discarding waste to prevent material backlog;

[0043] If Δ ≤ lower threshold: the system enters the priority state for matching mash, ensuring the continuous progress of the production line;

[0044] If the lower threshold is less than Δ and the upper threshold is less than the lower threshold, dynamic scheduling of multiple targets is performed based on the device's idle status and the task queue length.

[0045] In this embodiment, 10 stills are configured: 4 stills in the lees discarding area and 6 stills in the lees mixing area. Stills No. 4 and No. 5, located at the junction of the lees discarding and mixing areas, can perform both lees discarding and mixing tasks depending on the logistics situation. In the lees discarding priority state, stills No. 4 and No. 5 discard lees to prevent excessive lees at the fermentation station, which would lead to insufficient empty fermentation pits and material accumulation. In the lees mixing priority state, stills No. 4 and No. 5 mix lees to promptly consume empty fermentation pit buffers and prevent excessive empty fermentation pits at the fermentation station, which could cause blockages in the fermentation pit logistics transfer line, thus ensuring the continuous operation of the production line.

[0046] Furthermore, this includes anomaly adjustment, which involves identifying anomalies based on collected data and adjusting the scheduling strategy according to the anomaly type. Anomaly types include: excessively high or low waste material expansion coefficient, task queue delays, or drastic fluctuations in the Δ value. A waste material expansion coefficient significantly higher than expected indicates buffer overflow; a waste material expansion coefficient too low indicates an empty pit; overhead crane / RGV operation congestion indicates task queue delays; and cycle time imbalance indicates drastic fluctuations in the Δ value. The scheduling strategy is automatically adjusted based on the anomaly type. For example, for buffer overflow scenarios, an emergency waste material dropping task is initiated; for empty pit scenarios, a priority waste material matching task is initiated; for waiting logistics equipment scenarios, task priority and resource allocation are adjusted; and for cycle time misalignment scenarios, the Δ calculation parameter k or the cycle time target H is dynamically adjusted.

[0047] To enhance anomaly recovery and flexible control capabilities, this invention also includes task interruption recovery control and flexible start / stop control.

[0048] Task interruption recovery control: In the event of a task interruption, the recovery priority is determined based on real-time scheduling metric values ​​and cache status, and the task is resumed according to the recovery priority.

[0049] Flexible start-stop control: Before starting or stopping, the system first checks whether the core on-site status meets the start-stop conditions, including: whether the storage tank is idle or available; whether the buffer slot is cleared or in a safe state; whether the executing equipment (such as overhead cranes and RGVs) is ready or stopped; and whether the previous round of tasks has been completed. After the start-stop conditions are met, during the start-up phase, the scheduling order is dynamically determined based on the scheduling index value, and the corresponding equipment is activated first according to the scheduling order; during the shutdown phase, the materials that have been released from the warehouse are gradually consumed according to the process flow and process sequence before stopping.

[0050] Correspondingly, the present invention also provides a multi-distillation still waste distribution scheduling system based on dynamic parameter perception, such as... Figure 2 As shown, the system comprises four functional layers from bottom to top: the state perception and acquisition layer, the execution control layer, the scheduling decision control layer, and the monitoring and feedback layer. These layers form a closed-loop control logic of "perception-decision-execution-feedback" to achieve dynamic scheduling of production cycle and flexible control of the system.

[0051] The status perception and acquisition layer is the basic perception unit of this system. It is deployed at various key locations in the brewing site. Through sensors, data interfaces and other means, this layer collects key data such as the operating status of the still, the number of empty cellars, the full load status of the buffer grain hopper, and the expansion and change parameters of the materials, providing a real-time perception basis for the upper-level scheduling logic.

[0052] The execution control layer is the execution vehicle for tasks, including overhead cranes, RGVs, and supporting equipment, used for material transfer to complete scheduling tasks such as waste disposal, waste mixing, and cellar loading. This layer, based on scheduling instructions, ensures efficient connection between on-site logistics paths and process nodes.

[0053] The scheduling decision control layer is the core algorithm and task generation unit of the system. Based on the data provided by the state-aware acquisition layer, this layer dynamically calculates the Δ value and, through the scheduling judgment model proposed in this invention, determines the type of task that should be executed first, and rationally allocates equipment resources. The system also integrates one-click start / stop control logic at this layer to ensure the safe start-up, smooth shutdown, and anomaly recovery of the production system.

[0054] The monitoring and feedback layer serves as the visualization and anomaly feedback interface for the scheduling system. This layer displays the scheduling execution status, receives execution result feedback, and, in the event of equipment malfunctions or task delays, sends data back to the scheduling decision and control layer for scheduling optimization, thereby achieving closed-loop control and resilient response.

[0055] Through the collaborative efforts of the state perception acquisition layer and the monitoring and feedback layer, this system realizes a closed-loop intelligent control mechanism from perception to decision-making and from execution to feedback. It supports the improvement of cycle stability, the enhancement of scheduling flexibility, and the construction of one-click start and stop capabilities for the entire process in the brewing workshop under the environment of parallel production of multiple stills.

[0056] Module diagram as follows Figure 3 As shown, the system includes a dynamic data acquisition module for real-time acquisition of key parameters in the brewing workshop's operation, including at least the status of the stills, the number of empty cellars, material buffer capacity, estimated expansion coefficient, and the status of logistics equipment. A threshold setting module is used to set scheduling thresholds, including upper and lower limits. A scheduling index calculation module calculates scheduling index values ​​based on the acquired data. A scheduling decision module performs dynamic scheduling control based on the scheduling index values ​​and thresholds; scheduling control includes task type, target area, required equipment, and execution window. A scheduling execution module completes scheduling according to dynamic scheduling control, controlling objects such as overhead cranes, RGVs, and cellar loading machines. To facilitate status monitoring, the scheduling execution module also includes a status monitoring unit to monitor equipment operating status and task execution in real-time, including completion status, timeout status, and fault status. By feeding back the execution results of the scheduling execution module to the scheduling index calculation module to update the scheduling index values, the system drives the next round of scheduling optimization, thus forming a dynamic closed-loop scheduling capability.

[0057] Furthermore, it includes an anomaly adjustment module: which determines anomalies based on collected data and adjusts the scheduling strategy according to the anomaly type. A task interruption recovery module: in the event of a task interruption, it determines the recovery priority based on real-time scheduling index values ​​and cache status, and resumes production according to the recovery priority. A flexible start-stop control module: during the start-up phase, it dynamically determines the scheduling order based on scheduling index values ​​and prioritizes activating the corresponding equipment according to the scheduling order; during the shutdown phase, it gradually consumes the materials already shipped according to the process flow and process sequence before stopping.

Claims

1. A method for scheduling of multiple retort drop-off based on dynamic parameter perception, characterized in that, Comprise: Step 1, real-time acquisition of key parameters in the running state of the brewing workshop, including at least the state of the retort, the number of empty cellar pools, the material buffer amount, the estimated value of the expansion coefficient and the state of the logistics equipment; Step 2, setting the scheduling threshold, including the upper threshold and the lower threshold; Step 3, calculating the scheduling index value based on the collected data; Step 4, dynamic scheduling based on the scheduling index value and the scheduling threshold.

2. The dynamic parameter awareness based multi-stillage distillation scheduling method of claim 1, wherein, Scheduling index value The calculation method is: , wherein k is the expansion coefficient estimated value; Z1 is the number of wine stills to be transferred in the current loss zone; Z2 is the number of wine stills to be transferred in the current matching zone; E1 is the number of available empty cellar pools; E2 is the number of full grain hoppers; E3 is the number of already cached cellar pools to be loaded; and H is the target value of the system beat.

3. The dynamic parameter-aware multi-still column distillation scheduling method of claim 1, wherein, Step 4 is specifically: Δ≥upper threshold: the system enters the priority state of losing liquor; Δ≤lower threshold: the system enters the priority state of liquor allocation; Lower threshold < Δ < upper threshold: multi-objective priority dynamic scheduling combined with the idle state of the equipment and the length of the task queue.

4. The dynamic parameter-aware multi-still column distillation scheduling method of claim 1, wherein, It also includes abnormal adjustment: abnormality judgment according to the collected data, and adjustment of the scheduling strategy according to the abnormal type.

5. The dynamic parameter awareness based multi-stillage distillation scheduling method of claim 1, wherein, It also includes task interruption recovery control: in the case of task interruption, the recovery priority is judged according to the real-time scheduling index value and the buffer state, and the production is resumed according to the recovery priority.

6. The method of claim 1, wherein, It also includes flexible start-stop control: before starting or stopping, it is judged whether the start-stop condition is met, when the condition is met, in the starting stage, the scheduling order is dynamically judged based on the scheduling index value, and the corresponding equipment is activated according to the scheduling order; In the shutdown stage, the materials that have been discharged are gradually consumed according to the process sequence after the process flow is stopped.

7. A multi-pot distillation scheduling system based on dynamic parameter sensing, characterized in that, Comprise: Dynamic data acquisition module: for real-time acquisition of key parameters in the running state of the brewing workshop, including at least the state of the retort, the number of empty cellar pools, the material buffer amount, the estimated value of the expansion coefficient and the state of the logistics equipment; Threshold setting module: for setting the scheduling threshold, including the upper threshold and the lower threshold; Scheduling index value calculation module: calculating the scheduling index value based on the collected data; Scheduling decision module: dynamic scheduling control based on the scheduling index value and the scheduling threshold; Scheduling execution module: complete the scheduling according to the dynamic scheduling control.

8. The dynamic parameter perception based multi-stillage distillation scheduling system of claim 7, wherein, It also includes an abnormal adjustment module: abnormality judgment according to the collected data, and adjustment of the scheduling strategy according to the abnormal type.

9. The dynamic parameter perception based multi-still pot distillation scheduling system according to claim 7, wherein, It also includes a task interruption recovery module: in the case of task interruption, the recovery priority is judged according to the real-time scheduling index value and the buffer state, and the production is resumed according to the recovery priority.

10. The dynamic parameter perception based multi-stillage distillation scheduling system of claim 7, wherein, It also includes a flexible start-stop control module: in the starting stage, the scheduling order is dynamically judged based on the scheduling index value, and the corresponding equipment is activated according to the scheduling order; in the shutdown stage, the materials that have been discharged are gradually consumed according to the process sequence after the process flow is stopped.

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