A method and system for optimizing control of production scheduling of an automatic drinking water equipment

Abstract

The present application relates to the technical field of industrial automation production scheduling and process control, and discloses a kind of drinking water automation equipment production scheduling optimization control method and system. First, each independent equipment is numbered and the maximum processing capacity per unit time is obtained to form a capacity set;Then, the total order processing capacity and the total scheduling time are obtained, and the schedulable / non-schedulable orders are estimated according to the shortest processing time;A strategy space of target orders, continuous processing time and potential processing volume is constructed for each device, and over-allocation is eliminated by device selection identifier and processing upper limit constraint;The active state, effective processing volume and utility value are calculated and iteratively optimized to obtain the final target order and processing time;Unfinished orders are identified and combined with the remaining available time to generate a backflow supplement time, and a final scheduling matrix is constructed to issue a master control execution start-stop, which realizes plan feasible screening, avoids invalid stacking, quantitatively evaluates and fills in the gap, and facilitates control execution and traceability.

Description

Technical Field

[0001] This invention relates to the field of industrial automation production scheduling and process control technology, specifically to a method and system for optimizing production scheduling of drinking water automation equipment. Background Technology

[0002] In the field of automated drinking water production, production tasks are typically completed collaboratively by multiple independent automated devices. The processing capacity of different devices varies per unit time, and the processing volume requirements, completion deadlines, and allocation methods for different orders also differ within the scheduling cycle. In existing technologies, a common practice is for a higher-level control system to issue processing tasks to each device based on the production plan, or for schedulers to break down orders based on experience and then allocate them to corresponding devices, with the control unit executing start-stop control according to a preset time sequence. For scenarios with small scale, few orders, and insignificant differences in device capacity, the above methods can accomplish basic production organization.

[0003] However, when the number of drinking water orders increases, the total order processing volume fluctuates more significantly, equipment capabilities differ markedly, and scheduling cycles are limited, existing technologies often rely solely on a single rule for allocation, such as processing according to order arrival order, equipment idle order, or fixed priority. This lacks joint analysis of the total order processing volume, maximum equipment processing capacity, and total scheduling time, making it difficult to promptly identify which orders can be completed within the current cycle and which cannot be included in the current scheduling scope. This easily leads to unreasonable production arrangements. Furthermore, when multiple independent devices collaboratively process multiple order tasks, existing technologies typically lack a unified strategy construction mechanism, failing to simultaneously consider the correspondence between equipment target orders, continuous processing time, and equipment processing volume. When multiple devices simultaneously select the same order task, the allocated processing volume may exceed the order demand; conversely, insufficient order allocation may leave some devices idle, resulting in decreased equipment utilization. Existing technologies also generally lack effective quantitative evaluation mechanisms for collaborative selection, conflict resolution, and resource constraint handling between devices, making it difficult to arrive at a more reasonable equipment allocation result among multiple optional scheduling schemes. Furthermore, if unfinished orders remain after the initial scheduling, existing technologies often lack a method for secondary allocation based on the remaining available time of the equipment. This fails to fully utilize the spare capacity of equipment not currently involved in order processing, leading to delays in some orders and insufficient capacity release within the scheduling cycle. Simultaneously, existing technologies typically lack a clear time allocation matrix corresponding to order tasks and equipment when generating the final scheduling result. This results in insufficient refinement in the subsequent generation of start and stop control signals for each device by the main control unit, thereby affecting the overall scheduling efficiency and execution accuracy of automated drinking water production.

[0004] Therefore, this case aims to propose a production scheduling optimization control method and system for automated drinking water equipment. First, the actual processing capacity of each independent device within a unit of time is identified. Then, it is determined whether the order is feasible within the current scheduling cycle. Subsequently, a strategy space is established for each device, and the processing upper limit constraint is used to avoid the repeated over-allocation of orders. On this basis, a collaborative game-like strategy iteration is executed with the device utility as the driving force to obtain a more reasonable order allocation and processing time. Finally, unfinished orders are supplemented and allocated, and the results are implemented in the final scheduling matrix and the main control unit execution layer. Summary of the Invention

[0005] This invention provides a method and system for optimizing production scheduling of automated drinking water equipment, which helps to solve the problems mentioned in the background art.

[0006] This invention provides the following technical solution: a method for optimizing production scheduling control of automated drinking water equipment, comprising: Each independent device participating in the scheduling is numbered, and the maximum processing capacity of each independent device within a unit of time is obtained to form a set of device processing capacities; Obtain the total processing volume of each drinking water order task within the scheduling period and the total duration of this production scheduling. Form a set of schedulable orders and a set of unschedulable orders based on the estimated shortest processing time of each order task. For each independent device, a device scheduling strategy is constructed to form the target order, continuous processing time and potential processing volume corresponding to each independent device, and to form the strategy space of each independent device; For each order task in the schedulable order set, a device selection identifier is generated, the number of devices selected for each order is counted, the cumulative processing volume allocated to each order is obtained, and the processing upper limit constraint is executed according to the total processing volume of each order task to form the device selection result after the processing upper limit constraint. Based on the equipment selection results after the upper limit constraint, the activation status and effective processing volume of each independent device are formed, and the equipment utility value and the total utility value of the production system are obtained. Based on the equipment utility value, a collaborative game iterative search is performed to form the final target order and final processing time for each independent device within the scheduling cycle; Unfinished orders are identified based on the baseline completed volume and remaining demand of each order task, and the reflow supplementary processing time is obtained by combining the remaining available time of the equipment in the current scheduling cycle when the final target order is not equal to the corresponding order task. The final scheduling matrix is ​​constructed based on the final processing time and the return supplement processing time. The final scheduling matrix is ​​then sent to the main control unit, and the start and stop control of the corresponding order tasks of each independent device is executed according to the final scheduling matrix.

[0007] Optionally, the step of numbering each independent device participating in the scheduling, obtaining the maximum processing capacity of each independent device within a unit of time, and forming a set of device processing capacities specifically includes: Multiple independent devices are deployed on the automated drinking water production line to participate in scheduling, and each independent device is numbered according to the scheduling identification order of the device in the production line. For each numbered device, a standard liquid carrier is used to continuously measure the processing capacity for 1 minute to obtain the maximum processing capacity of the currently enumerated device per unit time, and the maximum processing capacity is recorded as the maximum processing capacity of the currently enumerated device per unit time. The maximum processing capacity per unit time of each device is summarized in order of device number to form a set of device processing capacities.

[0008] Optionally, the step of obtaining the total processing volume of each drinking water order task within the scheduling period and the total duration of this production scheduling, and forming a schedulable order set and an unschedulable order set based on the shortest processing time estimation results of each order task, specifically includes: Multiple drinking water order tasks are set up within the current scheduling cycle, and each drinking water order task is numbered according to the scheduling order in which it enters the current scheduling cycle. For each numbered order task, obtain the total amount of drinking water that the currently enumerated order task needs to complete within the current scheduling period; Obtain the total time length covered by this production scheduling and use it as the total time length of the current scheduling cycle; For each order task, divide the total processing volume of the current enumerated order tasks by the maximum value of the maximum processing capacity of all devices per unit time to obtain the estimated shortest processing time for the current enumerated order task. For each order task, the shortest processing time estimate of the currently enumerated order task is compared with the total time length of the current scheduling cycle. Order tasks with a shortest processing time estimate that is less than or equal to the total time length of the current scheduling cycle are assigned to the schedulable order set, while order tasks with a shortest processing time estimate that is greater than the total time length of the current scheduling cycle are assigned to the unschedulable order set. When the set of schedulable orders is empty, stop production scheduling within the current scheduling cycle and keep all equipment idle.

[0009] Optionally, the step of constructing a device scheduling strategy for each independent device, forming the target order, continuous processing time, and potential processing volume corresponding to each independent device, and forming the strategy space for each independent device, specifically includes: For each device, a device scheduling strategy consisting of a target order and a continuous processing time is constructed. The target order is selected from the set of schedulable orders, and the continuous processing time is taken from a time range that is greater than zero and does not exceed the total length of the current scheduling cycle. For each device, multiply the maximum processing capacity per unit time of the currently enumerated device by the continuous processing time in the current enumerated device scheduling strategy to obtain the potential processing volume of the currently enumerated device under the current scheduling strategy. For each device, the strategy space corresponding to the current enumerated device is formed by combining each order task in the schedulable order set with each continuous processing time that is greater than zero and does not exceed the total time length of the current scheduling cycle.

[0010] Optionally, the process of generating device selection identifiers for each order task in the schedulable order set, counting the number of selected devices for each order, obtaining the cumulative allocated processing volume for each order, and executing a processing upper limit constraint according to the total processing volume of each order task to form a device selection result after the processing upper limit constraint specifically includes: For each device and each schedulable order task, compare whether the target order in the current enumerated device scheduling strategy is consistent with the current enumerated order task. If they are consistent, set the device selection flag of the current enumerated device for the current enumerated order task to one; if they are inconsistent, set the device selection flag of the current enumerated device for the current enumerated order task to zero. For each schedulable order task, the device selection identifiers of all devices corresponding to the current enumerated order task are accumulated to obtain the number of devices selected for the current enumerated order task in the current round of scheduling; For each schedulable order task, the potential processing volume of all devices whose target orders match the current enumerated order task is summarized to obtain the cumulative allocated processing volume of the current enumerated order task. When the cumulative processing volume of the current enumerated order task is less than or equal to the total processing volume of the current enumerated order task, the device selection flag of the current enumerated order task for all selected devices remains unchanged. When the cumulative processing volume of the current enumerated order task is greater than the total processing volume of the current enumerated order task, select reserved devices in ascending order of device number from all selected devices of the current enumerated order task until the total potential processing volume of the reserved devices is less than or equal to the total processing volume of the current enumerated order task. Under the premise that the total potential processing volume of the reserved devices is less than or equal to the total processing volume of the current enumerated order task, minimize the number of reserved devices and obtain the corresponding set of reserved devices. Keep the device selection identifier of each device in the reserved device set for the current enumerated order task as one, change the device selection identifier of the target order that is consistent with the current enumerated order task but is not included in the reserved device set to zero, and obtain the number of selected devices for the current enumerated order task again to form the device selection result after the upper limit constraint.

[0011] Optionally, the step of forming the activation status and effective processing volume of each independent device based on the device selection results after the processing upper limit constraint, and obtaining the device utility value and the total utility value of the production system, specifically includes: For each device, check if there is an order task with device selection identifier of 1 in the set of available orders for the currently enumerated device. If there is an order task with device selection identifier of 1, set the activation status of the currently enumerated device to 1; if there is no order task with device selection identifier of 1, set the activation status of the currently enumerated device to zero. For each device, multiply the current enumerated device's activation status, maximum processing capacity per unit time, and continuous processing time to obtain the effective processing volume of the current enumerated device; For each device, when the activation state of the current enumerated device is one, the effective processing volume of the current enumerated device is divided by the number of devices selected in the order corresponding to the target order of the current enumerated device to obtain the device utility value of the current enumerated device; when the activation state of the current enumerated device is zero, the device utility value of the current enumerated device is set to zero. The total utility value of the production system under the current scheduling scheme is obtained by summing the utility values ​​of all equipment.

[0012] Optionally, the step of performing a collaborative game-theoretic search based on the equipment utility value to form the final target order and final processing time for each independent device within the scheduling cycle specifically includes: Set the maximum number of iteration rounds to the product of the total number of devices and the number of orders in the schedulable order set, and perform device policy updates in consecutive iteration rounds. In the initial round, for each device, the order task with the smallest total processing volume is selected from the set of schedulable orders as the initial target order for the current enumerated device; when there are multiple order tasks with the same total processing volume, the order task with the smallest order number is selected as the initial target order for the current enumerated device. In the initial round, for each device, the total length of the current scheduling cycle is compared with the total processing volume of the initial target orders of the current enumerated device divided by the maximum processing capacity per unit time of the current enumerated device. The minimum of the two is taken as the initial processing time of the current enumerated device, and the initial target orders and the initial processing time are combined to form the initial scheduling strategy of the current enumerated device. In subsequent iterations, for each device and each schedulable order task, the total time length of the current scheduling cycle is compared with the time obtained by dividing the total processing volume of the current enumerated order tasks by the maximum processing capacity per unit time of the current enumerated device, and the minimum value of the two is taken as the candidate processing time of the current enumerated device for the current enumerated order task. For each device and each schedulable order task, the maximum processing capacity per unit time of the currently enumerated device is multiplied by the candidate processing time to form a candidate processing volume. The candidate processing volume is then divided by one and the sum of the number of devices that selected the current enumerated order task in the previous round to obtain the predicted utility value of the currently enumerated device for the current enumerated order task in the current round. For each device, compare the predicted utility values ​​of all schedulable order tasks for the current enumerated device, and select the order task with the largest predicted utility value as the new target order for the current enumerated device in the current round; when there are multiple order tasks with the same predicted utility value, select the order task with the smallest order number as the new target order for the current enumerated device in the current round, and use the candidate processing time corresponding to the new target order as the new processing time for the current enumerated device in the current round. For each device, the new target order and new processing time for the currently enumerated device in the current round are combined to form the updated scheduling strategy; When a certain round exists that ensures all devices maintain consistent scheduling strategies between the current and next rounds, iteration stops, and the target order and processing time corresponding to the current round are taken as the final target order and final processing time for each device. When all allowed rounds have been completed and there is still no situation where all devices maintain consistent scheduling strategies between adjacent rounds, the target order and processing time corresponding to the maximum iteration round are taken as the final target order and final processing time for each device.

[0013] Optionally, the step of identifying unfinished orders based on the baseline completed volume and remaining demand of each order task, and obtaining the reflow supplementation processing time by combining the remaining available time of the equipment whose final target order is not equal to the corresponding order task within the current scheduling cycle, specifically includes: Using the final target orders and final processing times of each device obtained through collaborative game iterative search as input, and in accordance with the order processing conflict detection and processing upper limit constraint rules, the device selection identifier and the number of selected devices for each order task are re-formed, and the device selection identifier, the number of selected devices for each order, and the final processing time of each device are used to form the benchmark processing result; For each device, the activation state of the current enumerated device is re-established based on the updated device selection identifier, and the effective processing volume of the current enumerated device is obtained. For each schedulable order task, the effective processing volume of all devices for the current enumerated order task and the device selection identifier are summarized to obtain the baseline completion volume of the current enumerated order task under the baseline processing result; For each schedulable order task, the remaining demand for the current enumerated order task is obtained by subtracting the baseline completion volume of the current enumerated order task from the total processing volume of the current enumerated order task. When the remaining demand is greater than zero, the current enumerated order task is determined to be an incomplete order. When the remaining demand is less than or equal to zero, the current enumerated order task is determined to be a completed order. For each incomplete order, filter all devices whose final target order is not equal to the current incomplete order to form a set of devices with different target orders corresponding to the current incomplete order; For each incomplete order, for each device in the set of devices for the opposite target order corresponding to the current incomplete order, when the activation state of the current enumerated device is one, the remaining available time of the current enumerated device in the current scheduling period is obtained by subtracting the final processing time of the current enumerated device from the total time length of the current scheduling period; when the activation state of the current enumerated device is zero, the remaining available time of the current enumerated device in the current scheduling period is taken as the total time length of the current scheduling period. For each incomplete order, if the set of devices for the opposite target orders corresponding to the current incomplete order is not empty and the sum of the maximum processing capacity per unit time of all devices in the set of devices for the opposite target orders corresponding to the current incomplete order is greater than zero, the remaining demand of the current incomplete order is divided by the sum of the maximum processing capacity per unit time of all devices in the set of devices for the opposite target orders corresponding to the current incomplete order to obtain the basic backflow time for the current incomplete order, and the basic backflow time is allocated to each device in the set of devices for the opposite target orders corresponding to the current incomplete order. For each device, the base backflow time allocated to the current enumerated device from all incomplete orders is summed to obtain the total base backflow time of the current enumerated device; When the sum of the base backflow times of the current enumerated devices is greater than zero, the remaining available time of the current enumerated devices is divided by the sum of the base backflow times of the current enumerated devices, and the minimum value between the resulting ratio and one is taken to obtain the time scaling factor of the current enumerated devices; when the sum of the base backflow times of the current enumerated devices is equal to zero, the backflow supplement processing time of the current enumerated devices for all unfinished orders is set to zero. For each incomplete order and each device in the corresponding set of devices for different target orders, the time scaling factor of the currently enumerated device is multiplied by the base backflow time corresponding to the current incomplete order to obtain the backflow supplement processing time of the currently enumerated device for the current incomplete order. When the set of devices for different target orders corresponding to the current incomplete order is empty, or when the sum of the maximum processing capacity per unit time of all devices in the set of devices for different target orders corresponding to the current incomplete order is equal to zero, the backflow supplement processing time of all devices for the current incomplete order is set to zero.

[0014] Optionally, the step of constructing a final scheduling matrix based on the final processing time and the backflow supplementary processing time, sending the final scheduling matrix to the main control unit, and executing the start and stop control of the corresponding order tasks for each independent device according to the final scheduling matrix specifically includes: The final scheduling matrix is ​​constructed with equipment as rows and order tasks as columns, forming the final time allocation result of all equipment to all order tasks in the current scheduling cycle; For each element in the final scheduling matrix, if the order task corresponding to the current matrix element belongs to the set of schedulable orders and, in the baseline processing result, the device selection flag for the order task corresponding to the current matrix element is set to one, then the final processing time of the device corresponding to the current matrix element in the baseline processing result is taken as the current matrix element. If the order task corresponding to the current matrix element belongs to the set of schedulable orders, the device selection flag for the order task corresponding to the current matrix element in the baseline processing result is set to zero, the remaining demand for the order task corresponding to the current matrix element is greater than zero, and the final target order of the device corresponding to the current matrix element is not equal to the order task corresponding to the current matrix element, then the device selection flag for the current matrix element is set to one. The current matrix element is defined as the return processing time of the order task corresponding to each matrix element. The current matrix element is set to zero when: the order task corresponding to the current matrix element belongs to the schedulable order set; the device selection flag for the order task corresponding to the current matrix element in the baseline processing result is zero; the remaining demand for the order task corresponding to the current matrix element is greater than zero; and the final target order of the device corresponding to the current matrix element is equal to the order task corresponding to the current matrix element. The current matrix element is set to zero when: the order task corresponding to the current matrix element belongs to the schedulable order set; and the remaining demand for the order task corresponding to the current matrix element is less than or equal to zero. The current matrix element is set to zero when: the order task corresponding to the current matrix element belongs to the unschedulable order set. The final scheduling matrix is ​​sent to the main control unit as a time instruction set. The main control unit then generates start and stop control signals for each order task for each device and executes the production scheduling of drinking water order tasks according to the time allocation results in the final scheduling matrix.

[0015] A system for implementing the production scheduling optimization control method for the aforementioned automated drinking water equipment includes: The equipment capacity acquisition module assigns a number to each independent device participating in the scheduling, obtains the maximum processing capacity of each independent device within a unit of time, and forms a set of equipment processing capacities. The order task management module obtains the total processing volume of each drinking water order task within the scheduling period and the total duration of this production scheduling, forming a set of schedulable orders and a set of unschedulable orders. The strategy construction and constraint processing module constructs equipment scheduling strategies for each independent device, forming the target order, continuous processing time, potential processing volume, equipment selection identifier, number of selected devices for the order, and equipment selection results after processing upper limit constraints for each independent device; The utility evaluation and iterative scheduling module forms the activation status and effective processing volume of each independent device, obtains the device utility value, performs collaborative game iterative search, and outputs the final target order and final processing time of each independent device. The supplementary scheduling module forms the baseline completion volume and remaining demand for each order task, and obtains the remaining available time and backflow supplementary processing time of the equipment in the current scheduling cycle for the final target order that is not equal to the corresponding order task. The execution control module constructs the final scheduling matrix, sends the final scheduling matrix to the main control unit, and generates start and stop control signals for the corresponding order tasks of each independent device based on the final scheduling matrix.

[0016] The present invention has the following beneficial effects: 1. By numbering each independent device participating in the scheduling and forming a set based on the measured or collected results of the maximum processing capacity per unit time, the problem of ignoring the differences in capabilities among different devices is addressed from the source. Scheduling no longer relies on nominal device specifications or empirical estimates, but instead uses consistent capability data as a common scale for subsequent strategy construction, volume estimation, and time allocation, reducing plan distortion caused by capability deviations. The capability set provides stable input for subsequent actions such as shortest processing time estimation, candidate processing volume, and effective processing volume, improving the consistency of the entire scheduling chain.

[0017] 2. Introducing a pre-screening mechanism for orders within the scheduling cycle boundary: After obtaining the total order processing volume and total scheduling time, a minimum processing time estimate is generated, and orders are divided into a schedulable order set and an unschedulable order set, thereby determining what can and cannot be done in the current cycle. This reduces ineffective scheduling and repeated adjustments: Existing technologies often include all orders in the schedule, only revealing timeouts or resource shortages at the execution stage, leading to frequent plan changes, chaotic equipment start-up and shutdown, and high on-site coordination costs. This solution identifies unachievable orders at the algorithm entry point, ensuring that subsequent strategy searches are only conducted within the feasible space, improving computational efficiency and scheduling stability. Simultaneously, when the schedulable order set is empty, scheduling for the current cycle is directly stopped, and equipment remains idle, avoiding energy waste and the risk of producing semi-finished products due to forcibly starting work that is known to be impossible.

[0018] 3. A scheduling strategy consisting of target orders and continuous processing time is constructed for each device, and the potential processing volume is calculated accordingly, thus forming a strategy space. This expands the traditional single-point allocation method of directly specifying which order a device should handle to a set of options for the device among multiple orders and processing time segments, providing a basis for subsequent collaborative optimization through searching and comparison. Explicitly incorporating the time dimension into the strategy definition avoids the execution uncertainty caused by simply allocating orders without allocating time, a common problem in existing technologies. Binding device capacity to time selection through potential processing volume facilitates subsequent allocation volume calculation, over-allocation detection, and gap identification. The strategy space provides operable discrete choices for collaborative iteration, reducing the practical burden of repeated manual trial scheduling.

[0019] 4. Explicitly encode whether a device is selected for a particular order using a device selection identifier. Over-allocation detection is performed by comparing the number of selected devices in the order with the cumulative allocated processing volume. Finally, a processing upper limit constraint is applied to form the device selection result. This solves the problem of allocated volume exceeding order demand when multiple devices simultaneously target the same order, avoiding resource stacking, ineffective production, or duplicate processing. When over-allocation occurs, reserved devices are selected in ascending order of device number, minimizing the number of reserved devices. This ensures deterministic and consistent constraint processing, reducing instability and disputes caused by manual coordination in existing technologies. Reserved and non-reserved devices are clearly distinguished by the device selection identifier, providing a traceable basis for subsequent activation status, effective processing volume, and utility value calculations.

[0020] 5. Shift the judgment of scheduling quality from experiential perception to calculable utility assessment: Identify equipment involved in production through its activation status, and obtain the effective processing volume by linking activation status with capacity and time, further forming equipment utility values ​​and the total utility value of the production system. This clearly distinguishes situations where equipment is allocated but should not be activated, reducing unnecessary start-ups and shutdowns; linking the effective processing volume with the number of selected equipment in an order reflects the dilution of revenue when multiple people and multiple machines share the same order, prompting scheduling to tend towards a more rational allocation structure; the total system utility value provides a unified metric for comparing different scheduling schemes without relying on subjective human judgment.

[0021] 6. A collaborative game-theoretic iterative search mechanism is introduced, enabling each device to update its strategy based on the predicted utility value within the set of schedulable orders, until the strategies of adjacent rounds remain consistent or the iteration limit is reached, thus forming the final target order and final processing time. This mechanism can automatically form a relatively stable allocation pattern through iteration when multiple devices compete for the same order, reducing the local optima or frequent conflicts caused by relying on single decisions in existing technologies. The candidate processing time is limited by the total scheduling cycle time, order demand, and device capacity, ensuring that each strategy update remains within the executable boundary. When predicted utility values ​​are the same, the rule of minimum order number breaks the tie, making the algorithm output deterministic and reproducible, avoiding the uncontrollable problem of different schedules arising from the same input.

[0022] 7. The initial scheduling results are reconstructed into baseline processing results through conflict detection and upper limit constraints. Then, unfinished orders are identified based on the baseline completed volume and remaining demand. The remaining available time of equipment with different target orders is used to form backflow supplementary processing time, realizing the systematic filling of gaps. The identification of unfinished orders is based on the baseline completed volume, avoiding the uncertainty of judging how much is missing and whether it can be filled based on experience in the existing technology. The calculation of remaining available time takes into account whether the equipment is activated, so that the available time of idle equipment and activated equipment are consistent, which is conducive to fair allocation. Through the layered generation of basic backflow time, time scaling factor and backflow supplementary processing time, the supplementary allocation follows both equipment capacity boundaries and time boundaries, avoiding the practical problems of filling the gaps but overloading the equipment or having time but uneven distribution.

[0023] 8. Construct a final scheduling matrix with equipment as rows and order tasks as columns. The baseline processing time and the backflow supplementary processing time are uniformly expressed in the same matrix and sent to the main control unit to generate start and stop control signals. The scheduling result is upgraded from a text or tabular plan to a structured time instruction set. The main control unit can directly read matrix elements to execute start and stop control, reducing the cost of on-site interpretation and secondary conversion. By assigning matrix elements the final processing time, backflow supplementary processing time, or zero under different conditions, the time allocation boundary for each equipment on each order is clearly defined, avoiding the ambiguity of how long to run and when to switch in the execution phase of existing technologies. The matrix representation naturally supports traceability and auditing, facilitating the identification of baseline allocation problems or backflow supplementary problems when anomalies occur. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the process of the present invention.

[0025] Figure 2 This is a schematic diagram of the process for acquiring and forming capability sets for the device according to the present invention.

[0026] Figure 3 This is a schematic diagram of the order schedulability determination and set partitioning process of the present invention.

[0027] Figure 4 This is a schematic diagram of the upper limit constraint process for selecting identifiers in the device of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example, refer to Figure 1 A method for production scheduling optimization control of automated drinking water equipment, comprising: Each independent device participating in the scheduling is numbered, and the maximum processing capacity of each independent device within a unit of time is obtained to form a set of device processing capacities; Obtain the total processing volume of each drinking water order task within the scheduling period and the total duration of this production scheduling. Form a set of schedulable orders and a set of unschedulable orders based on the estimated shortest processing time of each order task. For each independent device, a device scheduling strategy is constructed to form the target order, continuous processing time and potential processing volume corresponding to each independent device, and to form the strategy space of each independent device; For each order task in the schedulable order set, a device selection identifier is generated, the number of devices selected for each order is counted, the cumulative processing volume allocated to each order is obtained, and the processing upper limit constraint is executed according to the total processing volume of each order task to form the device selection result after the processing upper limit constraint. Based on the equipment selection results after the upper limit constraint, the activation status and effective processing volume of each independent device are formed, and the equipment utility value and the total utility value of the production system are obtained. Based on the equipment utility value, a collaborative game iterative search is performed to form the final target order and final processing time for each independent device within the scheduling cycle; Unfinished orders are identified based on the baseline completed volume and remaining demand of each order task, and the reflow supplementary processing time is obtained by combining the remaining available time of the equipment in the current scheduling cycle when the final target order is not equal to the corresponding order task. The final scheduling matrix is ​​constructed based on the final processing time and the return supplement processing time. The final scheduling matrix is ​​then sent to the main control unit, and the start and stop control of the corresponding order tasks of each independent device is executed according to the final scheduling matrix.

[0030] First, by establishing capability sets for independent devices and filtering orders based on their feasibility within the scheduling cycle, the system reduces the need for repeatedly revising plans due to forcibly including tasks that are clearly impossible to complete within the cycle. Second, by constructing a strategy for each device that includes target orders and continuous processing time, and by combining this with processing upper limit constraints to mitigate the over-allocation problem caused by multiple devices simultaneously targeting the same order, the system makes order demand a hard boundary, avoiding the accumulation of ineffective capacity. Third, by quantitatively evaluating activation status, effective processing volume, and utility value, the system provides a unified standard for evaluating scheduling performance, reducing instability caused by relying solely on experience-based judgments. Finally, by identifying the remaining demand for uncompleted orders and utilizing the remaining available time of devices not directly assigned to those orders to create backflow supplementary processing time, the system issues start-stop control to the main control unit in the form of a final scheduling matrix. This solves the practical problems of difficulty in systematically filling gaps after the initial scheduling and difficulty in directly translating plans into control commands, making the scheduling results more executable, traceable, and stable in scenarios with multiple orders and multiple devices.

[0031] Reference Figure 2 The step of numbering each independent device participating in the scheduling, obtaining the maximum processing capacity of each independent device within a unit of time, and forming a set of device processing capacities specifically includes: Multiple independent devices are deployed on the automated drinking water production line to participate in scheduling, and each independent device is numbered according to the scheduling identification order of the device in the production line. For each numbered device, a standard liquid carrier is used to continuously measure the processing capacity for 1 minute to obtain the maximum processing capacity of the currently enumerated device per unit time, and the maximum processing capacity is recorded as the maximum processing capacity of the currently enumerated device per unit time. The maximum processing capacity per unit time of each device is summarized in order of device number to form a set of device processing capacities.

[0032] Deployment on automated drinking water production lines Each independent device is recorded with its device number as follows: ;in, The total number of independent devices participating in scheduling on an automated drinking water production line; For the first The maximum processing capacity of the equipment per unit time was measured. The test method was to measure the maximum processing capacity using a standard liquid carrier within one minute, and this was recorded as _____. ;in, For the first The maximum processing flow that a single device can achieve per unit time; The processing capabilities of all devices can be represented as a column vector as follows: ;in, This is a column vector representing the maximum processing capacity per unit time of all equipment on an automated drinking water production line.

[0033] Reference Figure 3 The process of obtaining the total processing volume of each drinking water order task within the scheduling period and the total duration of this production scheduling, and forming a schedulable order set and an unschedulable order set based on the shortest processing time estimate of each order task, specifically includes: Multiple drinking water order tasks are set up within the current scheduling cycle, and each drinking water order task is numbered according to the scheduling order in which it enters the current scheduling cycle. For each numbered order task, obtain the total amount of drinking water that the currently enumerated order task needs to complete within the current scheduling period; Obtain the total time length covered by this production scheduling and use it as the total time length of the current scheduling cycle; For each order task, divide the total processing volume of the current enumerated order tasks by the maximum value of the maximum processing capacity of all devices per unit time to obtain the estimated shortest processing time for the current enumerated order task. For each order task, the shortest processing time estimate of the currently enumerated order task is compared with the total time length of the current scheduling cycle. Order tasks with a shortest processing time estimate that is less than or equal to the total time length of the current scheduling cycle are assigned to the schedulable order set, while order tasks with a shortest processing time estimate that is greater than the total time length of the current scheduling cycle are assigned to the unschedulable order set. When the set of schedulable orders is empty, stop production scheduling within the current scheduling cycle and keep all equipment idle.

[0034] Set within the scheduling period For each drinking water order task, record the order number as... ;in, The number of drinking water orders that need to be processed within a given scheduling period; Get the The total amount of drinking water that each order needs to process within this scheduling cycle is denoted as... ; Set the total time duration covered by this production scheduling as follows: ; Calculate orders The shortest processing time estimate is: ;in, To select the device with the highest processing capacity per unit time from all devices and have it work continuously, complete the [number]th [task]. The theoretical minimum processing time required for each order; The schedulable order set and the unschedulable order set are constructed separately as follows: , ;in, For the length of this scheduling cycle The theoretically achievable order index set; For the length of this scheduling cycle The set of order indexes that is theoretically impossible to complete; when During this period, no production scheduling will be carried out, and all equipment will remain idle.

[0035] The process involves constructing separate equipment scheduling strategies for each independent device, resulting in target orders, continuous processing time, and potential processing volume for each device, and forming a strategy space for each independent device. Specifically, this includes: For each device, a device scheduling strategy consisting of a target order and a continuous processing time is constructed. The target order is selected from the set of schedulable orders, and the continuous processing time is taken from a time range that is greater than zero and does not exceed the total length of the current scheduling cycle. For each device, multiply the maximum processing capacity per unit time of the currently enumerated device by the continuous processing time in the current enumerated device scheduling strategy to obtain the potential processing volume of the currently enumerated device under the current scheduling strategy. For each device, the strategy space corresponding to the current enumerated device is formed by combining each order task in the schedulable order set with each continuous processing time that is greater than zero and does not exceed the total time length of the current scheduling cycle.

[0036] For each device, construct its scheduling policy in an ordered pair as follows: ;in, For the first The scheduling strategy for each device within this scheduling cycle; For the first The target order number selected by the equipment during the baseline scheduling phase; For the first The equipment continuously fulfilled orders during this scheduling cycle. Provide the processing time; Calculate the first The potential processing volume of the device under a given strategy is: ;in, In the first Taiwan equipment according to strategy Continuous operation At that time, the maximum volume of drinking water it can process; Construct the first The strategy space of the device is: ;in, For the first For a given device, this is the set of all feasible strategies.

[0037] Reference Figure 4 The process involves generating device selection identifiers for each order task in the schedulable order set, counting the number of selected devices for each order, obtaining the cumulative processing volume allocated to each order, and applying a processing upper limit constraint based on the total processing volume of each order task to form a device selection result after the processing upper limit constraint. Specifically, this includes: For each device and each schedulable order task, compare whether the target order in the current enumerated device scheduling strategy is consistent with the current enumerated order task. If they are consistent, set the device selection flag of the current enumerated device for the current enumerated order task to one; if they are inconsistent, set the device selection flag of the current enumerated device for the current enumerated order task to zero. For each schedulable order task, the device selection identifiers of all devices corresponding to the current enumerated order task are accumulated to obtain the number of devices selected for the current enumerated order task in the current round of scheduling; For each schedulable order task, the potential processing volume of all devices whose target orders match the current enumerated order task is summarized to obtain the cumulative allocated processing volume of the current enumerated order task. When the cumulative processing volume of the current enumerated order task is less than or equal to the total processing volume of the current enumerated order task, the device selection flag of the current enumerated order task for all selected devices remains unchanged. When the cumulative processing volume of the current enumerated order task is greater than the total processing volume of the current enumerated order task, select reserved devices in ascending order of device number from all selected devices of the current enumerated order task until the total potential processing volume of the reserved devices is less than or equal to the total processing volume of the current enumerated order task. Under the premise that the total potential processing volume of the reserved devices is less than or equal to the total processing volume of the current enumerated order task, minimize the number of reserved devices and obtain the corresponding set of reserved devices. Keep the device selection identifier of each device in the reserved device set for the current enumerated order task as one, change the device selection identifier of the target order that is consistent with the current enumerated order task but is not included in the reserved device set to zero, and obtain the number of selected devices for the current enumerated order task again to form the device selection result after the upper limit constraint.

[0038] Construct the indicator variable as follows: ;in, As a binary indicator variable, when the first... Target order number for the equipment Equal to orders The value is 1 if the condition is met, and 0 otherwise. For sets Each order Calculate orders The number of devices selected in the current round of scheduling is: ;in, For orders How many devices will select it simultaneously under the current policy configuration; For each Calculate orders The cumulative volume allocated for processing is: ;in, For the current set of device policies Orders received The total potential processing volume allocated; For each When given a set of device policies When performing upper limit constraint control, the following S401 and S402 rules are used: S401, if Then for all satisfying The equipment maintains the corresponding constant; S402, if Then, in order to satisfy From the set of devices, select a set of indexes in ascending order of device number. , so that: And in all sets of indices that satisfy the above inequality, The number of elements is the smallest. An empty set is allowed; where, For all selected orders The equipment was reserved for actually executing orders. A set of device indexes for processing tasks; For belonging to the set Equipment maintenance , for satisfying And does not belong to a set Device settings: And recalculate after the update: .

[0039] The process of determining the activation status and effective processing volume of each independent device based on the device selection results after the processing upper limit constraint, and obtaining the device utility value and the total utility value of the production system, specifically includes: For each device, check if there is an order task with device selection identifier of 1 in the set of available orders for the currently enumerated device. If there is an order task with device selection identifier of 1, set the activation status of the currently enumerated device to 1; if there is no order task with device selection identifier of 1, set the activation status of the currently enumerated device to zero. For each device, multiply the current enumerated device's activation status, maximum processing capacity per unit time, and continuous processing time to obtain the effective processing volume of the current enumerated device; For each device, when the activation state of the current enumerated device is one, the effective processing volume of the current enumerated device is divided by the number of devices selected in the order corresponding to the target order of the current enumerated device to obtain the device utility value of the current enumerated device; when the activation state of the current enumerated device is zero, the device utility value of the current enumerated device is set to zero. The total utility value of the production system under the current scheduling scheme is obtained by summing the utility values ​​of all equipment.

[0040] Construct a device activation indication function: ;in, For the first Device activation indicator; Calculate the first The effective processing volume of the unit is: ;in, For the first The effective processing volume of the device after considering the active state; For the The device has a utility value of: ;in, For the first The utility value obtained by the device under the current scheduling configuration; Calculate the total utility value of the entire drinking water production system under the current scheduling scheme. Specifically: .

[0041] The step of performing a collaborative game-theoretic search based on equipment utility values ​​to form the final target order and final processing time for each independent device within the scheduling cycle specifically includes: Set the maximum number of iteration rounds to the product of the total number of devices and the number of orders in the schedulable order set, and perform device policy updates in consecutive iteration rounds. In the initial round, for each device, the order task with the smallest total processing volume is selected from the set of schedulable orders as the initial target order for the current enumerated device; when there are multiple order tasks with the same total processing volume, the order task with the smallest order number is selected as the initial target order for the current enumerated device. In the initial round, for each device, the total length of the current scheduling cycle is compared with the total processing volume of the initial target orders of the current enumerated device divided by the maximum processing capacity per unit time of the current enumerated device. The minimum of the two is taken as the initial processing time of the current enumerated device, and the initial target orders and the initial processing time are combined to form the initial scheduling strategy of the current enumerated device. In subsequent iterations, for each device and each schedulable order task, the total time length of the current scheduling cycle is compared with the time obtained by dividing the total processing volume of the current enumerated order tasks by the maximum processing capacity per unit time of the current enumerated device, and the minimum value of the two is taken as the candidate processing time of the current enumerated device for the current enumerated order task. For each device and each schedulable order task, the maximum processing capacity per unit time of the currently enumerated device is multiplied by the candidate processing time to form a candidate processing volume. The candidate processing volume is then divided by one and the sum of the number of devices that selected the current enumerated order task in the previous round to obtain the predicted utility value of the currently enumerated device for the current enumerated order task in the current round. For each device, compare the predicted utility values ​​of all schedulable order tasks for the current enumerated device, and select the order task with the largest predicted utility value as the new target order for the current enumerated device in the current round; when there are multiple order tasks with the same predicted utility value, select the order task with the smallest order number as the new target order for the current enumerated device in the current round, and use the candidate processing time corresponding to the new target order as the new processing time for the current enumerated device in the current round. For each device, the new target order and new processing time for the currently enumerated device in the current round are combined to form the updated scheduling strategy; When a certain round exists that ensures all devices maintain consistent scheduling strategies between the current and next rounds, iteration stops, and the target order and processing time corresponding to the current round are taken as the final target order and final processing time for each device. When all allowed rounds have been completed and there is still no situation where all devices maintain consistent scheduling strategies between adjacent rounds, the target order and processing time corresponding to the maximum iteration round are taken as the final target order and final processing time for each device.

[0042] Set the maximum number of allowed iteration rounds to [value]. The iteration round index is set in the collaborative game strategy update process. ;in, For set The number of elements in the middle; In the initial round Then, execute steps S611 to S613 sequentially: S611, The order number for constructing the initial strategy for each device is: When there are multiple orders make When the same minimum value is obtained, the order with the smallest number is selected as the minimum value. ;in, For the 0th iteration The order number selected for the device; S612, The initial processing time for each device is: ;in, For the 0th iteration The equipment is an order. The length of the allocated processing time; S613, Order No. The complete strategy of the device in iteration round 0 is as follows ; In the In the cycle, for each device And each order The candidate processing time is: ;in, In the first In the first round of evaluation, if the The equipment is entirely used for orders. The length of candidate processing time under scheduling cycle constraints; And based on the indicator variables formed in the previous round of strategy Calculate the utility value of this candidate order: ;in, In the first In the cycle, assume the first Select order for each device And adopt candidate time Predicted utility value at that time; For device indexing; It is a binary indicator variable used to describe the first... In the first iteration, the... Do you want to select an order for this device? ; For each device ,exist The above selection makes The largest order number is denoted as: When there are multiple orders make When the same maximum value is obtained, the order with the smallest number is selected as the maximum value. ;in, In the first In the first iteration, the... The order number for each piece of equipment is selected based on a utility comparison. make ;in, In the first In the first iteration, the... Taiwan equipment is its new option order The allocated processing time; The updated strategy is formed: ;in, For the first The equipment in the first The complete strategy in round iteration; When there is an integer satisfy And for all devices have If the system reaches a Nash equilibrium, then the policy iteration is stopped; where, This is the first round index where the strategy for all devices remains unchanged between two iterations; In this case, for each device make: , ; When for all There are no integers that satisfy the above conditions. At that time, after completing the first After rounds of iteration, for each device make: , ; At this point, the final strategy for each device is: .

[0043] The process of identifying incomplete orders based on the baseline completed volume and remaining demand of each order task, and calculating the reflow supplementation processing time by combining the remaining available time of the equipment whose final target order is not equal to the corresponding order task within the current scheduling cycle, specifically includes: Using the final target orders and final processing times of each device obtained through collaborative game iterative search as input, and in accordance with the order processing conflict detection and processing upper limit constraint rules, the device selection identifier and the number of selected devices for each order task are re-formed, and the device selection identifier, the number of selected devices for each order, and the final processing time of each device are used to form the benchmark processing result; For each device, the activation state of the current enumerated device is re-established based on the updated device selection identifier, and the effective processing volume of the current enumerated device is obtained. For each schedulable order task, the effective processing volume of all devices for the current enumerated order task and the device selection identifier are summarized to obtain the baseline completion volume of the current enumerated order task under the baseline processing result; For each schedulable order task, the remaining demand for the current enumerated order task is obtained by subtracting the baseline completion volume of the current enumerated order task from the total processing volume of the current enumerated order task. When the remaining demand is greater than zero, the current enumerated order task is determined to be an incomplete order. When the remaining demand is less than or equal to zero, the current enumerated order task is determined to be a completed order. For each incomplete order, filter all devices whose final target order is not equal to the current incomplete order to form a set of devices with different target orders corresponding to the current incomplete order; For each incomplete order, for each device in the set of devices for the opposite target order corresponding to the current incomplete order, when the activation state of the current enumerated device is one, the remaining available time of the current enumerated device in the current scheduling period is obtained by subtracting the final processing time of the current enumerated device from the total time length of the current scheduling period; when the activation state of the current enumerated device is zero, the remaining available time of the current enumerated device in the current scheduling period is taken as the total time length of the current scheduling period. For each incomplete order, if the set of devices for the opposite target orders corresponding to the current incomplete order is not empty and the sum of the maximum processing capacity per unit time of all devices in the set of devices for the opposite target orders corresponding to the current incomplete order is greater than zero, the remaining demand of the current incomplete order is divided by the sum of the maximum processing capacity per unit time of all devices in the set of devices for the opposite target orders corresponding to the current incomplete order to obtain the basic backflow time for the current incomplete order, and the basic backflow time is allocated to each device in the set of devices for the opposite target orders corresponding to the current incomplete order. For each device, the base backflow time allocated to the current enumerated device from all incomplete orders is summed to obtain the total base backflow time of the current enumerated device; When the sum of the base backflow times of the current enumerated devices is greater than zero, the remaining available time of the current enumerated devices is divided by the sum of the base backflow times of the current enumerated devices, and the minimum value between the resulting ratio and one is taken to obtain the time scaling factor of the current enumerated devices; when the sum of the base backflow times of the current enumerated devices is equal to zero, the backflow supplement processing time of the current enumerated devices for all unfinished orders is set to zero. For each incomplete order and each device in the corresponding set of devices for different target orders, the time scaling factor of the currently enumerated device is multiplied by the base backflow time corresponding to the current incomplete order to obtain the backflow supplement processing time of the currently enumerated device for the current incomplete order. When the set of devices for different target orders corresponding to the current incomplete order is empty, or when the sum of the maximum processing capacity per unit time of all devices in the set of devices for different target orders corresponding to the current incomplete order is equal to zero, the backflow supplement processing time of all devices for the current incomplete order is set to zero.

[0044] For each The final set of equipment strategies obtained through collaborative game theory As input, calculate the corresponding upper limit constraint control rule. and ; In obtaining the current Then, for each device Calculate the updated activation indicator: ;in, For order indexing; And calculate the updated effective processing volume: ; Then according to and Calculate orders Baseline completed volume: ;in, To determine the order based solely on the execution of the baseline strategy determined by the outcome of the cooperative game. The actual processing volume obtained; For each Calculate the remaining demand: ;in, For the first The remaining unprocessed processing requirements for each order after the baseline scheduling has been completed; like Then the order Incomplete; if Then the order The task is deemed complete; no further allocation will be made. For each Constructing orders that were not involved The set of device indexes for benchmark processing is: ;in, In order to avoid directly allocating equipment resources to orders in the final baseline strategy The set of device indexes; For sets Each device in The remaining available time within the scheduling period is calculated based on its activation indicator: ;in, For the first The remaining time that a device can still use for backflow supplementation processing within the current scheduling cycle after the baseline scheduling is executed; For all satisfied , Non-empty and orders Establish the basic reflux time allocation: ;in, To address orders without considering the time limit for individual devices Residual demand Distribute the data evenly across the set according to unit processing capacity. The base time length allocated to the equipment in the middle; For each device Calculate the total base return time for all unfulfilled orders: ;in, If we disregard equipment time limits and simply distribute the remaining demand from all unfinished orders evenly back to the first [order / process] according to the basic rules... When a device is used, the total return time for that device across all relevant orders; when Build time scaling factor: ;in, In order to target the Time scaling factor for each device; when At that time, no time scaling factor is constructed. And set all corresponding supplementary times to 0; Ultimately, for each satisfied The time required for constructing and supplementing the equipment and order pairs is as follows: ;in, For the first time after considering the remaining time constraint of the equipment The equipment is an order. Additional reflow processing time; when empty or At that time, regarding the order Make all .

[0045] The process of constructing a final scheduling matrix based on the final processing time and the return processing time, distributing the final scheduling matrix to the main control unit, and executing the start and stop control of the corresponding order tasks for each independent device according to the final scheduling matrix specifically includes: The final scheduling matrix is ​​constructed with equipment as rows and order tasks as columns, forming the final time allocation result of all equipment to all order tasks in the current scheduling cycle; For each element in the final scheduling matrix, if the order task corresponding to the current matrix element belongs to the set of schedulable orders and, in the baseline processing result, the device selection flag for the order task corresponding to the current matrix element is set to one, then the final processing time of the device corresponding to the current matrix element in the baseline processing result is taken as the current matrix element. If the order task corresponding to the current matrix element belongs to the set of schedulable orders, the device selection flag for the order task corresponding to the current matrix element in the baseline processing result is set to zero, the remaining demand for the order task corresponding to the current matrix element is greater than zero, and the final target order of the device corresponding to the current matrix element is not equal to the order task corresponding to the current matrix element, then the device selection flag for the current matrix element is set to one. The current matrix element is defined as the return processing time of the order task corresponding to each matrix element. The current matrix element is set to zero when: the order task corresponding to the current matrix element belongs to the schedulable order set; the device selection flag for the order task corresponding to the current matrix element in the baseline processing result is zero; the remaining demand for the order task corresponding to the current matrix element is greater than zero; and the final target order of the device corresponding to the current matrix element is equal to the order task corresponding to the current matrix element. The current matrix element is set to zero when: the order task corresponding to the current matrix element belongs to the schedulable order set; and the remaining demand for the order task corresponding to the current matrix element is less than or equal to zero. The current matrix element is set to zero when: the order task corresponding to the current matrix element belongs to the unschedulable order set. The final scheduling matrix is ​​sent to the main control unit as a time instruction set. The main control unit then generates start and stop control signals for each order task for each device and executes the production scheduling of drinking water order tasks according to the time allocation results in the final scheduling matrix.

[0046] Construct the final scheduling matrix: ;in, This is a matrix representing the final time allocation results for all orders across all devices within this scheduling cycle. Elements in the final scheduling matrix Specifically: ;in, In the final scheduling scheme, the first The equipment is used to process orders during this scheduling cycle. Total time; With matrix As a time instruction set, it is sent to the main control unit, which then generates instructions for each device. The start and stop control signals enable the equipment In the corresponding order Running time is Complete the production scheduling and execution of drinking water orders.

[0047] This embodiment also provides a system for optimizing the production scheduling control of automated drinking water equipment, including: The equipment capacity acquisition module assigns a number to each independent device participating in the scheduling, obtains the maximum processing capacity of each independent device within a unit of time, and forms a set of equipment processing capacities. The order task management module obtains the total processing volume of each drinking water order task within the scheduling period and the total duration of this production scheduling, forming a set of schedulable orders and a set of unschedulable orders. The strategy construction and constraint processing module constructs equipment scheduling strategies for each independent device, forming the target order, continuous processing time, potential processing volume, equipment selection identifier, number of selected devices for the order, and equipment selection results after processing upper limit constraints for each independent device; The utility evaluation and iterative scheduling module forms the activation status and effective processing volume of each independent device, obtains the device utility value, performs collaborative game iterative search, and outputs the final target order and final processing time of each independent device. The supplementary scheduling module forms the baseline completion volume and remaining demand for each order task, and obtains the remaining available time and backflow supplementary processing time of the equipment in the current scheduling cycle for the final target order that is not equal to the corresponding order task. The execution control module constructs the final scheduling matrix, sends the final scheduling matrix to the main control unit, and generates start and stop control signals for the corresponding order tasks of each independent device based on the final scheduling matrix.

[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0049] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing production scheduling control of automated drinking water equipment, characterized in that, include: Each independent device participating in the scheduling is numbered, and the maximum processing capacity of each independent device within a unit of time is obtained to form a set of device processing capacities; Obtain the total processing volume of each drinking water order task within the scheduling period and the total duration of this production scheduling. Form a set of schedulable orders and a set of unschedulable orders based on the estimated shortest processing time of each order task. For each independent device, a device scheduling strategy is constructed to form the target order, continuous processing time and potential processing volume corresponding to each independent device, and to form the strategy space of each independent device; For each order task in the schedulable order set, a device selection identifier is generated, the number of devices selected for each order is counted, the cumulative processing volume allocated to each order is obtained, and the processing upper limit constraint is executed according to the total processing volume of each order task to form the device selection result after the processing upper limit constraint. Based on the equipment selection results after the upper limit constraint, the activation status and effective processing volume of each independent device are formed, and the equipment utility value and the total utility value of the production system are obtained. Based on the equipment utility value, a collaborative game iterative search is performed to form the final target order and final processing time for each independent device within the scheduling cycle; Unfinished orders are identified based on the baseline completed volume and remaining demand of each order task, and the reflow supplementary processing time is obtained by combining the remaining available time of the equipment in the current scheduling cycle when the final target order is not equal to the corresponding order task. The final scheduling matrix is ​​constructed based on the final processing time and the return supplement processing time. The final scheduling matrix is ​​then sent to the main control unit, and the start and stop control of the corresponding order tasks of each independent device is executed according to the final scheduling matrix.

2. The method for production scheduling optimization control of automated drinking water equipment according to claim 1, characterized in that, The process of assigning numbers to each independent device participating in the scheduling, obtaining the maximum processing capacity of each independent device within a unit of time, and forming a set of device processing capacities specifically includes: Multiple independent devices are deployed on the automated drinking water production line to participate in scheduling, and each independent device is numbered according to the scheduling identification order of the device in the production line; For each numbered device, a standard liquid carrier is used to continuously measure the processing capacity for 1 minute to obtain the maximum processing capacity of the currently enumerated device per unit time, and the maximum processing capacity is recorded as the maximum processing capacity of the currently enumerated device per unit time. The maximum processing capacity per unit time of each device is summarized in order of device number to form a set of device processing capacities.

3. The method for production scheduling optimization control of automated drinking water equipment according to claim 2, characterized in that, The process of obtaining the total processing volume of each drinking water order task within the scheduling period and the total duration of this production scheduling, and forming a set of schedulable orders and a set of unschedulable orders based on the estimated shortest processing time of each order task, specifically includes: Multiple drinking water order tasks are set up within the current scheduling cycle, and each drinking water order task is numbered according to the scheduling order in which it enters the current scheduling cycle. For each numbered order task, obtain the total amount of drinking water that the currently enumerated order task needs to complete within the current scheduling period; Obtain the total time length covered by this production scheduling and use it as the total time length of the current scheduling cycle; For each order task, divide the total processing volume of the current enumerated order tasks by the maximum value of the maximum processing capacity of all devices per unit time to obtain the estimated shortest processing time for the current enumerated order task. For each order task, the shortest processing time estimate of the currently enumerated order task is compared with the total time length of the current scheduling cycle. Order tasks with a shortest processing time estimate that is less than or equal to the total time length of the current scheduling cycle are assigned to the schedulable order set, while order tasks with a shortest processing time estimate that is greater than the total time length of the current scheduling cycle are assigned to the unschedulable order set. When the set of schedulable orders is empty, stop production scheduling within the current scheduling cycle and keep all equipment idle.

4. The method for production scheduling optimization control of automated drinking water equipment according to claim 3, characterized in that, The process involves constructing separate equipment scheduling strategies for each independent device, resulting in target orders, continuous processing time, and potential processing volume for each device, and forming a strategy space for each independent device. Specifically, this includes: For each device, a device scheduling strategy consisting of a target order and a continuous processing time is constructed. The target order is selected from the set of schedulable orders, and the continuous processing time is taken from a time range that is greater than zero and does not exceed the total length of the current scheduling cycle. For each device, multiply the maximum processing capacity per unit time of the currently enumerated device by the continuous processing time in the current enumerated device scheduling strategy to obtain the potential processing volume of the currently enumerated device under the current scheduling strategy. For each device, the strategy space corresponding to the current enumerated device is formed by combining each order task in the schedulable order set with each continuous processing time that is greater than zero and does not exceed the total time length of the current scheduling cycle.

5. The method for production scheduling optimization control of automated drinking water equipment according to claim 4, characterized in that, The process involves generating device selection identifiers for each order task in the schedulable order set, counting the number of selected devices for each order, obtaining the cumulative allocated processing volume for each order, and applying a processing upper limit constraint based on the total processing volume of each order task to generate a device selection result after the processing upper limit constraint. Specifically, this includes: For each device and each schedulable order task, compare whether the target order in the current enumerated device scheduling strategy is consistent with the current enumerated order task. If they are consistent, set the device selection flag of the current enumerated device for the current enumerated order task to one; if they are inconsistent, set the device selection flag of the current enumerated device for the current enumerated order task to zero. For each schedulable order task, the device selection identifiers of all devices corresponding to the current enumerated order task are accumulated to obtain the number of devices selected for the current enumerated order task in the current round of scheduling; For each schedulable order task, the potential processing volume of all devices whose target orders match the current enumerated order task is summarized to obtain the cumulative allocated processing volume of the current enumerated order task. When the cumulative processing volume of the current enumerated order task is less than or equal to the total processing volume of the current enumerated order task, the device selection flag of the current enumerated order task for all selected devices remains unchanged. When the cumulative processing volume of the current enumerated order task is greater than the total processing volume of the current enumerated order task, select reserved devices in ascending order of device number from all selected devices of the current enumerated order task until the total potential processing volume of the reserved devices is less than or equal to the total processing volume of the current enumerated order task. Under the premise that the total potential processing volume of the reserved devices is less than or equal to the total processing volume of the current enumerated order task, minimize the number of reserved devices and obtain the corresponding set of reserved devices. Keep the device selection identifier of each device in the reserved device set for the current enumerated order task as one, change the device selection identifier of the target order that is consistent with the current enumerated order task but is not included in the reserved device set to zero, and obtain the number of selected devices for the current enumerated order task again to form the device selection result after the upper limit constraint.

6. The method for production scheduling optimization control of automated drinking water equipment according to claim 5, characterized in that, The process of determining the activation status and effective processing volume of each independent device based on the device selection results after the processing upper limit constraint, and obtaining the device utility value and the total utility value of the production system, specifically includes: For each device, check if there is an order task with device selection identifier of 1 in the set of available orders for the currently enumerated device. If there is an order task with device selection identifier of 1, set the activation status of the currently enumerated device to 1; if there is no order task with device selection identifier of 1, set the activation status of the currently enumerated device to zero. For each device, multiply the current enumerated device's activation status, maximum processing capacity per unit time, and continuous processing time to obtain the effective processing volume of the current enumerated device; For each device, when the activation state of the current enumerated device is one, the effective processing volume of the current enumerated device is divided by the number of devices selected in the order corresponding to the target order of the current enumerated device to obtain the device utility value of the current enumerated device; when the activation state of the current enumerated device is zero, the device utility value of the current enumerated device is set to zero. The total utility value of the production system under the current scheduling scheme is obtained by summing the utility values ​​of all equipment.

7. The method for production scheduling optimization control of automated drinking water equipment according to claim 6, characterized in that, The step of performing a collaborative game-theoretic search based on equipment utility values ​​to form the final target order and final processing time for each independent device within the scheduling cycle specifically includes: Set the maximum number of iteration rounds to the product of the total number of devices and the number of orders in the schedulable order set, and perform device policy updates in consecutive iteration rounds; In the initial round, for each device, the order task with the smallest total processing volume is selected from the set of schedulable orders as the initial target order for the current enumerated device; when there are multiple order tasks with the same total processing volume, the order task with the smallest order number is selected as the initial target order for the current enumerated device. In the initial round, for each device, the total length of the current scheduling cycle is compared with the total processing volume of the initial target orders of the current enumerated device divided by the maximum processing capacity per unit time of the current enumerated device. The minimum of the two is taken as the initial processing time of the current enumerated device, and the initial target orders and the initial processing time are combined to form the initial scheduling strategy of the current enumerated device. In subsequent iterations, for each device and each schedulable order task, the total time length of the current scheduling cycle is compared with the time obtained by dividing the total processing volume of the current enumerated order tasks by the maximum processing capacity per unit time of the current enumerated device, and the minimum value of the two is taken as the candidate processing time of the current enumerated device for the current enumerated order task. For each device and each schedulable order task, the maximum processing capacity per unit time of the currently enumerated device is multiplied by the candidate processing time to form a candidate processing volume. The candidate processing volume is then divided by one and the sum of the number of devices that selected the current enumerated order task in the previous round to obtain the predicted utility value of the currently enumerated device for the current enumerated order task in the current round. For each device, compare the predicted utility values ​​of all schedulable order tasks for the current enumerated device, and select the order task with the largest predicted utility value as the new target order for the current enumerated device in the current round; when there are multiple order tasks with the same predicted utility value, select the order task with the smallest order number as the new target order for the current enumerated device in the current round, and use the candidate processing time corresponding to the new target order as the new processing time for the current enumerated device in the current round. For each device, the new target order and new processing time for the currently enumerated device in the current round are combined to form the updated scheduling strategy; When a certain round exists that ensures all devices maintain consistent scheduling strategies between the current and next rounds, iteration stops, and the target order and processing time corresponding to the current round are taken as the final target order and final processing time for each device. When all allowed rounds have been completed and there is still no situation where all devices maintain consistent scheduling strategies between adjacent rounds, the target order and processing time corresponding to the maximum iteration round are taken as the final target order and final processing time for each device.

8. The method for production scheduling optimization control of automated drinking water equipment according to claim 7, characterized in that, The process of identifying incomplete orders based on the baseline completed volume and remaining demand of each order task, and calculating the reflow supplementation processing time by combining the remaining available time of the equipment whose final target order is not equal to the corresponding order task within the current scheduling cycle, specifically includes: Using the final target orders and final processing times of each device obtained through collaborative game iterative search as input, and in accordance with the order processing conflict detection and processing upper limit constraint rules, the device selection identifier and the number of selected devices for each order task are re-formed, and the device selection identifier, the number of selected devices for each order, and the final processing time of each device are used to form the benchmark processing result; For each device, the activation state of the current enumerated device is re-established based on the updated device selection identifier, and the effective processing volume of the current enumerated device is obtained. For each schedulable order task, the effective processing volume of all devices for the current enumerated order task and the device selection identifier are summarized to obtain the baseline completion volume of the current enumerated order task under the baseline processing result; For each schedulable order task, the remaining demand for the current enumerated order task is obtained by subtracting the baseline completion volume of the current enumerated order task from the total processing volume of the current enumerated order task. When the remaining demand is greater than zero, the current enumerated order task is determined to be an incomplete order. When the remaining demand is less than or equal to zero, the current enumerated order task is determined to be a completed order. For each incomplete order, filter all devices whose final target order is not equal to the current incomplete order to form a set of devices with different target orders corresponding to the current incomplete order; For each incomplete order, for each device in the set of devices for the opposite target order corresponding to the current incomplete order, when the activation state of the current enumerated device is one, the remaining available time of the current enumerated device in the current scheduling period is obtained by subtracting the final processing time of the current enumerated device from the total time length of the current scheduling period; when the activation state of the current enumerated device is zero, the remaining available time of the current enumerated device in the current scheduling period is taken as the total time length of the current scheduling period. For each incomplete order, if the set of devices for the opposite target orders corresponding to the current incomplete order is not empty and the sum of the maximum processing capacity per unit time of all devices in the set of devices for the opposite target orders corresponding to the current incomplete order is greater than zero, the remaining demand of the current incomplete order is divided by the sum of the maximum processing capacity per unit time of all devices in the set of devices for the opposite target orders corresponding to the current incomplete order to obtain the basic backflow time for the current incomplete order, and the basic backflow time is allocated to each device in the set of devices for the opposite target orders corresponding to the current incomplete order. For each device, the base backflow time allocated to the current enumerated device from all incomplete orders is summed to obtain the total base backflow time for the current enumerated device; When the sum of the base backflow times of the current enumerated devices is greater than zero, the remaining available time of the current enumerated devices is divided by the sum of the base backflow times of the current enumerated devices, and the minimum value between the resulting ratio and one is taken to obtain the time scaling factor of the current enumerated devices; when the sum of the base backflow times of the current enumerated devices is equal to zero, the backflow supplement processing time of the current enumerated devices for all unfinished orders is set to zero. For each incomplete order and each device in the corresponding set of devices for different target orders, the time scaling factor of the currently enumerated device is multiplied by the base backflow time corresponding to the current incomplete order to obtain the backflow supplement processing time of the currently enumerated device for the current incomplete order. When the set of devices for different target orders corresponding to the current incomplete order is empty, or when the sum of the maximum processing capacity per unit time of all devices in the set of devices for different target orders corresponding to the current incomplete order is equal to zero, the backflow supplement processing time of all devices for the current incomplete order is set to zero.

9. The method for production scheduling optimization control of automated drinking water equipment according to claim 8, characterized in that, The process of constructing a final scheduling matrix based on the final processing time and the return processing time, distributing the final scheduling matrix to the main control unit, and executing the start and stop control of the corresponding order tasks for each independent device according to the final scheduling matrix specifically includes: The final scheduling matrix is ​​constructed with equipment as rows and order tasks as columns, forming the final time allocation result of all equipment to all order tasks in the current scheduling cycle; For each element in the final scheduling matrix, if the order task corresponding to the current matrix element belongs to the set of schedulable orders and, in the baseline processing result, the device selection flag for the order task corresponding to the current matrix element is set to one, then the final processing time of the device corresponding to the current matrix element in the baseline processing result is taken as the current matrix element. If the order task corresponding to the current matrix element belongs to the set of schedulable orders, the device selection flag for the order task corresponding to the current matrix element in the baseline processing result is set to zero, the remaining demand for the order task corresponding to the current matrix element is greater than zero, and the final target order of the device corresponding to the current matrix element is not equal to the order task corresponding to the current matrix element, then the device selection flag for the current matrix element is set to one. The current matrix element is defined as the return processing time of the order task corresponding to each matrix element. The current matrix element is set to zero when: the order task corresponding to the current matrix element belongs to the schedulable order set; the device selection flag for the order task corresponding to the current matrix element in the baseline processing result is zero; the remaining demand for the order task corresponding to the current matrix element is greater than zero; and the final target order of the device corresponding to the current matrix element is equal to the order task corresponding to the current matrix element. The current matrix element is set to zero when: the order task corresponding to the current matrix element belongs to the schedulable order set; and the remaining demand for the order task corresponding to the current matrix element is less than or equal to zero. The current matrix element is set to zero when: the order task corresponding to the current matrix element belongs to the unschedulable order set. The final scheduling matrix is ​​sent to the main control unit as a time instruction set. The main control unit then generates start and stop control signals for each order task for each device and executes the production scheduling of drinking water order tasks according to the time allocation results in the final scheduling matrix.

10. A system employing the production scheduling optimization control method for automated drinking water equipment as described in claim 9, characterized in that, include: The equipment capacity acquisition module assigns a number to each independent device participating in the scheduling, obtains the maximum processing capacity of each independent device within a unit of time, and forms a set of equipment processing capacities. The order task management module obtains the total processing volume of each drinking water order task within the scheduling period and the total duration of this production scheduling, forming a set of schedulable orders and a set of unschedulable orders. The strategy construction and constraint processing module constructs equipment scheduling strategies for each independent device, forming the target order, continuous processing time, potential processing volume, equipment selection identifier, number of devices selected for the order, and processing upper limit constraints for each independent device; The utility evaluation and iterative scheduling module forms the activation status and effective processing volume of each independent device, obtains the device utility value, performs collaborative game iterative search, and outputs the final target order and final processing time of each independent device. The supplementary scheduling module forms the baseline completion volume and remaining demand for each order task, and obtains the remaining available time and backflow supplement processing time of the equipment in the current scheduling cycle for the final target order that is not equal to the corresponding order task. The execution control module constructs the final scheduling matrix, sends the final scheduling matrix to the main control unit, and generates start and stop control signals for the corresponding order tasks of each independent device based on the final scheduling matrix.

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