Method, device and equipment for determining order scheduling information, and storage medium

Abstract

The application discloses a kind of order scheduling information determination method, device and equipment and storage medium, belong to computer technical field.The method specifically includes: obtaining environmental parameter information in industrial production and first weight matrix, the environmental parameter information includes order urgency, equipment idle rate and energy price fluctuation rate;Based on the environmental parameter information, using fuzzy logic controller, obtain weight adjustment amount;Based on the weight adjustment amount, the first weight matrix is updated to obtain second weight matrix;Based on the second weight matrix and the preset multi-objective value, using particle swarm optimization algorithm, determine optimized order scheduling information.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to the fields of industrial production scheduling optimization technology and industrial order management technology, and particularly to a method, apparatus, equipment and storage medium for determining order scheduling information. Background Technology

[0002] Currently, multi-objective order scheduling methods in related technologies mainly include order scheduling schemes using weighted summation and order scheduling schemes based on heuristic algorithms.

[0003] The weighted summation method for order scheduling transforms a multi-objective problem into a single-objective problem by assigning fixed weight coefficients to each objective function. However, the determination of these weight coefficients is subjective, making it difficult to quantify objective priorities. In dynamic production environments, fixed weights cannot adapt to real-time changes and lack the ability to respond quickly to disturbances such as sudden orders or equipment failures.

[0004] Order scheduling schemes based on heuristic algorithms, such as NSGA-II, offer multiple non-dominated solutions for selection, but they have high computational complexity and lack the ability to respond to real-time disturbances. When the production environment changes, the entire algorithm needs to be rerun to obtain a new optimal solution, which is often impractical in actual production because production plans need to be frequently adjusted to cope with various unforeseen circumstances. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for determining order scheduling information, which can solve the problem of poor reliability in multi-objective order scheduling optimization in industrial production scheduling. The technical solution is as follows: Firstly, a method for determining order scheduling information is provided, the method comprising: In response to receiving at least one production event information, acquire environmental parameter information in industrial production, or, In response to the received optimization request information, environmental parameter information in industrial production is obtained.

[0006] In one possible implementation, obtaining the weight adjustment amount based on the environmental parameter information using a fuzzy logic controller includes: Based on the environmental parameter information of each production event, a fuzzy logic controller is used to obtain the weight adjustment amount of each production event. Based on the preset adjustment limit and the weight adjustment amount of each production event information, the total weight adjustment amount is obtained.

[0007] In one possible implementation, obtaining the weight adjustment amount based on the environmental parameter information using a fuzzy logic controller includes: The environmental parameter information is standardized to obtain standardized environmental parameter information; Based on the standardized environmental parameter information and fuzzy rule base, the centroid algorithm is used for defuzzification to obtain the weight adjustment amount.

[0008] In one possible implementation, updating the first weight matrix based on the weight adjustment amount to obtain the second weight matrix includes: Based on the weight adjustment amount, the first weight matrix is ​​updated using a smoothing algorithm; Based on the result of the update process, a second weight matrix is ​​obtained.

[0009] In one possible implementation, determining the optimized order scheduling information based on preset multi-objective values ​​and the second weight matrix using a particle swarm optimization algorithm includes: The fitness is calculated based on the preset multi-objective values ​​and the second weight matrix; Based on the fitness, the particle swarm optimization algorithm is used to iteratively optimize the particles in order to determine the optimized order scheduling information.

[0010] Secondly, a device for determining order scheduling information is provided, the device comprising: The acquisition unit is used to acquire environmental parameter information and a first weight matrix in industrial production. The environmental parameter information includes order urgency, equipment idle rate and energy price volatility. The obtaining unit is used to obtain the weight adjustment amount based on the environmental parameter information using a fuzzy logic controller; An update unit is used to update the first weight matrix based on the weight adjustment amount to obtain a second weight matrix; The optimization unit is used to determine the optimized order scheduling information based on preset multi-objective values ​​and the second weight matrix using a particle swarm optimization algorithm.

[0011] Thirdly, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement the aspects and any possible implementations described above.

[0012] Fourthly, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the methods described above and any possible implementations.

[0013] Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the aspects and any possible implementations described above.

[0014] The beneficial effects of the technical solution provided in this application include at least the following: As can be seen from the above technical solution, the embodiments of this application can obtain environmental parameter information and a first weight matrix in industrial production. The environmental parameter information includes order urgency, equipment idle rate, and energy price volatility. Based on the environmental parameter information, a fuzzy logic controller can be used to obtain a weight adjustment amount. Based on the weight adjustment amount, the first weight matrix is ​​updated to obtain a second weight matrix. Based on preset multi-objective values ​​and the second weight matrix, a particle swarm optimization algorithm is used to determine the optimized order scheduling information. Since the weight adjustment amount and an adaptively dynamically adjusted weight matrix are generated by combining the fuzzy logic controller and the particle swarm optimization algorithm according to the changes in real-time environmental parameters such as order urgency, equipment status, and energy prices, and based on the dynamically adjusted weight matrix, the globally optimal scheduling scheme can be quickly searched. This achieves the collaborative optimization and dynamic balance of multiple objectives such as delivery time, cost, and resource utilization, thereby improving the reliability of real-time adjustment and optimization of order scheduling information in production planning.

[0015] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a method for determining order scheduling information according to an embodiment of this application; Figure 2 This is a schematic diagram of a method for determining order scheduling information provided in another embodiment of this application; Figure 3This is a schematic diagram of the application scenario of the method for determining order scheduling information provided in another embodiment of this application; Figure 4 This is a structural block diagram of an order scheduling information determination device provided in another embodiment of this application; Figure 5 This is a block diagram of an electronic device used to implement the method for determining order scheduling information according to embodiments of this application. Detailed Implementation

[0018] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0019] Obviously, the described embodiments are only some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0020] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0021] Please refer to Figure 1 This document illustrates a flowchart of a method for determining order scheduling information according to an embodiment of this application. Specifically, the method for determining order scheduling information may include: Step 101: Obtain environmental parameter information and a first weight matrix in industrial production. The environmental parameter information includes order urgency, equipment idle rate, and energy price volatility.

[0022] Step 102: Based on the environmental parameter information, obtain the weight adjustment amount using a fuzzy logic controller.

[0023] Step 103: Based on the weight adjustment amount, update the first weight matrix to obtain the second weight matrix.

[0024] Step 104: Based on the preset multi-objective values ​​and the second weight matrix, the optimized order scheduling information is determined using the particle swarm optimization algorithm.

[0025] It should be noted that the environmental parameter information may include the current environmental parameter information obtained before each iteration of optimization.

[0026] It should be noted that the first weight matrix may include a pre-set initial weight matrix and an initial weight matrix optimized in each iteration.

[0027] It should be noted that the preset multi-objective values ​​can include objective functions such as minimizing total latency, minimizing resource idle rate, and minimizing energy consumption. The initial weight matrix can be determined based on the weights corresponding to minimizing total latency, minimizing resource idle rate, and minimizing energy consumption.

[0028] In this way, by acquiring environmental parameter information and a first weight matrix from industrial production, including order urgency, equipment idle rate, and energy price volatility, a fuzzy logic controller can be used to obtain weight adjustment amounts based on these environmental parameter information. The first weight matrix is ​​then updated based on these weight adjustment amounts to obtain a second weight matrix. Based on preset multi-objective values ​​and the second weight matrix, a particle swarm optimization algorithm is used to determine optimized order scheduling information. Because by generating weight adjustment amounts and an adaptively dynamically adjusted weight matrix based on real-time changes in environmental parameters such as order urgency, equipment status, and energy prices, combined with the fuzzy logic controller and particle swarm optimization algorithm, and by quickly searching for the globally optimal scheduling scheme based on the dynamically adjusted weight matrix, collaborative optimization and dynamic balance of multiple objectives such as delivery time, cost, and resource utilization can be achieved, thereby improving the reliability of real-time adjustments and optimizations of order scheduling information in production planning.

[0029] Optionally, in one possible implementation of this embodiment, in step 101, environmental parameter information in industrial production can be obtained in response to at least one received production event information, or environmental parameter information in industrial production can be obtained in response to received optimization request information.

[0030] In this implementation, production event information includes events such as emergency order insertion, equipment failure, and delivery date changes.

[0031] In a specific implementation of this method, in step 102, firstly, given at least one received production event information, environmental parameter information for each production event information can be obtained. Secondly, based on the environmental parameter information for each production event information, a fuzzy logic controller can be used to obtain the weight adjustment amount for each production event information. Thirdly, based on a preset adjustment limit and the weight adjustment amount for each production event information, the total weight adjustment amount can be obtained.

[0032] In one specific implementation, after obtaining the total weight adjustment, in step 103, the first weight matrix can be updated based on the total weight adjustment to obtain the second weight matrix.

[0033] In this implementation, the total weight adjustment can be corrected based on preset upper and lower limits.

[0034] For example, the preset upper and lower limits can be ±0.3. The total weight adjustment amount shall not exceed ±0.3 each time.

[0035] In this implementation, the optimization request information can be optimization requests updated according to a preset period. The optimization request information can include iterative optimization requests and optimization requests received at fixed time intervals.

[0036] In this way, weights can be adjusted in a timely manner based on event information or periodic update requests, which can improve the efficiency and response speed of optimizing order scheduling information.

[0037] Optionally, in one possible implementation of this embodiment, in step 102, firstly, the environmental parameter information can be standardized to obtain standardized environmental parameter information. Secondly, based on the standardized environmental parameter information and the fuzzy rule base, a centroid algorithm can be used for defuzzification to obtain the weight adjustment amount.

[0038] In this implementation, environmental parameter information may include order urgency, equipment idle rate, and energy price volatility. Standardized environmental parameter information may include standardized order urgency, standardized equipment idle rate, and standardized energy price volatility.

[0039] In a specific implementation of this approach, the order urgency, equipment idle rate, and energy price volatility are standardized to obtain standardized order urgency, standardized equipment idle rate, and standardized energy price volatility.

[0040] In this implementation, the weight adjustment amount may include a first weight adjustment amount, a second weight adjustment amount, and a third weight adjustment amount.

[0041] In a specific implementation of this approach, based on standardized order urgency, standardized equipment idle rate, and standardized energy price volatility, a fuzzy logic controller is used to obtain the first weight adjustment amount, the second weight adjustment amount, and the third weight adjustment amount.

[0042] In this way, fuzzy control reasoning based on standardized environmental parameter information can be used to obtain more accurate and effective weight adjustment amounts, which can improve the accuracy and reliability of the determined order scheduling information in the future.

[0043] It should be noted that the specific implementation process provided in this embodiment can be combined with various specific implementation processes provided in the aforementioned implementation methods to realize the order scheduling information determination method of this embodiment. Detailed descriptions can be found in the relevant content of the aforementioned implementation methods, and will not be repeated here.

[0044] Optionally, in one possible implementation of this embodiment, in step 103, firstly, the first weight matrix can be updated using a smoothing algorithm based on the weight adjustment amount. Secondly, a second weight matrix can be obtained based on the result of the update process.

[0045] In this implementation, the smoothing algorithm may include a preset smoothing coefficient.

[0046] In a specific implementation of this method, the smoothing coefficient of the smoothing algorithm can be used to smooth the weight adjustment amount, and then the first weight matrix can be updated based on the result of the smoothing process to obtain the second weight matrix.

[0047] In this specific implementation, the first weight matrix may include a first weight, a second weight, and a third weight. The second weight matrix may include the updated first weight, second weight, and third weight.

[0048] Here, the first weight, the second weight, and the third weight can be updated separately to obtain the updated first weight, the second weight, and the third weight, and the second weight matrix can be obtained based on the updated first weight, the second weight, and the third weight.

[0049] In this way, by smoothing the weight adjustment amount and then using the smoothed weight adjustment amount to update the current weight matrix, excessive adjustment can be avoided, thus effectively preventing order scheduling oscillations.

[0050] It should be noted that the specific implementation process provided in this embodiment can be combined with various specific implementation processes provided in the aforementioned implementation methods to realize the order scheduling information determination method of this embodiment. Detailed descriptions can be found in the relevant content of the aforementioned implementation methods, and will not be repeated here.

[0051] Optionally, in one possible implementation of this embodiment, in step 104, firstly, the fitness can be calculated based on preset multi-objective values ​​and the second weight matrix. Based on the fitness, the particles are iteratively optimized using a particle swarm optimization algorithm to determine the optimized order scheduling information.

[0052] In this implementation, the particle swarm optimization algorithm can be an improved particle swarm optimization algorithm (IPSO).

[0053] In one specific implementation of this method, based on the fitness, the particle swarm optimization algorithm is used to iteratively optimize the particles until the preset optimization termination condition is met, thereby obtaining the optimized order scheduling information.

[0054] In this implementation, the preset optimization termination conditions may include the number of iterations reaching the maximum number of iterations threshold or the fitness being less than a preset threshold.

[0055] Understandably, the second weight matrix is ​​used in the fitness function calculation. Fitness can be used to guide the particle search direction.

[0056] In this way, by using the particle swarm optimization algorithm to optimize order scheduling based on the fitness calculated according to the preset multi-objective values ​​and the second weight matrix, order scheduling information that can effectively cope with the current production environment can be obtained, further improving the reliability of order scheduling information and the response efficiency of scheduling optimization.

[0057] It should be noted that the specific implementation process provided in this embodiment can be combined with various specific implementation processes provided in the aforementioned implementation methods to realize the order scheduling information determination method of this embodiment. Detailed descriptions can be found in the relevant content of the aforementioned implementation methods, and will not be repeated here.

[0058] To better understand the methods of the embodiments of this application, the methods of the embodiments of this application will be described below in conjunction with the accompanying drawings and specific application scenarios.

[0059] Figure 2 This is a flowchart illustrating a method for determining order scheduling information according to another embodiment of this application, as shown below. Figure 2 As shown.

[0060] Step 201: In response to at least one received production event information, or, received optimization request information, obtain order urgency, equipment idle rate, energy price volatility, and a first weight matrix.

[0061] In this embodiment, the first weight matrix may include an initial weight matrix and a current weight matrix.

[0062] For example, when constructing a particle swarm optimization algorithm, the particle swarm size N can be preset, for example, N is 20-100; the particle dimension D can be set, which can represent the number of variables in the production scheduling problem; the maximum number of iterations K can be set, for example, K is suggested to be 50-100; the particle position and velocity can be initialized; and the initial weight matrix W0 can be set, for example W0=[0.3,0.3,0.4].

[0063] In this embodiment, order urgency, equipment idle rate, and energy price volatility can be environmental parameters used in industrial production. These environmental parameters can be collected in real time through an ERP / MES system.

[0064] In this embodiment, order urgency can include the urgency of all orders in the order pool, and the urgency of an order can be the ratio of the remaining days of delivery to the total production cycle. Equipment idle rate can be the ratio of the idle time of equipment resources to the total time. Energy price volatility can be the ratio of the current energy price to the historical average price.

[0065] It is understandable that environmental parameter information can be obtained before each iteration during the iterative optimization of particles.

[0066] In this embodiment, production event information includes events such as emergency order insertion, equipment failure, and delivery date changes.

[0067] In this embodiment, the optimization request information can be optimization requests updated according to a preset period. The optimization request information can include optimization requests received at fixed time intervals and iterative optimization requests.

[0068] Understandably, weight adjustments can be triggered immediately upon detecting urgent order insertions, equipment malfunctions, or delivery date changes. Alternatively, weight adjustments can be triggered periodically by updating and optimizing request information according to a preset schedule.

[0069] Understandably, when an event trigger and a periodic update occur simultaneously, the event trigger is processed first, and the periodic update is postponed until the event is processed. If the weights after event processing already meet the optimization requirements, the current periodic update can be skipped. Furthermore, each weight update records a timestamp, and the periodic update checks the time interval since the last update. If the time interval is less than a preset time threshold, such as 5 minutes, the current periodic update is skipped.

[0070] Furthermore, in this embodiment, when there are multiple production events, the weights can be adjusted according to the priority of the events to obtain optimized new order scheduling information.

[0071] For example, priority 1 (highest level): equipment failure event; priority 2: emergency order insertion event; priority 3: delivery date change event. First, each event can be processed sequentially according to priority to obtain the corresponding weight adjustment amount. Second, the total weight adjustment amount for each event is calculated. Here, upper and lower limits for the total weight adjustment amount can be set, for example, a single adjustment should not exceed ±0.3 to prevent excessive oscillation. Third, based on the total weight adjustment amount, the first weight matrix is ​​updated to calculate the second weight matrix. Finally, subsequent optimization steps can be performed to obtain optimized order scheduling information.

[0072] Step 202: Standardize the order urgency, equipment idle rate, and energy price volatility respectively to obtain standardized order urgency, standardized equipment idle rate, and standardized energy price volatility.

[0073] In this embodiment, preferably, the order urgency can be standardized using an entropy algorithm to obtain a standardized order urgency, as shown in formula (1): (1) in, For order i, the standardized order urgency. Let i be the number of days remaining for order i. and These are the minimum and maximum remaining days for all orders in the order pool, respectively, serving as the baseline range for normalization.

[0074] Understandably, when the overall remaining days in the order pool are relatively long, that is... When the number is large, even if the remaining days of a certain order are... The longer the remaining time, the higher the urgency level; when the overall remaining days in the order pool are short, and When they are both small, The urgency of an order may also be relatively low. Here, by standardizing the urgency of each order in the order pool, the urgency of an order can be made relative, which can reflect the urgency of the order in the entire production task. and It can be used to establish a benchmark for comparison of relative urgency.

[0075] In this embodiment, preferably, the equipment idle rate can be linearly normalized to obtain a standardized equipment idle rate, as shown in formula (2): (2) in, The standardized equipment idle rate for order m. is the idle time of device m, is the total time of device m.

[0076] It can be understood that by performing linear normalization processing on the equipment idle rate, standardization can be achieved. A larger standardized equipment idle rate indicates that the equipment needs to be used with higher priority. An enterprise aims to maximize equipment utilization, so the busier the equipment is, the higher the priority for work dispatching. When the equipment idle time is larger, is smaller (close to 0), indicating that the equipment has a light load and does not need to be arranged with priority; when the equipment idle time is smaller, is larger (close to 1), indicating that the equipment has a heavy load and tasks need to be arranged with priority. The standardized equipment idle rate can characterize the equipment load rate, and a larger standardized equipment idle rate can indicate that the equipment is busier and needs to be scheduled with higher priority.

[0077] In this embodiment, preferably, logarithmic transformation standardization processing can be performed on the energy price volatility to obtain the standardized energy price volatility, which can be specifically shown in formula (3): (3) wherein, is the standardized energy price volatility, is the current energy price, is the historical average energy price.

[0078] Step 203: Based on the standardized order urgency, the standardized equipment idle rate and the standardized energy price volatility, a fuzzy logic controller is used to obtain a first weight adjustment amount, a second weight adjustment amount and a third weight adjustment amount.

[0079] In this embodiment, the standardized order urgency, the standardized equipment idle rate and the standardized energy price volatility can adopt triangular membership functions, and the first weight adjustment amount, the second weight adjustment amount and the third weight adjustment amount can adopt the center of gravity method for defuzzification.

[0080] In this embodiment, the Fuzzy Logic Controller (FLC) may include a fuzzy rule base.

[0081] Exemplarily, part of the information of the fuzzy rule base can be shown as follows: IF E=high AND R=low THEN = increase; IF E=medium AND R=medium THEN = maintain; IF E=low AND P=high THEN = increase; IF R=HIGH THEN =Increase; IF equipment failure THEN =A significant increase; It should be noted that here, E can represent order urgency, R can represent equipment idle rate, and P can represent energy price volatility. The amount that can be adjusted for the first weight. The amount that can be adjusted for the second weight. This can be used to adjust the third weight.

[0082] It is understandable that the weight adjustment amount is obtained through FLC fuzzy logic reasoning based on the current environmental parameters. , , .

[0083] Step 204: Based on the first weight matrix, the first weight adjustment, the second weight adjustment, and the third weight adjustment, obtain the second weight matrix using a smoothing algorithm.

[0084] In this embodiment, the first weight matrix may include the current first weight, second weight, and third weight.

[0085] In this embodiment, the smoothing coefficient of the smoothing algorithm can be used to smooth the first weight adjustment, the second weight adjustment, and the third weight adjustment. Then, based on the result of the smoothing and the first weight matrix, an updated second weight matrix is ​​obtained.

[0086] For example, based on the first weight matrix, the first weight adjustment, the second weight adjustment, and the third weight adjustment, the second weight matrix can be obtained using a smoothing algorithm, as shown in formula (4): (4) in, It can be the second weight matrix. This can be the first weight matrix. You can adjust the weight. This can be a smoothing coefficient, which can be an exponential decay function. .here, For example, it could be .

[0087] In this way, the smoothing coefficient can ensure the smoothness of the weight adjustment, so as to avoid scheduling oscillations caused by sudden changes.

[0088] Step 205: Calculate particle fitness based on the preset target value and the second weight matrix.

[0089] In this embodiment, the preset target values ​​may include minimizing total latency, minimizing resource idle rate, and minimizing energy consumption. The second weight matrix includes a fourth weight, a fifth weight, and a sixth weight.

[0090] It is understandable that the fourth weight can be the updated first weight. The fifth weight can be the updated second weight. The sixth weight can be the updated third weight.

[0091] In this embodiment, for example, the particle fitness is calculated based on a preset target value and a second weight matrix, as shown in formula (5): (5) in, It is possible, It can be the second weight matrix. To minimize the total latency, This can help minimize resource idle rate. This can help minimize energy consumption.

[0092] Understandably, the updated Used in fitness function calculations to guide particle search direction.

[0093] Step 206: Based on fitness, use the particle swarm optimization algorithm to iteratively optimize the particles until the preset optimization termination condition is met, and obtain the optimized order scheduling information.

[0094] In this embodiment, the preset optimization termination condition may include the number of iterations reaching a maximum iteration threshold or the fitness being less than a preset threshold.

[0095] In this embodiment, the particle velocity is updated. It can be as shown in formula (6): (6) in, Let i be the position of particle i in the d-th dimension during the k-th iteration. Let be the individual best position of particle i in the d-th dimension. This represents the globally optimal position of the entire particle swarm in the d-th dimension. The preset inertial weights can control the degree to which particles maintain their original speed. , It is a preset learning factor, or acceleration coefficient, which can control the step size of the particle's learning towards the individual optimum and the global optimum, respectively. It is a random number.

[0096] Update particle position It can be shown in formula (7): (7) in, Let i be the position of particle i in the d-th dimension during the k-th iteration. To update particle velocity.

[0097] Understandably, by using the particle swarm optimization algorithm, the individual optimal pbest and the group optimal gbest are updated until the preset optimization termination condition is met, thus obtaining the optimized order scheduling information.

[0098] In this embodiment, Figure 3 This is a schematic diagram illustrating the architecture of an application scenario for a method for determining order scheduling information provided in another embodiment of this application, as shown below. Figure 3 As shown. The method for determining the order scheduling information can be based on the PSO+FLC multi-objective optimization algorithm framework. First, the weight matrix can be initialized. Based on setting the particle swarm size N, particle dimension D, maximum number of iterations K, particle position and velocity, the initial weight matrix W0=[ , , Secondly, based on periodic triggers (regular iterations) and event triggers (emergency order insertion, equipment failure, or delivery date changes, etc.), external environmental parameter information is collected for fuzzy logic reasoning. Based on the current environmental parameter information, the weight adjustment amount is obtained through FLC reasoning. , , Next, update the weight matrix: Next, the fitness is calculated based on the updated weight matrix. Then, particle swarm optimization is performed until the number of iterations meets the maximum number of iterations K, or the fitness value is less than a preset threshold. At this point, the optimization can be terminated, and the optimized order scheduling information can be obtained.

[0099] In this embodiment, taking the production scheduling of power tools as an example, a power tool manufacturing company uses the dynamic weight matrix mechanism in this embodiment to optimize the production scheduling of its injection molding workshop. This workshop has 50 injection molding machines and processes more than 200 customer orders daily. The scheduling system needs to simultaneously balance three major objectives: order delivery fulfillment rate, equipment resource utilization, and energy cost control (corresponding to weighting factors respectively). , , ).

[0100] For urgent order insertions, when receiving an urgent order for 500 drill housings with a delivery time requirement of 4 hours, an event-driven update of the preset target value's corresponding weight can be immediately triggered. In this embodiment, the weight update method calculates the new weight. The weight corresponding to the delivery time... The resource idle rate has been increased from 0.4 to 0.7 to prioritize the production of urgent orders. The weight corresponding to the resource idle rate is... The energy consumption threshold was reduced from 0.4 to 0.2, allowing some equipment to remain temporarily idle to accommodate rush orders. (Energy consumption weighting) The energy consumption per unit was reduced from 0.2 to 0.1, allowing for higher energy consumption in short-term tasks to buy time. Based on the updated weights, optimization was performed, enabling rescheduling within 3 minutes. Urgent orders were allocated to 3 idle devices, and some non-critical tasks were paused to ensure on-time order delivery.

[0101] In the event of a sudden equipment failure, such as a large injection molding machine shutting down due to spindle overheating, affecting the production schedule of 10 orders for the day, the new weights, including the resource idle rate weight, can be calculated using the weight update method of a fuzzy controller as described in this embodiment after receiving the equipment failure information. The efficiency was increased from 0.3 to 0.6, prioritizing the transfer of tasks from faulty equipment to other equipment to reduce overall resource idleness; the delivery option weight was increased. The energy consumption weight has been slightly adjusted from 0.5 to 0.3, appropriately relaxing the delivery time restrictions for non-urgent orders; Maintaining a weight of 0.1 is considered acceptable, as energy consumption is not a core concern during fault repair. Based on the updated weights, a particle swarm optimization algorithm is used for optimization to obtain new order scheduling information. Tasks from the faulty equipment can be automatically diverted to five backup devices, and production capacity loss can be kept below 5% by dynamically adjusting the production rhythm.

[0102] In the event of sudden changes in energy prices, when a peak electricity price warning is issued by the power grid and the electricity price increases by 50%, the new weights, namely the energy consumption weights, can be calculated using the weight update method of the fuzzy controller in this embodiment. Increasing the efficiency from 0.2 to 0.5 allows for the relocation of energy-intensive processes to periods of lower energy cost; delivery option weighting The weighting for resource idle rate was reduced from 0.6 to 0.3, allowing for slight delays in some orders to avoid peak electricity demand; The weight was adjusted from 0.2 to 0.2 and kept stable. Based on the updated weights, a particle swarm optimization algorithm was used for optimization to obtain new order scheduling information. During peak electricity price periods, the system can automatically postpone the heating start-up plan of 8 injection molding machines, saving approximately 15% in energy costs.

[0103] For periodic updates, incremental optimization based on historical data can be implemented, with weight fine-tuning performed hourly. This is combined with feedback optimization based on the actual scheduling effects of the most recent 50 order batches, such as delivery delay rates, average equipment idle time, and energy consumption per unit output. Here, an exponentially decaying moving average algorithm is used to avoid sudden weight changes caused by a single abnormal data point. For example, if the delivery delay rate is detected to be below 5% for three consecutive times, the weight is gradually reduced. Each adjustment increment is ≤0.05; through the fuzzy logic controller, the weights can be slightly adjusted based on environmental parameters such as real-time temperature and humidity in the workshop and equipment idle rate. For example, the weights can be slightly increased in hot weather. This is to prevent the equipment from overheating.

[0104] Furthermore, the pre-configured initial weight matrix can be adjusted based on peak and off-peak seasons. For example, during the peak season (October-December) for overseas Christmas stocking, order volume surges to an average of 300 orders per day, significantly increasing delivery pressure. The pre-configured initial weight matrix can be adjusted to W=[0.6,0.2,0.2], prioritizing delivery speed. During the off-peak season (February-April), order volume drops to an average of 80 orders per day, making resource idle costs the primary concern. The pre-configured initial weight matrix can be adjusted to W=[0.2,0.5,0.3], focusing on optimizing equipment utilization and energy consumption balance. Alternatively, the weight matrix used to calculate fitness in the particle swarm optimization algorithm can be directly set based on peak and off-peak seasons.

[0105] Thus, by adopting the scheme in this embodiment, a dynamic weight matrix can be constructed to minimize the total delay time, the resource idle rate, and the energy consumption. The weight matrix can be dynamically adjusted based on real-time sensing of production environment parameters. By combining the particle swarm optimization algorithm and the fuzzy logic controller, multi-objective optimization solutions can be iteratively generated to achieve intelligent optimization of multi-objective conflict problems such as delivery time, cost, and resource utilization. The weights can be automatically adjusted according to changes in the production environment to achieve collaborative optimization of multiple objectives.

[0106] Furthermore, by adopting the scheme in this embodiment, the weight matrix corresponding to the preset target value can be adjusted in real time according to the production environment parameters, avoiding the subjectivity and poor adaptability problems caused by fixed weights in the traditional weighted method.

[0107] Furthermore, the scheme in this embodiment achieves a smooth transition of weights through a fuzzy logic controller, avoiding scheduling oscillations caused by sudden changes, while combining it with a particle swarm optimization algorithm to quickly generate adaptive solutions.

[0108] Furthermore, by adopting the scheme in this embodiment, the weight matrix can respond promptly to changes in the production environment through a dual-path weight update mechanism that combines event-driven and periodic updates.

[0109] Furthermore, by adopting the scheme in this embodiment, the obtained environmental parameters such as order urgency, resource idle rate, and energy price volatility are standardized, quantified, and normalized, so that the environmental parameters can objectively and quantitatively reflect the production status.

[0110] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0111] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0112] Figure 4 This invention provides a structural block diagram of an order scheduling information determination device according to an embodiment of the present application. Figure 4 As shown. The order scheduling information determination device 400 of this embodiment may include an acquisition unit 401, a obtaining unit 402, an updating unit 403, and an optimization unit 404. The acquisition unit 401 is used to acquire environmental parameter information and a first weight matrix in industrial production. The environmental parameter information includes order urgency, equipment idle rate, and energy price volatility. The obtaining unit 402 is used to obtain weight adjustment amounts based on the environmental parameter information using a fuzzy logic controller. The updating unit 403 is used to update the first weight matrix based on the weight adjustment amounts to obtain a second weight matrix. The optimization unit 404 is used to determine optimized order scheduling information based on preset multi-objective values ​​and the second weight matrix using a particle swarm optimization algorithm.

[0113] Optionally, in one possible implementation of this embodiment, the acquisition unit 401 is used to acquire environmental parameter information in industrial production in response to at least one received production event information, or to acquire environmental parameter information in industrial production in response to received optimization request information.

[0114] Optionally, in one possible implementation of this embodiment, the obtaining unit 402 is used to obtain the weight adjustment amount of each production event information based on the environmental parameter information of each production event information using a fuzzy logic controller; and to obtain the total weight adjustment amount based on a preset adjustment limit and the weight adjustment amount of each production event information.

[0115] Optionally, in one possible implementation of this embodiment, the obtaining unit 402 is used to standardize the environmental parameter information to obtain standardized environmental parameter information; based on the standardized environmental parameter information and the fuzzy rule base, the centroid algorithm is used to perform defuzzification processing to obtain the weight adjustment amount.

[0116] Optionally, in one possible implementation of this embodiment, the update unit 403 is used to update the first weight matrix based on the weight adjustment amount using a smoothing algorithm; and to obtain the second weight matrix based on the result of the update process.

[0117] Optionally, in one possible implementation of this embodiment, the optimization unit 404 is used to calculate the fitness based on the preset multi-objective values ​​and the second weight matrix; based on the fitness, the particle swarm optimization algorithm is used to perform iterative optimization processing on the particles to determine the optimized order scheduling information.

[0118] In this embodiment, an acquisition unit can acquire environmental parameter information and a first weight matrix from industrial production. The environmental parameter information includes order urgency, equipment idle rate, and energy price volatility. Based on the environmental parameter information, the acquisition unit uses a fuzzy logic controller to obtain weight adjustment amounts. The update unit updates the first weight matrix based on the weight adjustment amounts to obtain a second weight matrix. The optimization unit uses a particle swarm optimization algorithm based on preset multi-objective values ​​and the second weight matrix to determine optimized order scheduling information. By generating weight adjustment amounts and adaptively dynamically adjusted weight matrices based on real-time changes in environmental parameters such as order urgency, equipment status, and energy prices, combined with a fuzzy logic controller and particle swarm optimization algorithm, and quickly searching for the globally optimal scheduling scheme based on the dynamically adjusted weight matrix, collaborative optimization and dynamic balance of multiple objectives such as delivery time, cost, and resource utilization can be achieved, thereby improving the reliability of real-time adjustment and optimization of order scheduling information in production planning.

[0119] The technical solution of this application involves the collection, storage, use, processing, transmission, provision, and disclosure of user personal information, such as user image and attribute data, which comply with relevant laws and regulations and do not violate public order and good morals.

[0120] According to embodiments of this application, this application also provides an electronic device, a readable storage medium, and a computer program product.

[0121] Figure 5A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of this application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0122] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0123] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0124] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the method for determining order scheduling information. For example, in some embodiments, the method for determining order scheduling information may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the method for determining order scheduling information described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method for determining order scheduling information by any other suitable means (e.g., by means of firmware).

[0125] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0127] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0128] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0129] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0130] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0131] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0132] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for determining order scheduling information, characterized in that, The method includes: Acquire environmental parameter information and a first weight matrix in industrial production, wherein the environmental parameter information includes order urgency, equipment idle rate and energy price volatility; Based on the environmental parameter information, a fuzzy logic controller is used to obtain the weight adjustment amount; Based on the weight adjustment amount, the first weight matrix is ​​updated to obtain the second weight matrix; Based on the preset multi-objective values ​​and the second weight matrix, the optimized order scheduling information is determined using the particle swarm optimization algorithm.

2. The method according to claim 1, characterized in that, The acquisition of environmental parameter information in industrial production includes: In response to receiving at least one production event information, acquire environmental parameter information in industrial production, or, In response to the received optimization request information, environmental parameter information in industrial production is obtained.

3. The method according to claim 2, characterized in that, The step of obtaining the weight adjustment amount based on the environmental parameter information using a fuzzy logic controller includes: Based on the environmental parameter information of each production event, a fuzzy logic controller is used to obtain the weight adjustment amount of each production event. Based on the preset adjustment limit and the weight adjustment amount of each production event information, the total weight adjustment amount is obtained.

4. The method according to claim 1, characterized in that, The step of obtaining the weight adjustment amount based on the environmental parameter information using a fuzzy logic controller includes: The environmental parameter information is standardized to obtain standardized environmental parameter information; Based on the standardized environmental parameter information and fuzzy rule base, the centroid algorithm is used for defuzzification to obtain the weight adjustment amount.

5. The method according to claim 1, characterized in that, The step of updating the first weight matrix based on the weight adjustment amount to obtain the second weight matrix includes: Based on the weight adjustment amount, the first weight matrix is ​​updated using a smoothing algorithm; Based on the result of the update process, a second weight matrix is ​​obtained.

6. The method according to any one of claims 1-5, characterized in that, The step of determining the optimized order scheduling information based on preset multi-objective values ​​and the second weight matrix using a particle swarm optimization algorithm includes: The fitness is calculated based on the preset multi-objective values ​​and the second weight matrix; Based on the fitness, the particle swarm optimization algorithm is used to iteratively optimize the particles in order to determine the optimized order scheduling information.

7. A device for determining order scheduling information, characterized in that, The device includes: The acquisition unit is used to acquire environmental parameter information and a first weight matrix in industrial production. The environmental parameter information includes order urgency, equipment idle rate and energy price volatility. The obtaining unit is used to obtain the weight adjustment amount based on the environmental parameter information using a fuzzy logic controller; An update unit is used to update the first weight matrix based on the weight adjustment amount to obtain a second weight matrix; The optimization unit is used to determine the optimized order scheduling information based on preset multi-objective values ​​and the second weight matrix using a particle swarm optimization algorithm.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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