Resource scheduling method and device, computer equipment, storage medium and program product

Abstract

The invention relates to a resource scheduling method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring a resource scheduling request initiated by a user, a user satisfaction index and related data of each supply provider, and inputting the resource scheduling request and the related data into a resource demand prediction model to obtain a target resource purchase quantity and target resource purchase time, inputting the target resource purchase quantity, the target resource purchase time, the related data and the user satisfaction index into a strategy initialization module included in the resource scheduling strategy construction model to obtain an initial resource scheduling strategy, and finally inputting the initial resource scheduling strategy into a strategy optimization module included in the resource scheduling strategy construction model to obtain a resource scheduling strategy. And obtaining a target resource scheduling strategy, and performing resource scheduling according to the target resource scheduling strategy. Through the method, reasonable allocation of resources is realized, the scheduling accuracy of resource scheduling is improved, and invalid scheduling of the resources is avoided.

Description

Technical Field

[0001] The present application relates to the technical field of resource scheduling, and in particular to a resource scheduling method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] In terms of resource scheduling, the distribution and allocation of resources are currently mainly arranged based on production plans and inventory levels. Warehouse managers extract resources from inventory and ship them according to the demand notifications from the production department.

[0003] However, the current resource scheduling method has the problem of low resource scheduling accuracy. Summary of the invention

[0004] Based on this, it is necessary to provide a resource scheduling method, apparatus, computer equipment, computer-readable storage medium and computer program product that can improve scheduling accuracy in response to the above technical problems.

[0005] In a first aspect, the present application provides a resource scheduling method, comprising:

[0006] Obtain resource scheduling requests initiated by users, user satisfaction indicators, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data;

[0007] Input resource scheduling requests, supply capacity data, and production plan data into a pre-built resource demand forecasting model to obtain the target resource procurement quantity and target resource procurement time;

[0008] Input the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction index into the policy initialization module included in the pre-built resource scheduling strategy construction model, and obtain the initial resource scheduling strategy through the policy initialization module; the initial resource scheduling strategy includes multiple resource scheduling groups, and the resource scheduling group is used to represent the allocation quantity of any resource at the current production location;

[0009] The initial resource scheduling strategy is input into the strategy optimization module included in the resource scheduling strategy construction model, the target resource scheduling strategy is obtained through the strategy optimization module, and resource scheduling is performed according to the target resource scheduling strategy.

[0010] In one embodiment, the strategy optimization module includes a group selection submodule and a group change submodule;

[0011] The initial resource scheduling strategy is input into the strategy optimization module included in the resource scheduling strategy construction model, and the target resource scheduling strategy is obtained through the strategy optimization module, including:

[0012] Input the initial resource scheduling strategy into the group selection submodule, and obtain the target resource scheduling group through the group selection submodule;

[0013] Input the target resource scheduling group into the group change submodule, perform crossover and mutation operations on the target resource scheduling group, and obtain the changed target resource scheduling group;

[0014] Constructing a current resource scheduling strategy according to the changed target resource scheduling group and other resource scheduling groups except the target resource scheduling group in the initial resource scheduling strategy;

[0015] When the termination judgment result of the current resource scheduling policy meets the preset conditions, the current resource scheduling policy is used as the target resource scheduling policy.

[0016] In one embodiment, taking the current resource scheduling policy as the target resource scheduling policy includes:

[0017] Based on the current resource scheduling strategy and the preset fitness function, obtain the fitness corresponding to the current resource scheduling strategy;

[0018] When the number of group changes of the group change submodule reaches a preset number of iterations, or the fitness reaches a preset fitness threshold, the current resource scheduling strategy is used as the target resource scheduling strategy.

[0019] In one embodiment, the resource demand prediction model is trained by the following steps:

[0020] Obtain historical resource demand data, historical resource scheduling requests, supply capacity data of each supply provider, and production plan data;

[0021] According to the historical resource demand data, historical resource scheduling requests, supply capacity data and production plan data, a feature data training set and a feature data test set are constructed respectively;

[0022] Using the feature data training set, the model parameters of the resource demand prediction model to be trained are trained to obtain the target resource demand prediction model;

[0023] The characteristic data test set is input into the target resource demand prediction model, and the target resource demand prediction model is subjected to a performance test. If the test result of the performance test indicates that the target resource demand prediction model passes the test, the target resource demand prediction model is used as the trained resource demand prediction model.

[0024] In an exemplary embodiment, the model parameters of the resource demand prediction model to be trained are trained using the feature data training set to obtain the target resource demand prediction model, including:

[0025] Inputting the feature data training set into the resource demand prediction model to be trained to obtain first predicted resource demand data corresponding to the historical resource scheduling request;

[0026] Obtaining a corresponding model loss value according to a loss function preset in the resource demand prediction model to be trained, the historical resource demand data included in the feature data training set, and the first predicted resource demand data;

[0027] Based on the model loss value, the back propagation algorithm and the stochastic gradient descent method are used to adjust the model parameters in the resource demand prediction model to be trained accordingly until the loss function converges to obtain the target resource demand prediction model.

[0028] In one embodiment, the resource scheduling request, supply capacity data, and production plan data are input into a pre-built resource demand forecasting model to obtain a target resource procurement quantity and a target resource procurement time, including:

[0029] Inputting resource scheduling request, supply capacity data and production plan data into a resource demand forecasting model, and obtaining second forecast resource demand data through the resource demand forecasting model;

[0030] Obtaining procurement cost data, evaluation information of each supply provider, inventory cost data, and out-of-stock cost data corresponding to the second predicted resource demand data;

[0031] According to the second predicted resource demand data, procurement cost data, evaluation information, inventory cost data and out-of-stock cost data, the target resource procurement quantity and target resource procurement time are obtained.

[0032] In a second aspect, the present application also provides a resource scheduling device, including:

[0033] Data acquisition module, used to obtain resource scheduling requests initiated by users, user satisfaction indicators, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data;

[0034] The data preparation module is used to input resource scheduling requests, supply capacity data and production plan data into the pre-built resource demand forecasting model to obtain the target resource procurement quantity and target resource procurement time;

[0035] A data input module is used to input the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction index into a strategy initialization module included in a pre-built resource scheduling strategy construction model, and obtain an initial resource scheduling strategy through the strategy initialization module; the initial resource scheduling strategy includes multiple resource scheduling groups, and the resource scheduling group is used to represent the allocation quantity of any resource at the current production location;

[0036] The resource scheduling module is used to input the initial resource scheduling strategy into the strategy optimization module included in the resource scheduling strategy construction model, obtain the target resource scheduling strategy through the strategy optimization module, and perform resource scheduling according to the target resource scheduling strategy.

[0037] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0038] Obtain resource scheduling requests initiated by users, user satisfaction indicators, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data;

[0039] Input resource scheduling requests, supply capacity data, and production plan data into a pre-built resource demand forecasting model to obtain the target resource procurement quantity and target resource procurement time;

[0040] Input the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction index into the policy initialization module included in the pre-built resource scheduling strategy construction model, and obtain the initial resource scheduling strategy through the policy initialization module; the initial resource scheduling strategy includes multiple resource scheduling groups, and the resource scheduling group is used to represent the allocation quantity of any resource at the current production location;

[0041] The initial resource scheduling strategy is input into the strategy optimization module included in the resource scheduling strategy construction model, the target resource scheduling strategy is obtained through the strategy optimization module, and resource scheduling is performed according to the target resource scheduling strategy.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0043] Obtain resource scheduling requests initiated by users, user satisfaction indicators, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data;

[0044] Input resource scheduling requests, supply capacity data, and production plan data into a pre-built resource demand forecasting model to obtain the target resource procurement quantity and target resource procurement time;

[0045] Input the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction index into the policy initialization module included in the pre-built resource scheduling strategy construction model, and obtain the initial resource scheduling strategy through the policy initialization module; the initial resource scheduling strategy includes multiple resource scheduling groups, and the resource scheduling group is used to represent the allocation quantity of any resource at the current production location;

[0046] The initial resource scheduling strategy is input into the strategy optimization module included in the resource scheduling strategy construction model, the target resource scheduling strategy is obtained through the strategy optimization module, and resource scheduling is performed according to the target resource scheduling strategy.

[0047] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0048] Obtain resource scheduling requests initiated by users, user satisfaction indicators, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data;

[0049] Input resource scheduling requests, supply capacity data, and production plan data into a pre-built resource demand forecasting model to obtain the target resource procurement quantity and target resource procurement time;

[0050] Input the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction index into the policy initialization module included in the pre-built resource scheduling strategy construction model, and obtain the initial resource scheduling strategy through the policy initialization module; the initial resource scheduling strategy includes multiple resource scheduling groups, and the resource scheduling group is used to represent the allocation quantity of any resource at the current production location;

[0051] The initial resource scheduling strategy is input into the strategy optimization module included in the resource scheduling strategy construction model, the target resource scheduling strategy is obtained through the strategy optimization module, and resource scheduling is performed according to the target resource scheduling strategy.

[0052] The resource scheduling method, apparatus, computer equipment, computer-readable storage medium and computer program product obtain resource scheduling requests initiated by users, user satisfaction indicators, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data, input the resource scheduling requests, supply capacity data and production plan data into a pre-built resource demand prediction model to obtain the target resource procurement quantity and target resource procurement time, and then input the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction indicators into a policy initialization module included in a pre-built resource scheduling strategy construction model, and obtain an initial resource scheduling strategy including multiple resource scheduling groups through the policy initialization module, wherein the resource scheduling group is used to characterize the allocation quantity of any resource at the current production location, and finally input the initial resource scheduling strategy into a policy optimization module included in the resource scheduling strategy construction model, obtain the target resource scheduling strategy through the policy optimization module, and perform resource scheduling according to the target resource scheduling strategy. First, the resource demand prediction model is used to determine the user's current resource procurement quantity and time. The strategy is then initialized based on the determined resource procurement quantity and time, as well as the relevant data of each supplier and user satisfaction indicators. At the same time, the strategy is optimized in real time to improve the rationality and effectiveness of resource allocation of the final resource scheduling strategy, thereby improving the scheduling accuracy of resource scheduling and avoiding ineffective resource scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 An application environment diagram of a resource scheduling method in an embodiment;

[0055] Figure 2 A schematic diagram of a resource scheduling method in one embodiment;

[0056] Figure 3 A schematic diagram of a material procurement strategy optimization model in an embodiment;

[0057] Figure 4 A schematic diagram of a material scheduling strategy optimization model in another embodiment;

[0058] Figure 5 is a structural block diagram of a resource scheduling device in one embodiment;

[0059] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] The resource scheduling method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. Server 104 obtains the resource scheduling request initiated by the user from terminal 102, obtains the user satisfaction index, the supply capacity data of each supply provider, the production plan data, the real-time inventory data and the transportation cost data from the data storage system, inputs the resource scheduling request, the supply capacity data and the production plan data into the pre-built resource demand prediction model, obtains the target resource procurement quantity and the target resource procurement time, and then inputs the target resource procurement quantity, the target procurement time, the production plan data, the real-time inventory data, the transportation cost data and the user satisfaction index into the policy initialization module included in the resource scheduling strategy construction model constructed by Xie Na, and obtains the initial resource scheduling strategy through the policy initialization module, wherein the initial resource scheduling strategy includes multiple resource scheduling groups, and the resource scheduling group is used to characterize the allocation quantity of any resource at the current production location, and finally inputs the initial resource scheduling strategy into the policy optimization module included in the resource scheduling strategy construction model, obtains the target resource scheduling strategy through the policy optimization module, and performs resource scheduling according to the target resource scheduling strategy. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0062] In an exemplary embodiment, Figure 2As shown, a resource scheduling method is provided, which is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S201 to S204. Among them:

[0063] Step S201, obtaining resource scheduling requests initiated by users, user satisfaction indicators, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data.

[0064] Among them, supply capacity data can be understood as the resources or services that can be provided within a specific time, production plan data can be understood as the resource data of the supply provider's subsequent planned production, real-time inventory data can be understood as the real-time stored resource data of the supply provider, and transportation cost data can be understood as the cost data consumed in transporting resources from A to B.

[0065] For example, the server 104 obtains the resource scheduling request initiated by the user through the terminal 102, and can obtain the preset user satisfaction index, the supply capacity data of each supply provider, the production plan data, the real-time inventory data and the transportation cost data from the data storage system. The acquisition of multi-source data lays a solid data foundation for subsequent data preparation and improving the accuracy of the final target resource scheduling strategy.

[0066] Step S202, input the resource scheduling request, supply capacity data and production plan data into a pre-built resource demand forecasting model to obtain a target resource procurement quantity and a target resource procurement time.

[0067] Optionally, the server 104 inputs the resource scheduling request, supply capacity data and production plan data into a pre-built resource demand forecasting model, obtains the corresponding predicted resource demand data through the resource demand forecasting model, and simultaneously calculates the procurement cost data, evaluation information of each supply provider, inventory cost data and out-of-stock cost data corresponding to the predicted resource demand data, and finally obtains the target resource procurement quantity and target resource procurement time according to the predicted resource demand data, procurement cost data, evaluation information, inventory cost data and out-of-stock cost data. The predicted resource demand data, evaluation information of each supply provider and various cost data are used as the basis for determining the resource procurement information, which improves the accuracy of determining the procurement quantity and procurement time, and thus improves the accuracy of determining the resource scheduling strategy.

[0068] Step S203, input the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction index into the policy initialization module included in the pre-built resource scheduling strategy construction model, and obtain the initial resource scheduling strategy through the policy initialization module; the initial resource scheduling strategy includes multiple resource scheduling groups, and the resource scheduling group is used to characterize the allocation quantity of any resource at the current production location.

[0069] Among them, the resource scheduling group can be understood as the allocation quantity of any resource at the current production location.

[0070] Exemplarily, the server 104 inputs the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data, and user satisfaction index into the strategy initialization module included in the pre-built resource scheduling strategy construction model, and encodes and initializes the population: encodes the material scheduling plan using binary encoding or integer encoding. Assuming that n kinds of materials need to be scheduled to m production locations, the allocation quantity of each material at each production location can be used as a gene (i.e., a resource scheduling group), and all genes constitute a chromosome, representing a resource scheduling plan. The initial population is randomly generated, and the population size is determined according to the complexity of the problem and the computing resources, and is generally set to dozens to hundreds of individuals. The resource scheduling plan can be quickly formed by encoding, laying a data foundation for the subsequent construction of the target resource scheduling strategy.

[0071] Step S204: input the initial resource scheduling strategy into the strategy optimization module included in the resource scheduling strategy construction model, obtain the target resource scheduling strategy through the strategy optimization module, and perform resource scheduling according to the target resource scheduling strategy.

[0072] Optionally, the server 104 inputs the initial resource scheduling strategy into the strategy optimization module included in the resource scheduling strategy construction model, optimizes the initial resource scheduling strategy through the strategy optimization module, performs crossover and mutation operations on the resource scheduling group included in the initial resource scheduling strategy until the set iteration end condition is met, and uses the optimized resource scheduling strategy as the target resource scheduling strategy, and finally performs resource scheduling according to the target resource scheduling strategy. By performing crossover and mutation operations on the initial resource scheduling strategy, a better resource allocation scheme can be explored to improve resource utilization efficiency. After multiple rounds of iterative optimization, the generated target resource scheduling strategy can better meet production needs, reduce transportation costs, and improve user satisfaction.

[0073] In the above resource scheduling method, a resource scheduling request initiated by a user, a user satisfaction index, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data are obtained, and the resource scheduling request, supply capacity data and production plan data are input into a pre-built resource demand prediction model to obtain a target resource procurement quantity and a target resource procurement time. Then, the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction index are input into a policy initialization module included in a pre-built resource scheduling strategy construction model. An initial resource scheduling strategy including multiple resource scheduling groups is obtained through the policy initialization module, wherein the resource scheduling group is used to characterize the allocation quantity of any resource at the current production location. Finally, the initial resource scheduling strategy is input into a policy optimization module included in the resource scheduling strategy construction model. The target resource scheduling strategy is obtained through the policy optimization module, and resource scheduling is performed according to the target resource scheduling strategy. First, the resource demand prediction model is used to determine the user's current resource procurement quantity and time. The strategy is then initialized based on the determined resource procurement quantity and time, as well as the relevant data of each supplier and user satisfaction indicators. At the same time, the strategy is optimized in real time to improve the rationality and effectiveness of resource allocation of the final resource scheduling strategy, thereby improving the scheduling accuracy of resource scheduling and avoiding ineffective resource scheduling.

[0074] In one embodiment, the strategy optimization module includes a group selection submodule and a group change submodule;

[0075] The initial resource scheduling strategy is input into the strategy optimization module included in the resource scheduling strategy construction model, and the target resource scheduling strategy is obtained through the strategy optimization module, including: inputting the initial resource scheduling strategy into the group selection submodule, and obtaining the target resource scheduling group through the group selection submodule; inputting the target resource scheduling group into the group change submodule, performing crossover operation and mutation operation on the target resource scheduling group, and obtaining the changed target resource scheduling group; constructing the current resource scheduling strategy according to the changed target resource scheduling group and other resource scheduling groups except the target resource scheduling group in the initial resource scheduling strategy; and taking the current resource scheduling strategy as the target resource scheduling strategy when the termination judgment result of the current resource scheduling strategy meets the preset conditions.

[0076] Optionally, the server 104 inputs the initial resource scheduling strategy into a group selection submodule included in the strategy optimization module, and uses the group selection submodule to perform group selection on the resource scheduling groups included in the initial resource scheduling strategy by methods such as roulette selection and tournament selection to determine the target resource scheduling group to be changed, and then inputs the target resource scheduling group into a group change submodule included in the strategy optimization module, and uses the group change submodule to perform crossover operations and mutation operations on the target resource scheduling group to obtain the changed target resource scheduling group, and finally constructs the current resource scheduling strategy based on the changed target resource scheduling group and other resource scheduling groups in the initial resource scheduling strategy except the target resource scheduling group, and uses the current resource scheduling strategy as the target resource scheduling strategy when the termination judgment result of the current resource scheduling strategy meets the preset conditions.

[0077] The above method can achieve the following technical effects:

[0078] 1. By selecting resource scheduling groups through methods such as roulette selection and tournament selection, it is possible to ensure a balance between diversity and adaptability, thereby effectively locking the target resource scheduling group to be changed.

[0079] 2. Through crossover operations, the excellent characteristics of different resource scheduling groups can be integrated to generate new scheduling combinations, which may bring about a better resource allocation method. The mutation operation introduces randomness, which can increase the diversity of solutions, prevent the algorithm from falling into the local optimum, and help find potential better solutions.

[0080] 3. After optimization through the above method, the target resource scheduling strategy obtained can reduce unnecessary resource waste, improve production efficiency, and thus reduce overall operating costs.

[0081] In one of the embodiments, the current resource scheduling policy is used as the target resource scheduling policy, including: obtaining the fitness corresponding to the current resource scheduling policy based on the current resource scheduling policy and a preset fitness function; when the number of group changes in the group change submodule reaches a preset number of iterations, or the fitness reaches a preset fitness threshold, the current resource scheduling policy is used as the target resource scheduling policy.

[0082] Among them, the fitness function can be understood as designing a fitness function with the goal of minimizing transportation costs, maximizing customer satisfaction, and ensuring timely supply of resources. Taking transportation costs as the main goal, while considering factors such as the timeliness of resource supply and the cost of meeting production needs, the higher the value of the fitness function, the better the scheduling plan.

[0083] Exemplarily, the server 104 obtains the fitness corresponding to the current resource scheduling strategy based on the current resource scheduling strategy and the preset fitness function. If the number of group changes of the group change submodule reaches the preset number of iterations, or the fitness reaches the preset fitness threshold, the continuous optimization of the corresponding current resource scheduling strategy is terminated, and the current resource scheduling strategy is used as the target resource scheduling strategy. Based on the fitness function, the advantages and disadvantages of the current resource scheduling strategy can be quantified, providing a clear basis for the subsequent optimization decision, ensuring that the evaluation of the strategy is more scientific and objective; by setting the number of iterations and the fitness threshold, it is ensured that the optimization process is a dynamic and continuous improvement process. When the conditions are met, the optimization can be terminated in time to avoid unnecessary waste of resources.

[0084] In an exemplary embodiment, the resource demand prediction model is trained by the following steps:

[0085] Obtain historical resource demand data, historical resource scheduling requests, supply capacity data of each supply provider, and production plan data; construct a feature data training set and a feature data test set respectively according to the historical resource demand data, historical resource scheduling requests, supply capacity data, and production plan data; use the feature data training set to train the model parameters of the resource demand prediction model to be trained, and obtain a target resource demand prediction model; input the feature data test set into the target resource demand prediction model, and perform a performance test on the target resource demand prediction model. If the test result of the performance test indicates that the target resource demand prediction model has passed the test, then the target resource demand prediction model is used as the trained resource demand prediction model.

[0086] Optionally, collect multi-source data through Internet of Things technology and internal enterprise information systems, including historical resource demand data, historical resource scheduling requests, supply provider supply capacity data, production plan data, etc. Clean, organize and analyze the collected data to extract key features. Use the Long Short-Term Memory Network (LSTM) algorithm to build a demand forecasting model. Normalize the historical material usage data and map the data to the [0,1] interval to facilitate model training. Construct an LSTM network consisting of an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is determined according to the number of input features. The historical length of the time series data can be used as an input feature, that is, the number of nodes in the input layer is equal to the historical length. The number of nodes in the hidden layer can be determined based on experience or experiments, and is generally set to A, B, etc. The number of nodes in the output layer is 1, indicating the predicted material demand. The preprocessed data is divided into a training set and a test set. Use the training set to train the LSTM model, use the test set to evaluate the trained model, and calculate indicators such as mean squared error (MSE) and mean absolute error (MAE) to measure the prediction accuracy of the model. If the model performance does not meet the requirements, adjust the model parameters or increase the amount of training data and retrain.

[0087] Through the above method, the following technical effects are achieved:

[0088] 1. The LSTM algorithm is good at processing time series data and can capture long-term dependencies in the data, thereby improving the accuracy of material demand forecasts.

[0089] 2. Cleaning and organizing the collected data can remove noise and irrelevant information, extract key features, and ensure the data quality of model training.

[0090] 3. Using indicators such as mean square error (MSE) and mean absolute error (MAE) to evaluate model performance can provide a clear direction for model improvement. When the model performance does not meet the requirements, iterative optimization can be performed by adjusting parameters or increasing the amount of training data to gradually improve the model's predictive ability.

[0091] In one embodiment, a feature data training set is used to train model parameters of a resource demand prediction model to be trained to obtain a target resource demand prediction model, including: inputting the feature data training set into the resource demand prediction model to be trained to obtain first predicted resource demand data corresponding to historical resource scheduling requests; obtaining a corresponding model loss value based on a loss function pre-set in the resource demand prediction model to be trained, the historical resource demand data included in the feature data training set, and the first predicted resource demand data; based on the model loss value, using a back propagation algorithm and a stochastic gradient descent method to adjust the model parameters in the resource demand prediction model to be trained accordingly until the loss function converges to obtain the target resource demand prediction model.

[0092] Among them, the backpropagation algorithm can be understood as an important algorithm in the neural network training process, which is mainly used to calculate the gradients of various parameters in the neural network so as to update these parameters through optimization methods such as gradient descent; the stochastic gradient descent (SGD) can be understood as an algorithm for optimizing machine learning models, which is used to iteratively update the parameters of the model.

[0093] Exemplarily, server 104 inputs the feature data training set into the resource demand prediction model to be trained, obtains the first predicted resource demand data corresponding to the historical resource scheduling request, and obtains the corresponding model loss value based on the loss function pre-set in the resource demand prediction model to be trained, the historical resource demand data contained in the feature data training set, and the first predicted resource demand data. When the model loss value does not meet the loss value threshold of the model training result, the back propagation algorithm and the stochastic gradient descent method are used to adjust the model parameters in the resource demand prediction model to be trained until the loss function converges. The model parameters are no longer updated, and the current resource demand prediction model is used as the target resource demand prediction model.

[0094] Through the above steps, the following technical effects are achieved:

[0095] 1. By continuously adjusting model parameters and based on the feedback mechanism of historical data, the accuracy of resource demand forecasting is improved and the forecast error is reduced.

[0096] 2. The application of back propagation and stochastic gradient descent makes the model training process highly automated, which can quickly adapt to data changes and improve training efficiency.

[0097] 3. Setting the threshold for loss function convergence can effectively control the quality of model training and ensure that the model stops training after reaching the predetermined performance standard to avoid overfitting.

[0098] In one of the embodiments, a resource scheduling request, supply capacity data and production plan data are input into a pre-built resource demand forecasting model to obtain a target resource procurement quantity and a target resource procurement time, including: inputting a resource scheduling request, supply capacity data and production plan data into the resource demand forecasting model, and obtaining second predicted resource demand data through the resource demand forecasting model; obtaining procurement cost data, evaluation information of each supply provider, inventory cost data and out-of-stock cost data corresponding to the second predicted resource demand data; and obtaining the target resource procurement quantity and the target resource procurement time based on the second predicted resource demand data, procurement cost data, evaluation information, inventory cost data and out-of-stock cost data.

[0099] Optionally, combine demand forecast data and supplier evaluation information to develop a material procurement plan. Consider factors such as procurement cost, inventory cost, and out-of-stock cost to determine the optimal procurement quantity and procurement time.

[0100] Cost analysis: Analyze the procurement costs, including the unit price of materials, procurement quantity, transportation costs, procurement fees, etc. Negotiate with suppliers to obtain more favorable prices and procurement terms. Comprehensively consider inventory costs such as storage costs, capital occupation costs, and material losses. Determine a reasonable inventory level based on the inventory cost model. Evaluate the out-of-stock costs, losses caused by production interruptions and delayed delivery due to material shortages.

[0101] Determine the purchase quantity: Based on the demand forecast results and the safety stock level, determine the Economic Order Quantity (EOQ). The EOQ formula is: , where D is the annual demand, S is the cost of each order, and H is the unit inventory holding cost.

[0102] Consider market supply and price fluctuations, and make appropriate adjustments to the purchase quantity. Increase purchase volume when prices are low to reduce purchase costs. Through comprehensive cost analysis, companies can more effectively control purchase and inventory costs, avoid unnecessary expenses, and thus improve overall economic benefits. Using the EOQ model to determine a reasonable purchase quantity helps reduce inventory holding costs, while ensuring the stability of material supply and reducing the risk of out-of-stocks.

[0103] In an exemplary embodiment, a specific implementation of a resource scheduling method is provided, which is as follows:

[0104] Collect multi-source data through IoT technology and the company's internal information system, including historical material (i.e. the aforementioned resources) usage data, market demand data, supplier (i.e. the aforementioned supply provider) supply capacity data, production plan data, etc. Clean, organize and analyze the collected data to extract key features.

[0105] 1. Demand forecasting model (i.e. the aforementioned resource demand forecasting model):

[0106] The demand forecasting model is constructed using the long short-term memory network (LSTM) algorithm. The historical material usage data is normalized and mapped to the [0,1] interval for model training. An LSTM network consisting of an input layer, a hidden layer, and an output layer is constructed. The number of nodes in the input layer is determined by the number of input features. The historical length of the time series data can be used as the input feature, that is, the number of nodes in the input layer is equal to the historical length. The number of nodes in the hidden layer can be determined based on experience or experiments, and is generally set to 64, 128, etc. The number of nodes in the output layer is 1, indicating the predicted material demand. The preprocessed data is divided into a training set and a test set. The LSTM model is trained using the training set, and the model parameters are optimized using the back propagation algorithm and the stochastic gradient descent (SGD) method. During the training process, appropriate parameters such as the learning rate and the number of iterations are set to ensure model convergence. The learning rate can be set to 0.001, and the number of iterations is 1000. The trained model is evaluated using the test set, and indicators such as the mean square error (MSE) and the mean absolute error (MAE) are calculated to measure the prediction accuracy of the model. If the model performance does not meet the requirements, adjust the model parameters or increase the amount of training data and retrain.

[0107] 2. Material procurement optimization strategy:

[0108] like Figure 3 As shown, combine the demand forecast results and supplier evaluation to formulate a material procurement plan. Consider factors such as procurement cost, inventory cost and out-of-stock cost to determine the optimal procurement quantity and procurement time.

[0109] Cost analysis: Analyze the purchase cost, including the unit price of materials, purchase quantity, transportation costs, purchase handling fees, etc. Negotiate with suppliers to obtain more favorable prices and purchase terms. Comprehensively consider inventory costs such as storage costs, capital occupation costs, and material losses. Determine a reasonable inventory level based on the inventory cost model. Evaluate the out-of-stock costs, losses caused by production interruptions and delayed delivery due to material shortages.

[0110] Determine the purchase quantity: Based on the demand forecast results and the safety stock level, determine the economic order quantity (EOQ). The EOQ formula is , where D is the annual demand, S is the cost of each order, and H is the unit inventory holding cost.

[0111] Consider market supply and price fluctuations and make appropriate adjustments to purchase quantities. Increase purchases when prices are low to reduce purchase costs.

[0112] 3. Material dispatch optimization strategy:

[0113] like Figure 4 As shown, intelligent algorithms are used to optimize material scheduling based on procurement time, procurement quantity, production plan, and real-time inventory levels. The material distribution routes and allocation plans are dynamically adjusted to ensure that materials can be supplied to the production site in a timely and accurate manner.

[0114] Encoding and initializing the population: Encode the material scheduling plan using binary or integer encoding. Assume that n types of materials need to be dispatched to m production locations. The allocation quantity of each material at each production location can be used as a gene. All genes form a chromosome, representing a material scheduling plan. The initial population is randomly generated. The population size is determined according to the complexity of the problem and computing resources, and is generally set to dozens to hundreds of individuals.

[0115] Fitness function design: Design the fitness function with the goal of minimizing transportation costs, maximizing customer satisfaction, and ensuring timely supply of materials. Take transportation costs as the main goal, while considering factors such as the timeliness of material supply and the degree of satisfaction of production needs. The higher the value of the fitness function, the better the scheduling plan.

[0116] Selection operation: Use appropriate selection methods, such as roulette selection, tournament selection, etc., to select excellent individuals from the current population to enter the next generation population. The probability of selection is proportional to the fitness value of the individual. The higher the fitness value, the greater the probability of being selected.

[0117] Crossover operation: Perform a crossover operation on the selected individuals to generate new individuals. Common crossover methods include single-point crossover and multi-point crossover. For example, in a single-point crossover, a crossover point is randomly selected, and the parts of the two individuals after the crossover point are exchanged to generate two new individuals.

[0118] Mutation operation: Perform mutation operation on individuals after crossover to increase the diversity of the population. Mutation operation can randomly change the value of certain genes in individuals. Change the value of a gene from 0 to 1, or from 1 to 0 with a certain probability.

[0119] Termination condition judgment: Set the termination condition, such as reaching the maximum number of iterations, the average fitness value of the population reaching a certain threshold, etc. When the termination condition is met, the algorithm stops running and outputs the optimal material scheduling plan.

[0120] Result output and application: Select the individual with the highest fitness value from the final population as the optimal solution, that is, the optimal material scheduling plan. Apply the optimal scheduling plan to actual material scheduling to guide the distribution and allocation of materials.

[0121] Compared with the prior art, this application has the following technical advantages:

[0122] 1. By combining resource forecast data, supplier evaluation information, procurement costs, inventory costs and out-of-stock costs, a procurement plan that is more in line with the actual situation is developed. The plan includes the specific time and quantity of procurement, as well as which supplier to purchase resources from, to help users quickly purchase materials.

[0123] 2. Under the constraints of procurement time, procurement quantity, production plan and real-time inventory level, genetic algorithm is used to initialize the resource scheduling strategy and further optimize the strategy, which improves the accuracy of resource allocation and resource scheduling strategy, avoids invalid resource calls, and saves resource scheduling costs.

[0124] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0125] Based on the same inventive concept, the embodiment of the present application also provides a resource scheduling device for implementing the resource scheduling method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more resource scheduling device embodiments provided below can refer to the limitations on the resource scheduling method above, and will not be repeated here.

[0126] In an exemplary embodiment, Figure 5 As shown, a resource scheduling device is provided, including: a data acquisition module 501, a data preparation module 502, a data input module 503 and a resource scheduling module 504, wherein:

[0127] Data acquisition module 501, used to acquire resource scheduling requests initiated by users, user satisfaction indicators, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data;

[0128] Data preparation module 502, used to input resource scheduling request, supply capacity data and production plan data into a pre-built resource demand forecasting model to obtain target resource procurement quantity and target resource procurement time;

[0129] The data input module 503 is used to input the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction index into the strategy initialization module included in the pre-built resource scheduling strategy construction model, and obtain the initial resource scheduling strategy through the strategy initialization module; the initial resource scheduling strategy includes multiple resource scheduling groups, and the resource scheduling group is used to represent the allocation quantity of any resource at the current production location;

[0130] The resource scheduling module 504 is used to input the initial resource scheduling strategy into the strategy optimization module included in the resource scheduling strategy construction model, obtain the target resource scheduling strategy through the strategy optimization module, and perform resource scheduling according to the target resource scheduling strategy.

[0131] In one embodiment, the policy optimization module includes a group selection submodule and a group change submodule; the resource scheduling module 504 also includes:

[0132] The target resource scheduling group determination submodule is used to input the initial resource scheduling strategy into the group selection submodule, and obtain the target resource scheduling group through the group selection submodule;

[0133] A change submodule, used for inputting the target resource scheduling group into the group change submodule, performing crossover operation and mutation operation on the target resource scheduling group, and obtaining the changed target resource scheduling group;

[0134] The current resource scheduling strategy construction submodule is used to construct the current resource scheduling strategy according to the changed target resource scheduling group and other resource scheduling groups except the target resource scheduling group in the initial resource scheduling strategy;

[0135] The target resource scheduling policy determination submodule is used to use the current resource scheduling policy as the target resource scheduling policy when the termination judgment result of the current resource scheduling policy meets the preset conditions.

[0136] In one of the embodiments, the target resource scheduling strategy determination submodule is also used to obtain the fitness corresponding to the current resource scheduling strategy based on the current resource scheduling strategy and a preset fitness function; when the number of group changes of the group change submodule reaches a preset number of iterations, or the fitness reaches a preset fitness threshold, the current resource scheduling strategy is used as the target resource scheduling strategy.

[0137] In an exemplary embodiment, the resource scheduling device also includes a model training module for obtaining historical resource demand data, historical resource scheduling requests, supply capacity data of each supply provider, and production plan data; constructing a feature data training set and a feature data test set according to the historical resource demand data, historical resource scheduling requests, supply capacity data, and production plan data, respectively; using the feature data training set, training the model parameters of the resource demand prediction model to be trained to obtain a target resource demand prediction model; inputting the feature data test set into the target resource demand prediction model, and performing a performance test on the target resource demand prediction model. If the test result of the performance test indicates that the target resource demand prediction model has passed the test, the target resource demand prediction model is used as the trained resource demand prediction model.

[0138] In one embodiment, the model training module is also used to input the feature data training set into the resource demand prediction model to be trained to obtain the first predicted resource demand data corresponding to the historical resource scheduling request; according to the loss function pre-set in the resource demand prediction model to be trained, the historical resource demand data contained in the feature data training set and the first predicted resource demand data, the corresponding model loss value is obtained; based on the model loss value, the back propagation algorithm and the stochastic gradient descent method are used to adjust the model parameters in the resource demand prediction model to be trained until the loss function converges to obtain the target resource demand prediction model.

[0139] In one of the embodiments, the data preparation module is also used to input resource scheduling requests, supply capacity data and production plan data into a resource demand forecasting model, and obtain second predicted resource demand data through the resource demand forecasting model; obtain procurement cost data, evaluation information of each supply provider, inventory cost data and out-of-stock cost data corresponding to the second predicted resource demand data; and obtain the target resource procurement quantity and target resource procurement time based on the second predicted resource demand data, procurement cost data, evaluation information, inventory cost data and out-of-stock cost data.

[0140] Each module in the resource scheduling device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0141] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store resource scheduling requests, user satisfaction indicators, supply capacity data, production plan data, real-time inventory data, transportation cost data, target resource procurement quantity and target resource procurement time. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a resource scheduling method is implemented.

[0142] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0143] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the resource scheduling method in the above embodiment when executing the computer program.

[0144] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the resource scheduling method in the above embodiment is implemented.

[0145] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the resource scheduling method in the above embodiment is implemented.

[0146] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0147] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0148] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0149] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A resource scheduling method, characterized in that: The method comprises: Obtain resource scheduling requests initiated by users, user satisfaction indicators, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data; Input the resource scheduling request, supply capacity data and production plan data into a pre-built resource demand forecasting model to obtain a target resource procurement quantity and a target resource procurement time; The target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction index are input into a policy initialization module included in a pre-built resource scheduling strategy construction model, and an initial resource scheduling strategy is obtained through the policy initialization module; the initial resource scheduling strategy includes multiple resource scheduling groups, and the resource scheduling group is used to characterize the allocation quantity of any resource at the current production location; The initial resource scheduling strategy is input into the strategy optimization module included in the resource scheduling strategy construction model, the target resource scheduling strategy is obtained through the strategy optimization module, and resource scheduling is performed according to the target resource scheduling strategy.

2. The method according to claim 1, characterized in that The strategy optimization module includes a group selection submodule and a group change submodule; The step of inputting the initial resource scheduling strategy into a strategy optimization module included in the resource scheduling strategy construction model, and obtaining the target resource scheduling strategy through the strategy optimization module, comprises: Inputting the initial resource scheduling strategy into the group selection submodule, and obtaining the target resource scheduling group through the group selection submodule; Inputting the target resource scheduling group into the group change submodule, performing crossover operation and mutation operation on the target resource scheduling group, and obtaining a changed target resource scheduling group; Constructing a current resource scheduling strategy according to the changed target resource scheduling group and other resource scheduling groups in the initial resource scheduling strategy except the target resource scheduling group; When the termination judgment result of the current resource scheduling policy meets a preset condition, the current resource scheduling policy is used as the target resource scheduling policy.

3. The method according to claim 2, characterized in that The taking the current resource scheduling strategy as the target resource scheduling strategy includes: Based on the current resource scheduling strategy and a preset fitness function, obtaining the fitness corresponding to the current resource scheduling strategy; When the number of group changes of the group change submodule reaches a preset number of iterations, or the fitness reaches a preset fitness threshold, the current resource scheduling strategy is used as the target resource scheduling strategy.

4. The method according to claim 1, characterized in that: The resource demand prediction model is trained by the following steps: Obtain historical resource demand data, historical resource scheduling requests, supply capacity data of each supply provider, and production plan data; Constructing a feature data training set and a feature data test set respectively according to the historical resource demand data, the historical resource scheduling request, the supply capacity data and the production plan data; Using the feature data training set, the model parameters of the resource demand prediction model to be trained are trained to obtain a target resource demand prediction model; The characteristic data test set is input into the target resource demand prediction model, and a performance test is performed on the target resource demand prediction model. If the test result of the performance test indicates that the target resource demand prediction model passes the test, the target resource demand prediction model is used as the trained resource demand prediction model.

5. The method according to claim 4, characterized in that The method of using the feature data training set to train the model parameters of the resource demand prediction model to be trained to obtain the target resource demand prediction model includes: Inputting the feature data training set into the resource demand prediction model to be trained to obtain first predicted resource demand data corresponding to the historical resource scheduling request; Obtaining a corresponding model loss value according to a loss function preset in the resource demand prediction model to be trained, the historical resource demand data included in the feature data training set, and the first predicted resource demand data; Based on the model loss value, the back propagation algorithm and the stochastic gradient descent method are used to adjust the model parameters in the resource demand prediction model to be trained accordingly until the loss function converges to obtain the target resource demand prediction model.

6. The method according to claim 1, characterized in that The resource scheduling request, supply capacity data and production plan data are input into a pre-built resource demand forecasting model to obtain a target resource procurement quantity and a target resource procurement time, including: Inputting the resource scheduling request, supply capacity data and production plan data into the resource demand forecasting model, and obtaining second forecast resource demand data through the resource demand forecasting model; Obtaining procurement cost data corresponding to the second predicted resource demand data, evaluation information of each of the supply providers, inventory cost data, and out-of-stock cost data; The target resource procurement quantity and the target resource procurement time are obtained according to the second predicted resource demand data, procurement cost data, evaluation information, inventory cost data and out-of-stock cost data.

7. A resource scheduling device, characterized in that: The device comprises: Data acquisition module, used to obtain resource scheduling requests initiated by users, user satisfaction indicators, supply capacity data of each supply provider, production plan data, real-time inventory data and transportation cost data; A data preparation module, used to input the resource scheduling request, supply capacity data and production plan data into a pre-built resource demand forecasting model to obtain a target resource procurement quantity and a target resource procurement time; A data input module is used to input the target resource procurement quantity, target resource procurement time, production plan data, real-time inventory data, transportation cost data and user satisfaction index into a strategy initialization module included in a pre-built resource scheduling strategy construction model, and obtain an initial resource scheduling strategy through the strategy initialization module; the initial resource scheduling strategy includes multiple resource scheduling groups, and the resource scheduling group is used to characterize the allocation quantity of any resource at the current production location; The resource scheduling module is used to input the initial resource scheduling strategy into the strategy optimization module included in the resource scheduling strategy construction model, obtain the target resource scheduling strategy through the strategy optimization module, and perform resource scheduling according to the target resource scheduling strategy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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