A method and system for pre-scheduling material support

Abstract

The present application belongs to the field of material support operation, and particularly relates to a material support pre-scheduling method and system. The method comprises: in the process of material scheduling, when a risk event occurs, the real-time value of the system situation assessment characteristic parameter is input into the trained system situation assessment neural network according to the set period to obtain the predicted total delay time length of the system operation completion; if the total delay time length is greater than the pre-scheduling threshold, the pre-scheduling scheme generation is triggered; the system situation assessment neural network is trained by the system situation assessment characteristic parameter value and the corresponding total delay time length in the training set; including the current number of warehouses in the system, warehouse inventory information, task demand information, resource state information, warehouse operation state information, current operation completion rate and current operation delay. Through the risk event related to the operation delay, the total delay that may exist is monitored in advance, and the pre-scheduling scheme generation is triggered in time.

Description

Technical Field

[0001] This invention belongs to the field of material support operations, and specifically relates to a material support pre-scheduling method and system. Background Technology

[0002] In existing technologies, the main function of marine supplies dispatching is to monitor the status of various resources within the system and coordinate their synchronous operation to complete supplies dispatching and support tasks. Before support is provided, the system formulates a support plan based on the task volume and resource availability. This plan includes the resource usage sequence and corresponding operation time. During the execution of the support plan, resources are dispatched according to the resource operation sequence to provide supplies support. If resource damage or new tasks occur during the support process that may lead to the failure of the current support task, supplies support needs to be temporarily suspended. Once a new, adapted support plan is formulated (such as adding tasks that allocate vehicles or other resources), support will resume.

[0003] It is evident that current common methods for handling scheduling problems primarily rely on reactive scheduling based on emergencies. Intervention occurs only after dispatchers or the system detects an anomaly, resulting in a certain degree of lag. For situations with large scheduling workloads and tight time constraints, firstly, personnel intervention after an event occurs introduces system delays, and in this emergency approach, dispatchers or the system cannot quickly find an optimal solution. Secondly, mid-process intervention in complex systems may disrupt existing scheduling operations, leading to overall delays in support. Dispatchers will face immense pressure in responding to increasingly urgent scheduling demands.

[0004] Chinese invention patent application CN120031279A discloses an intelligent decision-making method and system for the emergency allocation of emergency medical supplies. This method integrates graph neural networks, deep learning, reinforcement learning, Bayesian optimization, and genetic algorithms to efficiently process demand information from multiple emergency response points. By receiving demand information including the number, type, and severity of injuries, an intelligent assessment system dynamically calculates the urgency of the demand and generates a preliminary allocation plan. Then, a traffic prediction model combined with real-time and historical data is used to estimate road capacity and simulate various logistics and distribution strategies to optimize the preliminary plan and determine the optimal route. Subsequently, an AI simulation exercise system is used to verify the effectiveness of the supplies allocation in different scenarios, adjust the configuration plan, and finally generate comprehensive allocation instructions to guide actual operations. This achieves intelligent decision support for emergency medical supplies, significantly improving the efficiency and accuracy of emergency medical supply allocation. Summary of the Invention

[0005] The purpose of this invention is to provide a pre-scheduling method and system for material support, which solves the problems of delayed scheduling and untimely manual processing in existing scheduling problem-solving schemes.

[0006] To achieve the above objectives, the present invention provides a pre-scheduling method for material support, comprising: During the material scheduling process, when a risk event occurs, the real-time values ​​of the system situation assessment feature parameters are input into the trained system situation assessment neural network according to a set cycle to obtain the predicted total delay time for system operation completion; if the total delay time is greater than the pre-scheduling threshold, the pre-scheduling scheme is triggered to generate. The system situation assessment neural network is trained using the system situation assessment feature parameter values ​​and corresponding total latency in the training set; including the current number of warehouses in the system, inventory information of each warehouse, task requirement information, resource status information, operation status information of each warehouse, current operation completion rate, and current operation latency.

[0007] Furthermore, the methods for triggering the generation of the pre-schedule plan include: obtaining all the material scheduling operations that have not yet been completed after the current time node, and using all the material scheduling operations as new scheduling tasks to re-plan and derive a pre-schedule plan for material support.

[0008] Furthermore, it also includes: displaying the pre-scheduling plan to the user for confirmation; if the user confirms that the pre-scheduling plan has been added to the scheduling sequence, then the pre-scheduling plan is added to the current material support scheduling sequence.

[0009] Furthermore, methods for obtaining real-time values ​​of system situation assessment characteristic parameters include: retrieving real-time data of system situation assessment characteristic parameters from an in-memory database; Real-time data of system situation assessment characteristic parameters are obtained by formatting and caching system situation assessment characteristic parameter values ​​of different formats and frequencies collected in real time.

[0010] Furthermore, the real-time data of the system situation assessment characteristic parameters are obtained by collecting real-time data including on-site equipment operation monitoring information, operation video information, and operation information fed back from terminals and dispatching stations, and the collected data is preliminarily cleaned.

[0011] Furthermore, the methods for obtaining the system situation assessment feature parameter values ​​and the corresponding total latency for adding to the training set include: retrieving historical system situation assessment feature parameter values ​​and the corresponding total latency from a non-relational database; Historical system status assessment characteristic parameter values ​​and corresponding total delay durations are obtained by formatting and caching historical data of system status assessment characteristic parameter values ​​and corresponding total delay durations in different formats and frequencies.

[0012] Furthermore, the pre-scheduling plan is presented to the user for confirmation in the following ways: the pre-scheduling plan is presented to the user through a human-computer interaction device, and confirmation information input by the user through the human-computer interaction device is obtained.

[0013] Furthermore, the human-computer interaction device includes a handheld terminal or a display console.

[0014] Furthermore, risk events include vehicle or elevator malfunctions required for scheduling operations.

[0015] The above-described technical solution of the present invention provides a novel method for pre-scheduling of material support, the beneficial effects of which include: By identifying risk events related to job delays, monitoring of potential total delays is triggered in advance before the overall job scheduling system experiences anomalies. Based on this, combined with real-time system situation assessment feature parameter values ​​and a trained system situation assessment neural network, the total delay for job completion is predicted in real time, thereby enabling real-time monitoring of future delay durations. If the future delay duration exceeds the limit, a pre-scheduling plan is generated in a timely manner, thus avoiding the lag of manually adjusting scheduling after the entire system malfunctions.

[0016] The present invention also provides a material support pre-scheduling system, including a processor storing executable program instructions, the executable program instructions being used to execute the following material support pre-scheduling method, specifically including: During the material scheduling process, when a risk event occurs, the real-time values ​​of the system situation assessment feature parameters are input into the trained system situation assessment neural network according to a set cycle to obtain the predicted total delay time for system operation completion; if the total delay time is greater than the pre-scheduling threshold, the pre-scheduling scheme is triggered to generate. The system situation assessment neural network is trained using the system situation assessment feature parameter values ​​and corresponding total latency in the training set; including the current number of warehouses in the system, inventory information of each warehouse, task requirement information, resource status information, operation status information of each warehouse, current operation completion rate, and current operation latency.

[0017] Furthermore, the methods for triggering the generation of the pre-schedule plan include: obtaining all the material scheduling operations that have not yet been completed after the current time node, and using all the material scheduling operations as new scheduling tasks to re-plan and derive a pre-schedule plan for material support.

[0018] Furthermore, it also includes: displaying the pre-scheduling plan to the user for confirmation; if the user confirms that the pre-scheduling plan has been added to the scheduling sequence, then the pre-scheduling plan is added to the current material support scheduling sequence.

[0019] Furthermore, methods for obtaining real-time values ​​of system situation assessment characteristic parameters include: retrieving real-time data of system situation assessment characteristic parameters from an in-memory database; Real-time data of system situation assessment characteristic parameters are obtained by formatting and caching system situation assessment characteristic parameter values ​​of different formats and frequencies collected in real time.

[0020] Furthermore, the real-time data of the system situation assessment characteristic parameters are obtained by collecting real-time data including on-site equipment operation monitoring information, operation video information, and operation information fed back from terminals and dispatching stations, and the collected data is preliminarily cleaned.

[0021] Furthermore, the methods for obtaining the system situation assessment feature parameter values ​​and the corresponding total latency for adding to the training set include: retrieving historical system situation assessment feature parameter values ​​and the corresponding total latency from a non-relational database; Historical system status assessment characteristic parameter values ​​and corresponding total delay durations are obtained by formatting and caching historical data of system status assessment characteristic parameter values ​​and corresponding total delay durations in different formats and frequencies.

[0022] Furthermore, the pre-scheduling plan is presented to the user for confirmation in the following ways: the pre-scheduling plan is presented to the user through a human-computer interaction device, and confirmation information input by the user through the human-computer interaction device is obtained.

[0023] Furthermore, the human-computer interaction device includes a handheld terminal or a display console.

[0024] Furthermore, risk events include vehicle or elevator malfunctions required for scheduling operations.

[0025] The technical solution of the material support pre-scheduling system described above in this invention can achieve the same beneficial effects as the material support pre-scheduling method described above. Attached Figure Description

[0026] Figure 1 This is a flowchart of the material support pre-scheduling method in the embodiment of the material support pre-scheduling method of the present invention; Figure 2 This is an example diagram illustrating the architecture of the material support pre-scheduling method in the implementation of the material support pre-scheduling method of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0028] Implementation of Material Support Pre-dispatch Method This embodiment presents a technical solution for a pre-scheduling method for material support. By triggering risk events and training a neural network model with process data related to material support, it can monitor the possible delay after a risk event. This enables the generation of a pre-scheduling plan in a timely manner, avoiding the lag of manually adjusting the scheduling after the entire system malfunctions.

[0029] The method includes: During the material scheduling process, when a risk event occurs, the real-time values ​​of the system situation assessment feature parameters are input into the trained system situation assessment neural network according to a set cycle to obtain the predicted total delay time for system operation completion; if the predicted total delay time for system operation completion exceeds the pre-scheduling threshold, the pre-scheduling plan is triggered to generate. The system situation assessment neural network is trained using the system situation assessment feature parameter values ​​and the corresponding total delay time in the training set. The system situation assessment feature parameters include the current number of warehouses in the system, inventory information of each warehouse, task requirement information, resource status information, operation status information of each warehouse, current operation completion rate, and current operation delay.

[0030] Therefore, by using risk events related to job delays, the monitoring of potential total delays can be triggered in advance before the overall job scheduling system malfunctions. Based on this, by combining real-time system situation assessment feature parameter values ​​and the trained system situation assessment neural network, the total delay for job completion can be predicted in real time, thereby monitoring the future delay duration in real time. Once the future delay duration exceeds the limit (i.e., the pre-scheduling threshold), the pre-scheduling scheme is triggered in a timely manner. Thus, the lag of manually adjusting the scheduling after the entire system malfunctions can be avoided.

[0031] Specifically, resource status information may include resource status information such as vehicles and personnel, but this implementation method does not impose specific limitations.

[0032] In one embodiment, the set period can be 1 second; in other embodiments, other set period values ​​can also be used.

[0033] In this embodiment, risk events are the origin of pre-scheduling. The principle for identifying risk events is that they occur in the system's operational resources and affect the task completion time. Specific risk events may include vehicle malfunctions, elevator malfunctions, etc., all of which will affect the subsequent execution of tasks and prolong the system's task completion time; however, this embodiment does not impose specific limitations.

[0034] Reference Figure 1The method further includes: displaying the pre-scheduling plan to the user for confirmation; if the user confirms adding the pre-scheduling plan to the scheduling sequence, then the pre-scheduling plan is added to the current material support scheduling sequence. This setup enables secondary confirmation of the pre-scheduling plan and human-computer interaction, minimizing the possibility that problems with the generated pre-scheduling plan could affect the overall scheduling after application.

[0035] Specifically, the methods for triggering the generation of a pre-schedule plan include: obtaining all unfinished material scheduling tasks after the current time node, and using these unfinished tasks as new scheduling tasks for replanning to derive a pre-schedule plan for material support. For example, after obtaining information that future support may be delayed (i.e., after meeting the conditions for triggering the generation of a pre-schedule plan), the unfinished tasks after the current node are counted, such as 10 remaining Category A materials. These are then used as new tasks for replanning to derive a new material support plan, which serves as the generated pre-schedule plan.

[0036] Specifically, the methods for presenting the pre-scheduling plan to the user for confirmation include: displaying the pre-scheduling plan to the user through a human-computer interaction device and obtaining confirmation information input by the user through the human-computer interaction device. The human-computer interaction device includes a handheld terminal or a display console. (See reference...) Figure 2 This part can be implemented through the application layer. In a preferred embodiment, the pre-scheduling plan is submitted to the user as a prompt message through a human-computer interaction device. After the user weighs and confirms, if they confirm that the plan is added to the scheduling sequence, then the pre-scheduling plan is added to the material support scheduling sequence.

[0037] In a preferred embodiment, the operation of obtaining the predicted total delay time for system job completion and triggering the pre-scheduling scheme generation based on the total delay time can be implemented through the business service layer; specifically, the business service layer mainly performs fusion processing on the data of special material support, mainly including special material support business logic and big data processing business logic.

[0038] The big data processing business logic primarily uses neural network models to assist in system situation assessment based on job status information. By analyzing system status-related information, such as the percentage of jobs completed and current job delays, as feature parameters for system situation assessment, and using the total delay time for job completion as the target, a system situation assessment neural network is trained. Based on the trained system situation awareness model, features can be extracted from the collected data at a certain frequency, such as every second, and neural networks can be used to predict the future situation (i.e., the total delay time for job completion).

[0039] The material support management and scheduling module, after obtaining information such as potential delays in future support, counts the tasks that have not yet been completed after the current node, and re-plans accordingly to derive a new material support plan, which serves as the generated pre-scheduling plan.

[0040] In addition, in this embodiment, the method of obtaining the real-time values ​​of system situation assessment feature parameters includes: calling the real-time data of system situation assessment feature parameters from the memory database; the real-time data of system situation assessment feature parameters is obtained by formatting and caching the system situation assessment feature parameter values ​​of different formats and frequencies collected in real time.

[0041] Specifically, real-time data for system situation assessment characteristic parameters is obtained by collecting real-time data including on-site equipment operation monitoring information, operation video information, and operation information fed back from terminals and dispatching stations. The collected data undergoes preliminary cleaning, a process implemented through the data acquisition layer. This real-time data is then stored and processed at high speed using a currently popular in-memory database, a process implemented through the data storage layer.

[0042] The process of obtaining real-time values ​​of system situation assessment characteristic parameters can be achieved through the data integration layer. The data integration layer mainly integrates real-time data and offline data. Real-time data mainly integrates the data collected by the data acquisition layer. By using cluster services, message queues, and other methods, it processes the incremental data of material support that is constantly updated over time. Offline data integration mainly integrates the processed historical data. This layer mainly uses data processing middleware to format and cache the collected data of different formats and frequencies, preparing for data storage.

[0043] Furthermore, the methods for obtaining the system situation assessment feature parameter values ​​and corresponding total latency for inclusion in the training set include: retrieving historical system situation assessment feature parameter values ​​and corresponding total latency from a non-relational database; these historical system situation assessment feature parameter values ​​and corresponding total latency are obtained by formatting and caching historical data of system situation assessment feature parameter values ​​and corresponding total latency in different formats and frequencies. This operation can also be obtained through the data integration layer.

[0044] Offline data such as historical system situation assessment characteristic parameter values ​​and corresponding total latency are mainly stored in non-relational databases, or can be achieved through a data storage layer.

[0045] Within the pre-scheduling technology framework, and in response to the new application business needs of scheduling and various special operations, we provide suggestions and strategies for system operation risks and the safe and stable operation of material support through situational awareness of material support.

[0046] Implementation of Material Support Pre-dispatch System This embodiment provides a technical solution for a material support pre-scheduling system, including a processor containing executable program instructions. These instructions are executed to implement the following material support pre-scheduling method: During the material scheduling process, when a risk event occurs, the real-time values ​​of the system situation assessment feature parameters are input into the trained system situation assessment neural network according to a set cycle to obtain the predicted total delay time for system operation completion; if the predicted total delay time for system operation completion exceeds the pre-scheduling threshold, the pre-scheduling plan is triggered to generate. The system situation assessment neural network is trained using the system situation assessment feature parameter values ​​and corresponding total latency in the training set; including the current number of warehouses in the system, inventory information of each warehouse, task requirement information, resource status information, operation status information of each warehouse, current operation completion rate, and current operation latency.

[0047] Therefore, by using risk events related to job delays, the monitoring of potential total delays can be triggered in advance before the overall job scheduling system malfunctions. Based on this, by combining real-time system situation assessment feature parameter values ​​and the trained system situation assessment neural network, the total delay for job completion can be predicted in real time, thereby monitoring the future delay duration in real time. Once the future delay duration exceeds the limit (i.e., the pre-scheduling threshold), the pre-scheduling scheme is triggered in a timely manner. Thus, the lag of manually adjusting the scheduling after the entire system malfunctions can be avoided.

[0048] Specifically, resource status information may include resource status information such as vehicles and personnel, but this implementation method does not impose specific limitations.

[0049] In one embodiment, the set period can be 1 second; in other embodiments, other set period values ​​can also be used.

[0050] In this embodiment, risk events are the origin of pre-scheduling. The principle for identifying risk events is that they occur in the system's operational resources and affect the task completion time. Specific risk events may include vehicle malfunctions, elevator malfunctions, etc., all of which will affect the subsequent execution of tasks and prolong the system's task completion time; however, this embodiment does not impose specific limitations.

[0051] The pre-scheduling method for material support also includes: displaying the pre-scheduling plan to the user for confirmation; if the user confirms adding the pre-scheduling plan to the scheduling sequence, then the pre-scheduling plan is added to the current material support scheduling sequence. This setup enables secondary confirmation of the pre-scheduling plan and facilitates human-computer interaction, minimizing the possibility that problems with the generated pre-scheduling plan could affect the overall scheduling after application.

[0052] Specifically, the methods for triggering the generation of a pre-schedule plan include: obtaining all unfinished material scheduling tasks after the current time node, and using these unfinished tasks as new scheduling tasks for replanning to derive a pre-schedule plan for material support. For example, after obtaining information that future support may be delayed (i.e., after meeting the conditions for triggering the generation of a pre-schedule plan), the unfinished tasks after the current node are counted, such as 10 remaining Category A materials. These are then used as new tasks for replanning to derive a new material support plan, which serves as the generated pre-schedule plan.

[0053] Specifically, the pre-scheduling plan is presented to the user for confirmation via a human-computer interaction device (HCI device), and confirmation information input by the user through the HCI device is obtained. The HCI device includes a handheld terminal or a display console. This part can be implemented at the application layer. In a preferred embodiment, the pre-scheduling plan is submitted to the user as a prompt through the HCI device. After the user weighs and confirms the plan, if they confirm its inclusion in the scheduling sequence, the pre-scheduling plan is added to the material support scheduling sequence.

[0054] In a preferred embodiment, the operation of obtaining the predicted total delay time for system job completion and triggering the pre-scheduling scheme generation based on the total delay time can be implemented through the business service layer; specifically, the business service layer mainly performs fusion processing on the data of special material support, mainly including special material support business logic and big data processing business logic.

[0055] The big data processing business logic primarily uses neural network models to assist in system situation assessment based on job status information. By analyzing system status-related information, such as the percentage of jobs completed and current job delays, as feature parameters for system situation assessment, and using the total delay time for job completion as the target, a system situation assessment neural network is trained. Based on the trained system situation awareness model, features can be extracted from the collected data at a certain frequency, such as every second, and neural networks can be used to predict the future situation (i.e., the total delay time for job completion).

[0056] The material support management and scheduling module, after obtaining information such as potential delays in future support, counts the tasks that have not yet been completed after the current node, and re-plans accordingly to derive a new material support plan, which serves as the generated pre-scheduling plan.

[0057] In addition, in this embodiment, the method of obtaining the real-time values ​​of system situation assessment feature parameters includes: calling the real-time data of system situation assessment feature parameters from the memory database; the real-time data of system situation assessment feature parameters is obtained by formatting and caching the system situation assessment feature parameter values ​​of different formats and frequencies collected in real time.

[0058] Specifically, real-time data for system situation assessment characteristic parameters is obtained by collecting real-time data including on-site equipment operation monitoring information, operation video information, and operation information fed back from terminals and dispatching stations. The collected data undergoes preliminary cleaning, a process implemented through the data acquisition layer. This real-time data is then stored and processed at high speed using a currently popular in-memory database, a process implemented through the data storage layer.

[0059] The process of obtaining real-time values ​​of system situation assessment characteristic parameters can be achieved through the data integration layer. The data integration layer mainly integrates real-time data and offline data. Real-time data mainly integrates the data collected by the data acquisition layer. By using cluster services, message queues, and other methods, it processes the incremental data of material support that is constantly updated over time. Offline data integration mainly integrates the processed historical data. This layer mainly uses data processing middleware to format and cache the collected data of different formats and frequencies, preparing for data storage.

[0060] Furthermore, the methods for obtaining the system situation assessment feature parameter values ​​and corresponding total latency for inclusion in the training set include: retrieving historical system situation assessment feature parameter values ​​and corresponding total latency from a non-relational database; these historical system situation assessment feature parameter values ​​and corresponding total latency are obtained by formatting and caching historical data of system situation assessment feature parameter values ​​and corresponding total latency in different formats and frequencies. This operation can also be obtained through the data integration layer.

[0061] Offline data such as historical system situation assessment characteristic parameter values ​​and corresponding total latency are mainly stored in non-relational databases, or can be achieved through a data storage layer.

[0062] Within the pre-scheduling technology framework, and in response to the new application business needs of scheduling and various special operations, we provide suggestions and strategies for system operation risks and the safe and stable operation of material support through situational awareness of material support.

[0063] It should be understood that the above-described specific embodiments of the present invention are merely illustrative or explanatory of the principles of the present invention, and do not constitute a limitation thereof.

Claims

1. A method for pre-scheduling material support, characterized in that, include: During the material scheduling process, when a risk event occurs, the real-time values ​​of the system situation assessment feature parameters are input into the trained system situation assessment neural network according to a set cycle to obtain the predicted total delay time for system operation completion; if the total delay time is greater than the pre-scheduling threshold, the pre-scheduling scheme is triggered to generate. The system situation assessment neural network is trained using system situation assessment feature parameter values ​​and corresponding total latency in the training set; including the current number of warehouses in the system, inventory information of each warehouse, task requirement information, resource status information, operation status information of each warehouse, current operation completion rate, and current operation latency.

2. The material support pre-scheduling method according to claim 1, characterized in that, The methods for triggering the generation of the pre-schedule plan include: obtaining all the material scheduling operations that have not yet been completed after the current time node, and using all the material scheduling operations as new scheduling tasks to re-plan and derive the pre-schedule plan for material support.

3. The material support pre-scheduling method according to claim 1 or 2, characterized in that, Also includes: The pre-scheduling plan will be presented to the user for confirmation; If the user confirms that the pre-scheduling plan has been added to the scheduling sequence, then the pre-scheduling plan will be added to the current material support scheduling sequence.

4. The material support pre-scheduling method according to claim 1 or 2, characterized in that, The methods for obtaining real-time values ​​of system situation assessment characteristic parameters include: retrieving real-time data of system situation assessment characteristic parameters from an in-memory database; Real-time data of system situation assessment characteristic parameters are obtained by formatting and caching system situation assessment characteristic parameter values ​​of different formats and frequencies collected in real time.

5. The material support pre-scheduling method according to claim 4, characterized in that, The real-time data of the system situation assessment characteristic parameters are obtained by collecting real-time data including on-site equipment operation monitoring information, operation video information, and operation information fed back from terminals and dispatching stations, and the collected data is preliminarily cleaned.

6. The material support pre-scheduling method according to claim 1 or 2, characterized in that, The methods for obtaining the system situation assessment feature parameter values ​​and corresponding total latency for inclusion in the training set include: retrieving historical system situation assessment feature parameter values ​​and corresponding total latency from a non-relational database; Historical system status assessment characteristic parameter values ​​and corresponding total delay durations are obtained by formatting and caching historical data of system status assessment characteristic parameter values ​​and corresponding total delay durations in different formats and frequencies.

7. The material support pre-scheduling method according to claim 1 or 2, characterized in that, Presenting the pre-scheduling plan to the user for confirmation includes: presenting the pre-scheduling plan to the user through a human-computer interaction device and obtaining confirmation information input by the user through the human-computer interaction device.

8. The material support pre-scheduling method according to claim 7, characterized in that, The human-computer interaction device includes a handheld terminal or a display console.

9. The material support pre-scheduling method according to claim 1 or 2, characterized in that, Risk events include vehicle or elevator malfunctions required for scheduling operations.

10. A material support pre-scheduling system, comprising a processor, wherein the processor stores executable program instructions, characterized in that, The executable program instructions are executed to implement the material support pre-scheduling method according to any one of claims 1-9.

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