An APS method based on plug-in and cloud cooperation and related device

Abstract

This application proposes an APS method and related apparatus based on plug-in architecture and cloud collaboration, which solves the problems existing in current manufacturing production scheduling schemes. The method includes: deploying a dedicated table scheduling plug-in on a local terminal; in response to a first click operation, triggering a standardized scheduling table model plug-in to generate various standardized scheduling tables required by the manufacturing industry; inputting relevant data of production plan information into the corresponding standardized scheduling tables; performing data verification on the input data, marking and prompting corrections for detected abnormal data, until there is no abnormal data; in response to a second click operation, triggering a data upload plug-in to encrypt and upload the input data to the server; through the server, calling the cloud to perform production scheduling calculations based on the input data using multi-constraint dynamic adaptation and optimization strategies, transmitting the final production scheduling result back as structured encrypted data, and triggering a result download plug-in to fill the corresponding table in the scheduling result table.

Description

Technical Field

[0001] This application relates to the field of production scheduling technology in manufacturing, and more particularly to an APS method and related apparatus based on plug-in and cloud collaboration. Background Technology

[0002] Production planning and scheduling (APS), often simply referred to as "scheduling," is a core decision-making process in the operation and management of manufacturing enterprises. Its essence is to arrange the optimal production sequence and time for a series of tasks to be produced (such as customer orders and work orders) under the constraints of limited production resources (such as machines, manpower, materials, and time).

[0003] Currently, manufacturing scheduling technologies are mainly divided into the following three categories: 1) Traditional manual scheduling: Relies on the experience of the scheduler, manually enters information such as orders, resources and processes through Excel spreadsheets, and manually calculates the scheduling plan. It is suitable for scenarios with small order volume and simple process.

[0004] 2) Professional APS Systems: These are typically independently deployed professional systems that use intelligent optimization algorithms such as simulated annealing and ant colony optimization, combined with multi-dimensional constraints (such as capacity, inventory, and process priority), to perform scheduling calculations. They support complex production scenarios, but require dedicated servers and integration with enterprise databases, thus falling under the "re-deployment" model. For example, Chinese patent application CN115564266A discloses an advanced planning and scheduling system based on simulated annealing. This system uses the annealing algorithm and considers production constraints to propose various scheduling schemes such as shortest lead time, maximum order quantity, and first-come, first-served. It uses the annealing algorithm's domain search and the Metropolos acceptance criterion to determine whether to accept new solutions. Finally, it evaluates the resulting schemes and selects the scheduling scheme that best meets the set objectives. For example, Chinese patent application CN112785054A discloses a comprehensive production order scheduling system based on inventory shift and multiple constraints. This system uses an efficient ant colony algorithm model built on the server side with the input production orders and constraint rule set to handle traditional application system business problems. It also allows for parallel computing without interference from other computer server resources, thereby improving production order scheduling efficiency through a new hardware and software combination model.

[0005] 3) Lightweight scheduling tools: Some scheduling tools provide basic scheduling functions through independent clients or web pages, but they are not deeply integrated with the widely used Excel / WPS spreadsheets. Users still need to learn the new operation interface, and data needs to be manually imported and exported, resulting in poor collaboration.

[0006] In summary, existing manufacturing production scheduling schemes mainly have the following problems: (1) High deployment costs and high barriers to entry: Professional APS systems require enterprises to invest in hardware resources and IT maintenance teams, which is difficult for small and medium-sized enterprises to afford. In addition, the system interface is complex and the implementation cycle is long (usually several months), making it impossible to implement quickly. For example, the B / S deployment mode of the advanced planning and scheduling system based on simulated annealing algorithm and the Linux server + MySQL database of the comprehensive production order scheduling system based on inventory movement and multiple constraints require enterprises to invest in hardware resources and IT maintenance teams for deployment.

[0007] (2) High operational complexity and high learning cost: Existing scheduling systems (including the B / S deployment mode of the advanced planning and scheduling system based on simulated annealing algorithm and the comprehensive production order scheduling system based on inventory movement with multiple constraints) all require users to adapt to dedicated operation interfaces and understand professional scheduling terminology, which is not user-friendly for non-technical personnel. In addition, although traditional manual scheduling relies on Excel spreadsheets, it lacks standardized models and automated calculation capabilities, resulting in low efficiency.

[0008] (3) Data interaction is fragmented and lacks flexibility: The existing scheduling system and Excel / WPS spreadsheet are mostly in the "one-way import / export" mode, which cannot achieve real-time linkage; when it is necessary to temporarily adjust the scheduling parameters (such as equipment failure, emergency order insertion) or verify the new algorithm logic, it is necessary to reconnect the data interface, which is cumbersome and has a strong lag.

[0009] (4) Long algorithm verification cycle: In the early stage of implementation, the existing scheduling system needs to wait for the interface between the scheduling system and the enterprise ERP, MES and other systems to be connected before the effectiveness of the scheduling algorithm can be verified. If there are problems with the algorithm logic, the adjustment cost is high and the cycle is long. Summary of the Invention

[0010] This application provides an APS method and related apparatus based on plug-in architecture and cloud collaboration to solve the problems existing in related technologies. The technical solution is as follows: In a first aspect, embodiments of this application provide an APS method based on plug-in architecture and cloud collaboration, including: To address the production scheduling needs of the manufacturing industry, a dedicated table scheduling plugin is deployed on the local terminal. After deployment, a scheduling plugin function tab is added to the table menu bar. The scheduling plugin function tab includes function buttons for the dedicated table scheduling plugin. The dedicated table scheduling plugin includes a standardized scheduling table model plugin that supports custom field configuration, a data upload plugin that supports encrypted transmission, and a result download plugin. In response to the first click operation, the standardized scheduling table model plugin is triggered to generate various standardized scheduling tables required by the manufacturing industry on the table workbook currently displayed on the local terminal. The first click operation is the operation of the user clicking the function button of the standardized scheduling table model plugin on the scheduling plugin function tab. The various standardized scheduling tables include a scheduling result table. The relevant data of the production plan information are entered into the corresponding standardized scheduling tables in the various standardized scheduling tables; The corresponding standardized scheduling table is validated, and any abnormal data detected is marked and prompted for correction until there is no abnormal data in the corresponding standardized scheduling table. In response to the second click operation, the data upload plugin is triggered to encrypt and upload the entered data in the corresponding standardized scheduling table to the server. The second click operation is the operation of the user clicking the function button of the data upload plugin on the scheduling plugin function tab. The server calls the cloud to perform production scheduling calculations based on the input data using a multi-constraint dynamic adaptation and optimization strategy to obtain the final production scheduling result. The server transmits the final production scheduling result back to the local terminal in the form of encrypted structured data, triggering the result download plugin to fill the structured data into the corresponding table of the scheduling result table.

[0011] In one implementation, to meet the production scheduling needs of the manufacturing industry, a dedicated table scheduling plugin is deployed on the local terminal. After deployment, a new scheduling plugin function tab is added to the table menu bar, including: The various production scheduling functions required by the manufacturing industry are pre-packaged into table-recognizable table scheduling plugins to obtain the dedicated table scheduling plugins. The dedicated table scheduling plugin is deployed on the local terminal. After deployment, the dedicated table scheduling plugin is automatically integrated into the table menu bar, resulting in the scheduling plugin function tab.

[0012] In one implementation, the dedicated table scheduling plugin further includes a data validation plugin; performing data validation on the corresponding standardized scheduling table, marking detected abnormal data and prompting for correction, until there is no abnormal data in the corresponding standardized scheduling table, including: In response to the third click operation, the data verification plugin is triggered to perform data verification on the corresponding standardized scheduling table to detect whether there is abnormal data in the corresponding standardized scheduling table. The third click operation is the operation of the user clicking the function button of the data verification plugin on the scheduling plugin function tab. If abnormal data is detected in the corresponding standardized scheduling table, the data verification plugin marks the abnormal data in the corresponding standardized scheduling table and outputs a verification report, wherein the verification report is used to prompt the correction of the abnormal data. The system returns to the execution response of the third click operation, triggering the data verification plugin to perform data verification on the corresponding standardized scheduling table, detecting whether there is abnormal data in the corresponding standardized scheduling table, until there is no abnormal data in the corresponding standardized scheduling table.

[0013] In one implementation, the server invokes a cloud-based method employing a multi-constraint dynamic adaptation and optimization strategy to perform production scheduling calculations based on the entered data, resulting in the final production scheduling outcome, including: The server calls the cloud to perform the following production scheduling calculation process: The production orders corresponding to the entered data are sorted according to work filtering and assignment rules to obtain the production order sorting; Allocate appropriate production resources to any work task corresponding to each target production order, wherein each target production order is a production order in the production order sorting; Based on the entered data, the current production constraints are identified, including order priority constraints, capacity constraints, changeover loss constraints, inventory limit constraints, minimum interval time constraints, and bottleneck resource constraints. Each target production order's work task is broken down into four task segments, and the time for each of the four task segments is calculated in conjunction with the production constraints. Based on the production order sorting, the production resources of any work task corresponding to each target production order, and the time of its four task segments, a preliminary production scheduling result is obtained; The preliminary production scheduling results are optimized to obtain the final production scheduling results.

[0014] In one implementation, optimizing the preliminary production scheduling results to obtain the final production scheduling results includes: The preliminary production scheduling results are optimized using a local search optimization strategy to obtain the preliminary optimized production scheduling results; The preliminary optimized production scheduling results are verified from multiple dimensions to confirm whether there are any anomalies in the preliminary optimized production scheduling results. If it is confirmed that there are no abnormalities in the preliminary optimized production scheduling results, then the preliminary optimized production scheduling results will be confirmed as the final production scheduling results. If it is confirmed that there is an anomaly in the preliminary optimized production scheduling result, the preliminary optimized production scheduling result is optimized a second time, and the production scheduling result after the second optimization is confirmed as the final production scheduling result.

[0015] In one implementation, the dedicated table scheduling plugin further includes a parameter update plugin that supports incremental data updates, and the method further includes: In response to the parameter update operation, the parameter update plugin is triggered to upload the changed data in the corresponding standardized scheduling table to the server. The parameter update operation is the operation of the user clicking the function button of the parameter update plugin on the scheduling plugin function tab after correcting and / or adding data in the corresponding standardized scheduling table. The server calls the cloud to update the final production scheduling result based on the changed data, and then sends the updated data in the final production scheduling result back to the local terminal, triggering the result download plugin to fill the corresponding table in the scheduling result table with the updated data.

[0016] In one implementation, updating the final production schedule result based on the changed data by calling the cloud from the server includes: The server calls the cloud to execute the following production scheduling result update process: Identify the constraints corresponding to the changed data, and based on the constraints, determine the target work tasks affected by the changed data in the final production scheduling result; The production scheduling is recalculated for the target work task, and the final production scheduling result is updated based on the calculation result.

[0017] Secondly, embodiments of this application also provide an APS device based on plug-in architecture and cloud collaboration, comprising: The deployment unit is used to deploy a dedicated table scheduling plugin on a local terminal to meet the production scheduling needs of the manufacturing industry. After deployment, a scheduling plugin function tab is added to the table menu bar. The scheduling plugin function tab includes function buttons for the dedicated table scheduling plugin. The dedicated table scheduling plugin includes a standardized scheduling table model plugin that supports custom field configuration, a data upload plugin that supports encrypted transmission, and a result download plugin. The first processing unit is configured to respond to the first click operation and trigger the standardized scheduling table model plugin to generate various standardized scheduling tables required by the manufacturing industry on the table workbook currently displayed on the local terminal. The first click operation is the operation of the user clicking the function button of the standardized scheduling table model plugin on the scheduling plugin function tab. The various standardized scheduling tables include a scheduling result table. The data entry unit is used to input the relevant data of the production plan information into the corresponding standardized scheduling tables in the various standardized scheduling tables. The verification unit is used to verify the data in the corresponding standardized scheduling table, mark the detected abnormal data and prompt for correction, until there is no abnormal data in the corresponding standardized scheduling table. The second processing unit is used to respond to the second click operation and trigger the data upload plugin to encrypt and upload the entered data in the corresponding standardized scheduling table to the server. The second click operation is the operation of the user clicking the function button of the data upload plugin on the scheduling plugin function tab. The calculation unit is used to call the cloud through the server to perform production scheduling calculation based on the input data using a multi-constraint dynamic adaptation and optimization strategy to obtain the final production scheduling result; the server transmits the final production scheduling result back to the local terminal as structured data in encrypted form, triggering the result download plugin to fill the structured data into the corresponding table of the scheduling result table.

[0018] In one embodiment, the dedicated table scheduling plugin further includes a parameter update plugin that supports incremental data updates, and the calculation unit is further configured to: In response to the parameter update operation, the parameter update plugin is triggered to upload the changed data in the corresponding standardized scheduling table to the server. The parameter update operation is the operation of the user clicking the function button of the parameter update plugin on the scheduling plugin function tab after correcting and / or adding data in the corresponding standardized scheduling table. The server calls the cloud to update the final production scheduling result based on the changed data, and then sends the updated data in the final production scheduling result back to the local terminal, triggering the result download plugin to fill the corresponding table in the scheduling result table with the updated data.

[0019] Thirdly, embodiments of this application also provide an electronic device, which includes: a memory and a processor, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the methods in any of the above embodiments, wherein the memory and the processor communicate with each other through an internal connection path.

[0020] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when run on a computer, implements the methods in any of the above-described embodiments.

[0021] The advantages or beneficial effects of the above technical solutions include at least the following: This application focuses on a framework of "table carrier + plug-in implementation + cloud collaboration," which does not rely on independent system deployment. By using a table scheduling plug-in to create a closed loop of "local table operation - cloud computing - local result feedback," the following effects can be achieved: (i) Low deployment cost and low threshold: Using spreadsheets (Excel / WPS) as a common carrier, production scheduling functions are implemented through plug-ins. There is no need to deploy a professional APS system, no need for hardware investment or IT team support. Advanced scheduling functions (such as automatic early warning, production resource optimization, etc.) can be achieved by upgrading the spreadsheet scheduling plug-in, which significantly reduces the technical threshold and cost for enterprises, and small and medium-sized enterprises can implement it quickly.

[0022] (ii) Easy to operate and low learning cost: Relying on the popularity of spreadsheets, users can operate them without professional training, and the plugin interface simplifies complex processes, such as data entry and result viewing, which can be completed in a familiar spreadsheet environment. (III) Real-time data linkage and high flexibility: It supports automatic data capture and input from the table and upload to the server. The server and the cloud work together to dynamically perform production scheduling calculations. Finally, the production scheduling calculation results are dynamically sent back to fill the scheduling result table on the local terminal, forming a "local-cloud-local" closed loop. This avoids the lag of manual scheduling. At the same time, the cloud collaborative architecture breaks through the limitations of traditional Excel local calculations. It relies on the distributed computing capabilities of the cloud to handle complex constraints (such as capacity and process priority), realizes multi-dimensional optimization, and has high production planning flexibility.

[0023] (iv) Short Algorithm Verification Cycle: In the initial stage of implementing a professional APS system, it supports seamless integration with the professional APS system, forming an efficient transition path of "verification-implementation". This allows for rapid verification of the effectiveness of the production scheduling algorithm logic in advance, without waiting for system interface integration, avoiding delays caused by interface failures and shortening the project cycle. For example, manufacturing enterprises can quickly assess the optimization effect of the production scheduling algorithm on capacity bottlenecks by simulating multiple production order scenarios, thus significantly shortening the project cycle and reducing trial and error costs.

[0024] (v) Data security and controllability: Data is stored on the enterprise's own server to avoid the risk of cloud leakage; the table scheduling plugin supports local encrypted transmission to protect the privacy of production data.

[0025] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of this application will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0026] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0027] Figure 1 A flowchart illustrating an APS method based on plug-in architecture and cloud collaboration, provided for an embodiment of this application; Figure 2 A flowchart illustrating another APS method based on plug-in architecture and cloud collaboration provided in this application embodiment; Figure 3 A structural block diagram of an APS device based on plug-in architecture and cloud collaboration provided in this application embodiment; Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0029] Figure 1 A flowchart illustrating an APS method based on plug-in architecture and cloud collaboration according to an embodiment of this application is shown. Figure 1 As shown, the method may include the following steps: S110. To meet the production scheduling needs of the manufacturing industry, a dedicated table scheduling plugin is deployed on the local terminal. After deployment, a scheduling plugin function tab is added to the table menu bar.

[0030] In one implementation, the scheduling plugin function tab may include dedicated function buttons for the table scheduling plugin.

[0031] In one implementation, the dedicated table scheduling plugin may include, but is not limited to: a standardized scheduling table model plugin that supports custom field configuration, a data upload plugin that supports encrypted transmission, and a result download plugin. It can be understood that the scheduling plugin's function tabs integrate entry points for functions such as initialization, data upload, and result download.

[0032] For example, the standardized scheduling table model plugin can support custom configuration of fields such as "mold model" and "changeover time" on the corresponding standardized scheduling table.

[0033] In one implementation, the process of step S110 may include the following sub-steps: S111. Pre-package various production scheduling functions required by the manufacturing industry into table-recognizable table scheduling plugins to obtain the dedicated table scheduling plugin.

[0034] In practical implementation, various production scheduling functions required by the manufacturing industry can be pre-packaged into individual table scheduling plugins that can be recognized by Excel or WPS Spreadsheets, resulting in a dedicated table scheduling plugin. This dedicated table scheduling plugin can be understood as including these individual table scheduling plugins.

[0035] For example, in the scenario of using Excel spreadsheets, the various spreadsheet scheduling plugins are COM plugins. In the scenario of using WPS Spreadsheets, the various spreadsheet scheduling plugins are JS plugins.

[0036] In this embodiment of the application, by executing sub-step S111, various production scheduling functions required by the manufacturing industry can be pre-designed as plug-ins, and can be called without modifying the main body of office software (such as Excel spreadsheets or WPS spreadsheets) when used later.

[0037] S112. Deploy the dedicated table scheduling plugin on the local terminal. After deployment, the dedicated table scheduling plugin will be automatically integrated into the table menu bar, resulting in the scheduling plugin function tab.

[0038] In practical implementation, an installation package for this dedicated spreadsheet scheduling plugin can be provided to users. This package is compatible with Windows and / or Mac operating systems. When a user double-clicks the installation package on their local terminal, the plugin is automatically deployed. During deployment, the plugin automatically detects the spreadsheet version (e.g., Excel 2016 and above, or WPS 2019 and above), ensuring compatibility with the local terminal environment and generating a compatibility report. Once deployed, the plugin is automatically integrated into the spreadsheet menu bar, appearing as a separate tab.

[0039] As an example, the scheduling plugin's feature tab could be called the "Production Scheduling" dedicated tab.

[0040] In this embodiment of the application, by executing sub-step S112, selectable table scheduling plugins can be provided to users in a visual manner through the scheduling plugin function tab.

[0041] In other words, by executing step S110, the embodiments of this application can achieve seamless integration with office software, establish a unified starting point for users, and facilitate users to complete complex scheduling in a familiar office software environment (such as Excel or WPS).

[0042] S120. In response to the first click operation, the standardized scheduling table model plugin is triggered to generate various standardized scheduling tables required by the manufacturing industry on the table workbook currently displayed on the local terminal.

[0043] In one implementation, the first click operation is the user clicking the function button of the standardized scheduling table model plugin on the scheduling plugin function tab.

[0044] In one implementation, various standardized scheduling tables may include, but are not limited to: order information table, resource list table, process rule table, and scheduling result table.

[0045] In this implementation, the standardized scheduling table model refers to a predefined set of Excel or WPS spreadsheet templates, including but not limited to four types of standardized scheduling tables: "Order Information Table", "Resource List Table", "Process Rule Table", and "Scheduling Result Table". These four types of standardized scheduling tables cover all data fields required for production scheduling.

[0046] For example, when a user clicks the function button of the Standardized Scheduling Table Model Plugin in the Scheduling Plugin tab (which can be presented as the "Initialize Model" button), the Standardized Scheduling Table Model Plugin will automatically generate these four types of standardized scheduling tables in the Excel or WPS workbook currently displayed on the local terminal.

[0047] As an example, the standardized scheduling table model plugin supports custom configuration fields in these four types of standardized scheduling tables. For instance, users can select the fields to be enabled through the plugin interface (such as adding fields like "order urgency" and "equipment maintenance time"). Clicking the "confirm" button on the plugin interface will trigger the standardized scheduling table model plugin to automatically update the column structure of these four types of standardized scheduling tables.

[0048] In this embodiment of the application, a structured template is provided for data entry by performing step S120.

[0049] S130. Enter the relevant data of the production plan information into the corresponding standardized scheduling tables in various standardized scheduling tables.

[0050] In one implementation, users can input relevant data of production plan information into the corresponding standardized scheduling tables in various standardized scheduling tables.

[0051] As an example, taking various standardized scheduling tables, including order information tables, resource lists, process rule tables, and scheduling result tables, relevant data from production planning information can be entered into the order information tables, resource lists, and process rule tables. This relevant data can include, but is not limited to, production order demand data (including multiple production orders) and production resource data. For example, this relevant data may include order number, product model, quantity, delivery date, equipment number, capacity, and available time.

[0052] For example, a small to medium-sized electronics assembly plant mainly produces smartphone chargers and faces the demand for "multiple varieties and small batches" of orders (e.g., receiving 10-15 customized orders per day). Production resources include: 3 SMT production lines (R1 / R2 / R3), 2 testing machines (T1 / T2), and 3 sets of molds (M1 / M2 / M3, corresponding to different charger models). Constraints exist: ① Changeover loss (30 minutes of setup time required to switch molds between different models); ② Capacity constraints (daily capacity of a single SMT line ≤ 500 units); ③ Delivery time constraints (order delivery time is concentrated in 3-7 days). In this production scenario, after the user enters the relevant data for the production plan according to the list format of the order information table, resource list table, and process rule table, the order information table, resource list table, and process rule table can be as shown in Tables 1-3 below.

[0053] Table 1 Order Information Table

[0054] Table 2 Resource List Table 3 Process Rules In this embodiment of the application, standardized data entry and visualization can be achieved by performing step S130.

[0055] S140. Perform data verification on the corresponding standardized scheduling table, mark the detected abnormal data and prompt for correction, until there is no abnormal data in the corresponding standardized scheduling table.

[0056] In one implementation, when the dedicated table scheduling plugin also includes a data validation plugin, the implementation of step S140 may include the following sub-steps: S141. In response to the third click operation, the data verification plugin is triggered to perform data verification on the corresponding standardized scheduling table and detect whether there is abnormal data in the corresponding standardized scheduling table.

[0057] In practice, the third click is the user clicking the data validation plugin's function button on the scheduling plugin's function tab. This validation report is used to prompt correction of the abnormal data.

[0058] In practical implementation, the data validation plugin can have a built-in data rule engine to verify the completeness and rationality of data entered into the standardized scheduling table, such as verifying that the order delivery date is not earlier than the current date and that the equipment capacity is not negative.

[0059] As an example, when a user clicks the function button of the data validation plugin on the scheduling plugin function tab (which can be presented as a "data validation" button), the data validation plugin will automatically scan all standardized scheduling tables to check for abnormal data (such as missing fields such as missing order delivery dates, unreasonable data such as negative capacity, etc.).

[0060] S142. If abnormal data is detected in the corresponding standardized scheduling table, the data verification plugin marks the abnormal data in the corresponding standardized scheduling table and outputs a verification report.

[0061] As an example, if the data validation plugin detects that an order in the corresponding standardized scheduling table has a delivery date earlier than the current date or that the resource capacity is entered as a negative number, it will mark the error and pop up a validation report to prompt the user to correct it, ensuring that the data meets the input requirements of the server-side algorithm.

[0062] S143. Return to sub-step S141 until there is no abnormal data in the corresponding standardized scheduling table.

[0063] As an example, after correcting the abnormal data in the corresponding standardized scheduling table, the user can click the "Data Validation" button again on the scheduling plugin's function tab to trigger the data validation plugin to automatically scan all standardized scheduling tables to check for abnormal data. This process continues until there is no abnormal data in the corresponding standardized scheduling table before proceeding with subsequent operations.

[0064] In this embodiment of the application, by executing step S140, it can be ensured that the data subsequently uploaded to the server is valid, so as to ensure the correctness of subsequent production scheduling calculations.

[0065] S150. In response to the second click operation, the data upload plugin is triggered to encrypt and upload the entered data in the corresponding standardized scheduling table to the server.

[0066] In one implementation, the second click operation is the user clicking the data upload plugin's function button (which can be presented as a "one-click upload" button) on the scheduling plugin's function tab.

[0067] In one implementation, in response to the second click operation, the data upload plugin can encrypt the entered data using the AES-256 algorithm (local encryption) and then upload it to the server via the HTTPS protocol (transmission encryption). This server is the in-house server of the manufacturing enterprise, such as the in-house server of the aforementioned small and medium-sized electronics assembly plant.

[0068] As an example, the data upload plugin can establish a temporary communication connection with the server via a preset API interface (the server address can be configured by the user). When the user clicks the "One-Click Upload" button in the "Add Scheduling Plugin" tab, the data upload plugin automatically extracts the entered data (i.e., the validated table data) from the corresponding standardized scheduling table, organizes it into structured data according to preset field rules, encrypts it, and then uploads it to the server via the API interface. During this process, the data upload plugin can display upload progress information (such as "Upload complete 80%)" on the Excel or WPS spreadsheet interface. If the upload fails, it will indicate the reason (such as server not responding, network interruption, etc.). Correspondingly, after receiving the entered data uploaded from the local terminal, the server can return an "Upload successful" receipt to the local terminal via the API interface.

[0069] It should be understood that the AES-256 algorithm used in the embodiments of this application is existing and will not be described in detail here.

[0070] It should be understood that in the Excel environment, API refers to the COM interface. In the WPS environment, API refers to the JSAPI interface.

[0071] In this embodiment of the application, by executing step S150, only structured data can be transmitted to the server, and the transmission is carried out using a dual encryption method of "local encryption + transmission channel encryption". In this way, the user does not need to manually process the data format or operate additional tools throughout the process. It can also avoid transmitting redundant information and prevent data from being stolen during network transmission, thus ensuring the security of data transmission.

[0072] S160. The server calls the cloud to perform production scheduling calculations based on the entered data using a multi-constraint dynamic adaptation and optimization strategy, and obtains the final production scheduling result.

[0073] In one implementation, during step S160, the following production scheduling calculation process can be performed by calling the cloud from the server: S161. Perform work filtering and assignment rule sorting on each production order corresponding to the entered data to obtain the production order sorting.

[0074] In practice, when the server receives input data uploaded from the local terminal, it can call the cloud to filter out several production orders to be scheduled / adjusted based on the real-time status of each production order corresponding to the input data and the specified production order filtering rules.

[0075] As an example, the specified production order filtering rule can include the following three levels of filtering: • Status filtering: Only retain "adjustable production orders". For example, retain production orders with statuses of "planned completion" or "unplanned", and exclude production orders with statuses of "paused" or "cancelled". • User-defined filtering: Filter by user-defined conditions, such as filtering production orders by conditions "order delivery time ≤ 7 days" or "product model = Item1"; • Bottleneck association filtering: Prioritize filtering production orders related to bottleneck resources (such as production orders not scheduled on bottleneck resources, and production orders that depend on upstream / downstream processes of bottleneck resources).

[0076] In this embodiment of the application, by filtering several production orders to be scheduled / adjusted based on the real-time status of each production order in the cloud according to the specified filtering rules, dynamic filtering of production orders can be performed to determine the range of production orders to be adjusted in the entered data.

[0077] For example, taking the aforementioned example of a small-to-medium-sized electronics assembly plant, after filtering production orders, production orders with a status of "paused" can be excluded, and only 3 production orders (order numbers OD20260103, OD20260102, and OD20260101) can be retained. In this way, the SMT placement, testing, and packaging processes (a total of 9 work tasks) corresponding to these 3 production orders can be retained.

[0078] In practice, for the selected production orders, the cloud can determine the execution order of these production orders according to a multi-rule weighted sorting strategy.

[0079] As an example, the multiple assignment rules, dynamic processing logic of each assignment rule, and sorting direction included in this multi-rule weighted sorting strategy can be shown in Table 4 below.

[0080] Table 4 As an example, the sorting calculation formula used by this multi-rule weighted sorting strategy can be shown in the following formula (1).

[0081] Priority score for each production order = (1) In formula (1), For each production order, the priority level value is calculated by the i-th dispatch rule among multiple dispatch rules. The weight value pre-set for the i-th assignment rule (can be set according to actual needs).

[0082] It should be understood that production orders with higher priority scores will be allocated resources and scheduling more frequently.

[0083] In this embodiment of the application, the execution order of these production orders is determined by a cloud-based multi-rule weighted sorting strategy, which can dynamically adjust the priority of these production orders so that the sorting of these production orders can adapt to changes in order parameters in real time.

[0084] For example, taking the aforementioned example of a small to medium-sized electronics assembly plant, after sorting according to the allocation rules and following the order priority (descending order) → delivery date (ascending order) → remaining production time (descending order), the resulting production order order can be as follows: 1) OD20260103 (C30, priority 3, earliest delivery time 1.03) → SMT placement → testing → packaging; 2) OD20260102 (C20, priority 1, lead time 1.06) → SMT placement → testing → packaging; 3) OD20260101 (C10, priority 2, lead time 1.04) → SMT placement → testing → packaging.

[0085] In this embodiment of the application, by executing step S161, production orders can be dynamically sorted using a work-led heuristic. For example, the priority of production orders can be dynamically determined by "work screening - assignment rule sorting", which can ensure that key production orders (such as urgent orders and orders with near delivery dates) are scheduled first.

[0086] S162. Allocate appropriate production resources to any work task corresponding to each target production order.

[0087] In practice, each target production order is the production order in the production order sequence.

[0088] In practice, the cloud can allocate appropriate production resources to any work task corresponding to each target production order according to the three-step calculation strategy of "production resource screening-evaluation-matching".

[0089] As an example, the calculation logic of this three-step "production resource screening-evaluation-matching" strategy can be as follows: Step 1: Dynamic Screening of Production Resources First, based on the real-time resource status, the cloud uses the specified resource filtering rules to filter out the effective production resources from the "Resource List" of the entered data, and then selects them as candidate production resources.

[0090] For example, the specified production resource filtering rule can include the following four levels of filtering: • Validity screening: Exclude invalid production resources (such as equipment marked as "scrapped" or "under maintenance") and overcapacity production resources (such as equipment that has reached the daily limit for processing quantity / weight). • Specification matching filter: Filter out equipment that can process products that match the “Resource List Table” and “Process Rule Table”. For example, if processing products with “Specification = White”, only equipment with “White” in the resource specification can be retained. Or, if the resource specification is “Numerical Specification = 5-10”, only equipment that can process products with numerical specifications in this range can be retained. • Post-resource constraint screening: If the production resources of the previous process have been scheduled, and there is a "post-resource constraint" (the resources of the previous process determine the resources of the subsequent process, such as the heat treatment furnace → quenching tank need to be matched), then only the production resources that meet the constraint rules are retained. For example, if the furnace resource used in the previous process is R1, and the quenching tank that the subsequent process needs to match is R2, then the subsequent process only selects R2 as a candidate production resource. • Maximum number of machines to be started: If the above resource list can also include the maximum number of machines to be started for each type of production resource, in this case, if the number of machines to be started for the same process has reached the limit within a certain period (e.g., a maximum of 10 machines to be started during the day shift), the remaining resources will be marked as "temporarily unavailable".

[0091] In this embodiment of the application, production resources are dynamically screened through the cloud. Based on the real-time resource status, effective production resources can be selected from the production resources given in the "resource list" to narrow down the candidate range of production resources.

[0092] Step 2: Dynamic Assessment of Production Resources After the candidate production resources are selected in the cloud, a multi-index weighted evaluation strategy can be used to calculate the suitability of each candidate production resource with any work task corresponding to each target production order.

[0093] As an example, the multiple evaluation indicators included in this multi-indicator weighted evaluation strategy and the dynamic processing logic of each evaluation indicator can be shown in Table 5 below.

[0094] As an example, the fitness calculation formula used in this multi-index weighted evaluation strategy can be shown in the following formula (2).

[0095] The fit between each candidate production resource and any work task = (2) In formula (2), For each candidate production resource, an evaluation value is obtained by evaluating it against any work task of each target production order using the j-th evaluation index among multiple evaluation indicators. Let be the weight value of the j-th evaluation indicator.

[0096] In the example above, the evaluation indicators and calculation formulas used in this multi-indicator weighted evaluation strategy are fully adapted to changes in real-time resource data.

[0097] Table 5 In this embodiment of the application, dynamic evaluation of production resources is performed in the cloud, which can quantify the suitability of each candidate production resource and select the most suitable production resource for each production order.

[0098] Step 3: Dynamic matching of primary and secondary production resources If any task in each target production order requires primary and secondary production resources (such as equipment + mold + operator), the cloud can match secondary production resources for that task according to the matching principle of "matching number priority - minimum waiting time" after determining the primary production resource for that task in the second step.

[0099] As an example, the "matching number priority - minimum waiting time" matching principle can include the following three levels of matching: Matching Numbers: The matching numbers of primary and secondary production resources must be consistent. For example, if the matching number of the primary production resource is "G1", then the secondary production resource can only be matched with production resources whose matching number contains "G1". • Waiting time matching: Among the production resources with matching numbers, select the production resource with the shortest waiting time. For example, if production resource M1 waits for 10 minutes and M2 waits for 20 minutes, then select M1 as the secondary production resource. • Manufacturing time coordination: Determine the manufacturing time relationship between primary and secondary production resources according to "standard process parameters", such as "taking the shortest time for primary production resources" and "taking the longest time for secondary production resources", and dynamically adjust the total manufacturing time of any work task.

[0100] In the embodiments of this application, by performing dynamic matching of primary and secondary production resources, the effectiveness of the combination of primary and secondary production resources allocated to any work task for each target production order can be guaranteed.

[0101] In practical applications, the core challenge of production scheduling calculation is to adapt to changes in the status of production resources in real time (such as equipment failure, staff absence, and lack of auxiliary production resources). Based on this, this application embodiment executes step S162, in which the cloud allocates appropriate production resources to any work task corresponding to each target production order according to the three-step calculation strategy of "production resource screening-evaluation-matching". This can achieve dynamic collaboration between production resources and production orders under the dynamic constraints of production resources.

[0102] For example, taking the SMT placement task of production order OD20260103 as an example, the cloud filters valid production resources (R1 / R2 / R3 are all valid resources, and molds M1 / M2 / M3 are matched with C10 / C20 / C30 respectively). Based on "waiting time (weight 1) + resource priority (weight 1.5) + changeover time (weight 1)", the evaluation of production resources R1, R2, and R3 can be as follows: R3: Waiting time 0 hours (currently idle), resource priority 100 (highest), transformation time 0 (no prerequisite tasks), evaluation value = 0×1 + 100×1.5 + 0×1 = 150; R1: Waiting time 1 hour (currently there are small batch tasks), resource priority 80, transformation time 0, evaluation value = 1×1 + 80×1.5 + 0×1 = 121; R2: Waiting time 0.5 hours, resource priority 90, replacement time 0, evaluation value = 0.5×1 + 90×1.5 + 0×1 = 135.5; Based on the above assessment, the optimal production resource can be determined as R3, and the cloud will allocate R3 to the SMT placement task of production order OD20260103.

[0103] S163. Based on the entered data, identify the current production constraints.

[0104] In practice, the production constraints may include, but are not limited to: order priority constraints, capacity constraints, changeover loss constraints, inventory ceiling constraints, minimum interval time constraints, and bottleneck resource constraints.

[0105] As an example, the dynamic processing logic and conflict handling methods corresponding to order priority constraints, capacity constraints, changeover loss constraints, inventory ceiling constraints, minimum interval time constraints, and bottleneck resource constraints can be found in Table 6 below.

[0106] Table 6 In this embodiment of the application, by identifying production constraints, it is convenient to perform scheduling calculations in real time by integrating production constraints, and to perform collaborative calculations according to the rule of "mandatory constraints take precedence over priority constraints", thereby realizing dynamic coordination of multiple constraints and thus avoiding the scheduling scheme obtained after calculation from exceeding the actual production capacity.

[0107] S164. Decompose any task of each target production order into four task segments (SubTasks), and calculate the time of these four task segments in combination with the above production constraints.

[0108] In practice, the time types of these four task segments correspond to pre-setting time, manufacturing time, changeover time, and post-setting time.

[0109] As an example, the dynamic processing logic, key influencing factors, and examples corresponding to the pre-set time can be as follows: • Dynamic processing logic (including real-time parameter adaptation) 1) Prioritize reading the "pre-set time" (if the user manually adjusts it, then use it directly); 2) If no time is specified, the calculation will be based on the combination of "static setting time + dynamic setting time": - Static setup time: Read the inherent preparation time of production resources (such as the basic time for mold change) from the pre-stored work available resource table in the cloud. - Dynamically set time: Calculated based on real-time item / specification / byproduct resources (when the same device switches to different specifications of products, the dynamic value with the highest priority is taken according to the specification setting time schedule stored in the cloud). 3) Final time = (static setting time + dynamic setting time) × manufacturing efficiency impact coefficient (can be adjusted synchronously according to fluctuations in resource efficiency); 4) Consider the work calendar: If the previously set time spans shifts / off-get off work hours, automatically insert non-working hours (e.g., if the previous settings are not completed after the day shift ends, they will be postponed to the start of the next day's morning shift).

[0110] Key influencing factors and examples -Dynamic factors: Item switching (the same item is set to 0 time), secondary production resource matching (different fixtures correspond to different time settings); Example: Equipment R1 switches to process "Item1" → "Item2". The static setting time is 2 hours, the dynamic specification setting time is 1 hour, and the manufacturing efficiency is 1.2. Then the actual calculated pre-setting time is (2+1) / 1.2 = 2.5 hours. If it spans the off-shift period (1 hour remaining), then the total time is 2.5 hours + 8 hours (night shift non-working time) = 10.5 hours.

[0111] As an example, the dynamic processing logic, key influencing factors, and examples related to manufacturing time can be as follows: • Dynamic processing logic (including real-time parameter adaptation) Manufacturing time is the dimension most frequently adjusted in dynamic calculations, requiring real-time adaptation to changes in resource efficiency, production batch size, and interruption limits. The calculation logic consists of 7 steps: Step 1: Determining the basic manufacturing time - Fixed duration (e.g., 5h / batch), proportional to quantity (e.g., 3s / piece), or a combination of both (e.g., 2h preparation + 2s / piece), read from the "Process Rules Table"; Step 2, Dynamic Manufacturing Efficiency Adaptation: -If resource efficiency is constant: Actual manufacturing time = Base manufacturing time / Efficiency value; - If resource efficiency is a variable (such as equipment aging, shift efficiency difference), in the forward arrangement mode, take the efficiency value of the time period where "manufacturing start time" is located, or in the reverse arrangement mode, take the efficiency value of the time period where "manufacturing end time" is located. Step 3, Rounding up manufacturing time: Round to the nearest whole number according to the resource allocation "rounding unit" (e.g., 10 minutes / 30 minutes) (rounding is the default). Step 4, Coordination of primary and secondary production resources: If any task requires primary and secondary production resources (such as equipment + fixtures), take the time base according to the "standard process parameters". For example, the time base can be selected as the time of primary production resources / the longest time of secondary production resources / the maximum / minimum / average of both. Step 5, Specify Manufacturing Time Override: If the user manually specifies a time (e.g., for urgent orders that must be completed in 2 hours), the above logic is skipped and the specified value is used directly; Step 6, Work Calendar and Interruption Limitations: -Uninterruptible task: Manufacturing ends - Start = Manufacturing time (without gaps); - Interruptible tasks: Manufacturing completion - Start = Manufacturing time + Interruption time (e.g., temporary equipment inspection, interruption time ≤ system parameter "maximum interruption length"); Step 7, Special handling of furnace resources: Pit furnaces are calculated based on "first working manufacturing time", and walking beam furnaces are calculated based on "the sum of all working manufacturing times".

[0112] Key influencing factors and examples -Dynamic factors: fluctuations in resource efficiency (e.g., equipment R2 efficiency of 1.0 during day shift and 0.8 during night shift), batch adjustments (increasing the order quantity from 100 to 200), and interruption events (equipment failure for 1 hour). Example: Task base manufacturing time = 10h (50 pieces, 2h / 10 pieces), resource efficiency variable (starting time is during day shift, efficiency 1.0), interruptible (interruption 1h), then actual manufacturing time = 10h / 1.0 + 1h (interruption) = 11h.

[0113] As an example, the dynamic processing logic, key influencing factors, and examples related to changeover time can be as follows: • Dynamic processing logic (including real-time parameter adaptation) Changeover time is crucial for "connecting preceding and following processes" in dynamic calculations. It needs to be correlated with the progress of subsequent processes in real time and is calculated in two categories: The first category is related to the start time of planned manufacturing for subsequent processes. Changeover time = Max{earliest start time of subsequent process - end time of current process + fixed buffer time, 0s}; The second category is related to the planned completion time of subsequent manufacturing processes. Changeover time = Max{Latest manufacturing end time of subsequent processes - Manufacturing end time of this process + Fixed buffer time, 0s}; In addition to the two categories mentioned above, for special scenarios, if the subsequent process is not scheduled or is canceled, the changeover time will be automatically set to 0 seconds (to avoid ineffective resource occupation).

[0114] Key influencing factors and examples - Dynamic factors: Adjustment of subsequent process schedule (such as the subsequent process starting 1 hour earlier), modification of buffer time (the user changes the fixed buffer from 30 minutes to 15 minutes). Example: The manufacturing process ends at 10:00, and the earliest start time of the next process is 10:30. With a fixed buffer of 10 minutes, the changeover time = (10:30 - 10:00) + 10 minutes = 40 minutes.

[0115] As an example, the dynamic processing logic, key influencing factors, and examples corresponding to the post-set time can be as follows: • Dynamic processing logic (including real-time parameter adaptation) The calculation logic is the same as the previously set time, but it needs to be additionally associated with the "resource lock status": 1) If resources need to be locked (e.g., equipment needs to be cleaned after processing), the post-set time = (static + dynamic set time) / manufacturing efficiency; 2) If the resource is not locked (or the primary and secondary production resources are not locked in sync), then the time setting will be set to 0s (to avoid resource idleness). 3) Handling across shifts: Set the time as before, and automatically extend to the next working period.

[0116] Key influencing factors and examples Dynamic factors: resource lock status (lock is canceled due to equipment failure), replacement needs (if no replacement is needed, the subsequent setting time is 0). In this embodiment of the application, by executing step S164, the four task segments obtained from the breakdown of any work task can be updated in real time according to real-time parameters (such as manufacturing efficiency, work calendar, interruption limit).

[0117] In an applicable scenario, after executing step S164, the timing of these four task segments can be verified to ensure the accuracy of the timing of the four task segments.

[0118] For example, assume that the standard SMT working hours for C30 are 1.2 hours / 100 pieces, the order quantity is 200 pieces, the R3 manufacturing efficiency is 1.1, the work calendar is an 8-hour day shift (9:00-17:00), and there are no interruption restrictions.

[0119] In this case, the manufacturing time = (200 / 100 × 1.2) / 1.1 ≈ 2.18 hours; Changeover time calculation: R3 has no prerequisite tasks, changeover time = 0, pre-setup time = 10 minutes (mold installation base time), post-setup time = 5 minutes (equipment cleaning). Constraint verification: R3 daily production capacity is 500 units, and the work task is 200 units ≤ 500 units, which meets the production capacity constraint; the task plan end time = 9:00 + 10 minutes (pre-set time) + 2.18 hours (manufacturing time) + 5 minutes (post-set time) ≈ 11:26, which meets the delivery time constraint of 1.03 days.

[0120] S165. Based on the production order sorting, the production resources of any work task corresponding to each target production order, and the time of these four task segments, a preliminary production scheduling result is obtained.

[0121] S166. Optimize the preliminary production scheduling results to obtain the final production scheduling results.

[0122] In practice, during step S166, the server can invoke the cloud to execute the following scheduling optimization process: S1661. The preliminary production scheduling results are optimized using a local search optimization strategy to obtain the preliminary optimized production scheduling results.

[0123] As an example, the local search optimization strategy employs a three-step iterative logic of "dynamic job evaluation - dynamic insertion position evaluation - dynamic solution evaluation" for production scheduling optimization: Step 1: Dynamic Job Evaluation: Select the job tasks to be adjusted The cloud-based system first assesses the "necessity of adjustment" of the existing work tasks corresponding to each target production order. The core assessment indicators and dynamic processing logic used are shown in Table 7 below.

[0124] Step 2: Dynamic Insertion Position Evaluation: Determining the Optimal Adjustment Position For the selected tasks to be adjusted, the cloud can use the evaluation indicators shown in Table 8 below to find the optimal insertion position on production resources to fully adapt to the real-time resource status.

[0125] Step 3: Evaluation of the dynamic solution: Selecting the optimal adjustment scheme After each adjustment, the cloud can use the evaluation indicators shown in Table 9 below (strongly linked to business objectives) to judge the merits of the production scheduling plan through "multi-objective weighted evaluation".

[0126] The solution evaluation formula used in the third step can be shown in the following formula (3).

[0127] Project evaluation value = Σ (indicator value × indicator weight) (3) In formula (3), the smaller the scheme evaluation value, the better the corresponding production scheduling result.

[0128] Table 7 Table 8 Table 9 If the evaluation value of the preliminary optimized production schedule is better than the preliminary production schedule, the adjustment is accepted; otherwise, the "taboo strategy" is triggered to avoid repeatedly adjusting the invalid positions of the preliminary production schedule.

[0129] For example, if the cloud detects that the SMT task of production order OD20260102 (C20) requires 30 minutes of changeover if assigned to R2 (matching mold M2), and 30 minutes of changeover if adjusted to R3 (after completing C30), but R3 has sufficient idle time afterward, then the local search evaluation result can be: total changeover time after adjustment = 30 minutes (R3) + 0 (R2 keeps the subsequent tasks of C20), which is 30 minutes less than the original production schedule result. The solution evaluation value is better, so the SMT task of production order OD20260102 is finally adjusted to R3 (starting at 11:31).

[0130] In this embodiment of the application, by executing step S1661, the execution order of work tasks for each target production order can be dynamically optimized, improving the optimality of the scheduling scheme and reducing changeover losses.

[0131] S1662. Perform multi-dimensional verification on the preliminary optimized production scheduling results to confirm whether there are any anomalies. If it is confirmed that there are no anomalies in the preliminary optimized production scheduling results, proceed to S1663; or, if it is confirmed that there are anomalies in the preliminary optimized production scheduling results, proceed to S1664.

[0132] In practical applications, cloud-based production scheduling calculations are not "one-time completions." They require "multi-dimensional verification, exception handling, and secondary optimization" to ensure the feasibility of the final production scheduling results and ultimately output production scheduling results that meet business objectives.

[0133] As an example, the cloud can perform multi-dimensional verification on the initially optimized production scheduling results, and can correlate with changes in production constraints in real time: • Delivery date verification: In forward scheduling, if the planned end time of the task is greater than the latest end time of the order, mark it as "delivery date delayed" and calculate the delay duration; • Constraint compliance verification: Check whether the following are violated: "Maximum manufacturing batch" (number of tasks ≤ resource batch limit), "Minimum interval time" (interval between preceding and following processes ≥ set value), and "Resource quantity constraint" (number of tasks ≤ available resources); • Inventory verification: Real-time correlation with inventory movement data. If the inventory of raw materials required for the schedule is less than the safety stock, mark "Insufficient materials" and trigger "Inventory Alert".

[0134] If the preliminary optimized production scheduling result fails to pass any of the above-mentioned dimension verifications, it can be determined that the preliminary optimized production scheduling result is abnormal. In this case, S1664 can be executed; otherwise, S1663 can be executed.

[0135] In this embodiment of the application, by executing step S1662, it can be ensured that the final production scheduling result output subsequently is compliant.

[0136] S1663. Confirm the preliminary optimized production scheduling results as the final production scheduling results.

[0137] S1664. Perform secondary optimization on the preliminary optimized production scheduling results, and confirm the secondary optimized production scheduling results as the final production scheduling results.

[0138] As an example, when an anomaly is detected in the initially optimized production scheduling results, the cloud will automatically trigger the following secondary optimization process: • Delivery delays: Prioritize adjusting low-priority orders, or split delayed orders (e.g., split 100 pieces into 50 pieces to be scheduled today and 50 pieces to be scheduled tomorrow). • Resource conflict: Activate backup resources (e.g., if device R1 fails, activate backup device R2), or adjust the task order (postpone conflicting tasks until resources are available). • Insufficient materials: Schedule the order to the "estimated arrival date of raw materials" or prompt the user to "make an emergency purchase".

[0139] In this embodiment of the application, by executing step S166, the feasibility of the final production scheduling result can be ensured by optimizing the preliminary production scheduling result, and the final output production scheduling result that meets the business objectives can be achieved.

[0140] In this embodiment of the application, by executing step S160, the optimal scheduling under dynamic constraints can be achieved by the server calling cloud-based strategies such as work-driven heuristic sorting, multi-indicator resource evaluation, and local search optimization.

[0141] S170. The server transmits the final production scheduling result back to the local terminal in the form of encrypted structured data, triggering the result download plugin to fill the corresponding table in the scheduling result table with the structured data.

[0142] In one implementation, after receiving the final production scheduling result returned from the cloud, the server processes the final production scheduling result into structured data according to the field mapping relationship of the scheduling result table, encrypts the structured data using the AES-256 algorithm, and then sends it back to the local terminal via the HTTPS protocol.

[0143] Correspondingly, after the result download plugin detects that the local terminal has received structured data from the server, it first decrypts the structured data, then breaks it down into table-recognizable cell data according to the field mapping relationship of the scheduling result table, and finally fills it into the corresponding table of the scheduling result table to complete the update of the scheduling result table.

[0144] In this embodiment of the application, the result download plugin will automatically fill the structured data into the corresponding table, and the user does not need to manually import the structured data returned by the server.

[0145] Based on the above examples, after the result download plugin fills the structured data into the corresponding table of the scheduling result table, the scheduling result table can be as shown in Table 10 below.

[0146] Based on the examples above, the scheduling result table can use different colors to mark the work tasks of each target production order. Specifically, special colors can be used to mark abnormal work tasks (such as overly delayed or conflicting tasks), and users can manually fine-tune the scheduling (e.g., dragging and dropping task times). For example, this scheduling result table can visually show that R3 completed two SMT tasks in 1.03 without resource conflicts; the testing task was assigned to T1 / T2, and the packaging task progressed simultaneously.

[0147] In another implementation, if the user enables Gantt charts, the result download plugin decrypts the structured data and generates a Gantt chart corresponding to the final production scheduling result based on the structured data. This enables the linkage between data and visualization charts, achieving synchronous updates of data and visualization content. The Gantt chart corresponding to the final production scheduling result can be displayed on the currently displayed workbook on the local terminal.

[0148] In one implementation, users can click the result download plugin's function button on the scheduling plugin tab to trigger the result download plugin to download various standardized scheduling tables to their local terminal.

[0149] In this embodiment of the application, by executing step S170, the dynamic feedback and visualization of the final production scheduling results can be achieved.

[0150] Table 10 In one applicable scenario provided in the embodiments of this application, combined with Figure 1 and Figure 2 As shown, when the dedicated table scheduling plugin also includes a parameter update plugin that supports incremental data updates, the APS method based on plug-inization and cloud collaboration provided in this application embodiment may further include the following steps: S180. In response to the parameter update operation, the parameter update plugin is triggered to upload the changed data in the corresponding standardized scheduling table to the server.

[0151] In one implementation, the parameter update operation is an operation in which the user corrects and / or adds data in the corresponding standardized scheduling table, and then clicks the function button of the parameter update plugin on the scheduling plugin function tab (which can be presented as the "Parameter Update" button).

[0152] As an example, when production parameters change (such as adjusting order priorities, deleting faulty equipment, etc.), users can directly modify the corresponding data in the corresponding standardized scheduling table (such as changing the priority of production order OD20260101 in the order information table from "2" to "1"). By clicking the "Parameter Update" button on the scheduling plugin function tab, the parameter update plugin will encrypt and upload only the changed data (not the full data) to the server.

[0153] Alternatively, if a small or medium-sized electronics assembly plant receives an urgent production order OD20260104 (C10, 100 units, delivery time 1.04 days, priority 1), the user (i.e., the employee) can add order data in the order information table and click the "Parameter Update" button on the scheduling plugin function tab. The parameter update plugin will then only upload the newly added order data.

[0154] In one implementation, the process of the parameter update plugin uploading the changed data in the corresponding standardized scheduling table to the server can be the same as or similar to the implementation process of step S150 above, and will not be described again in this embodiment.

[0155] S190. The server calls the cloud to update the final production scheduling result based on the changed data, and then sends the updated data in the final production scheduling result back to the local terminal, triggering the result download plugin to fill the corresponding table in the scheduling result table with the updated data.

[0156] In one implementation, the following production scheduling result update process can be executed by calling the cloud from the server: identifying the constraints corresponding to the changed data, and based on the constraints, determining the target work tasks affected by the changed data in the final production scheduling result; re-performing the production scheduling calculation on the target work tasks, and updating the final production scheduling result based on the calculation result.

[0157] As an example, the cloud can have a built-in incremental constraint adaptation module. After receiving changed data from the server, the cloud automatically calls the incremental constraint adaptation module to identify the constraints corresponding to the changed data (such as "delivery option readjustment" corresponding to order priority changes). Based on the constraints, the cloud determines the target work tasks affected by the changed data in the final production scheduling result (instead of rescheduling all work tasks). Then, the production scheduling is recalculated for the target work tasks, and the final production scheduling result is updated based on the calculation results. In this way, relying on the distributed computing resources of the cloud, the time taken for this production scheduling calculation is controlled within 10 seconds.

[0158] For example, in the scenario described above where a small to medium-sized electronics assembly plant receives an urgent production order OD20260104 (i.e., an urgent order insertion scenario), the cloud can perform the following production scheduling calculations: • Reordering: Urgent orders have the highest priority and are inserted at the beginning of the task queue; • Resource assessment: R1 is currently under low load, C10 matches mold M1, changeover time = 0 (R1's current task is small batch C10), the assessment value is optimal; • Constraint verification: R1 remaining capacity = 500 - 300 (OD20260101) = 200 ≥ 100, which meets the capacity constraint; planned start time = 5.03 13:00, manufacturing time = (100 / 100 × 1.2) / 1.0 = 1.2 hours, planned end time = 14:12, which meets the delivery time of 1.04.

[0159] As an example, after the results download plugin fills the corresponding tables in the scheduling results table with the updated data, it can highlight these tables to make it clear what changes have been made after the order data changes (such as the timeline of urgent production orders being brought forward), so as to achieve a closed loop of "data change → optimization → feedback".

[0160] In this embodiment, by executing steps S180 and S190, the changed data can be uploaded to the server in real time. The server then calls the cloud to perform a processing flow of "real-time data reception → incremental calculation adaptation → rapid result feedback" to dynamically optimize the final production scheduling result, thereby improving the flexibility of the production plan. For example, it supports real-time adjustments such as emergency order insertion and resource status changes. Only the changed data is uploaded, the server performs incremental calculations, and the results are immediately returned to the scheduling result table on the local terminal, meeting the dynamic production needs of the manufacturing industry.

[0161] In other words, by executing steps S110-S190, the embodiments of this application have at least the following four core innovations compared to the prior art: 1) The deep integration of the table scheduling plugin with cloud scheduling forms a closed loop of "local-cloud-local"; 2) Supports a dynamic scheduling mechanism for incremental data updates, adapting to scenarios such as emergency order insertion; 3) A scheduling algorithm that combines task fragmentation and dynamic adaptation with multiple constraints improves scheduling accuracy; 4) Dual data security protection with local encryption and transmission encryption.

[0162] As described above, the APS method based on plug-in and cloud collaboration provided in this application focuses on a framework of "table carrier + plug-in implementation + cloud collaboration," without relying on independent system deployment. By using a table scheduling plug-in to create a closed loop of "local table operation - cloud computing - local result feedback," the following effects can be achieved: (i) Low deployment cost and low threshold: Using spreadsheets (Excel / WPS) as the common carrier, production scheduling functions are realized through plug-ins. There is no need to deploy a professional APS system, no need for hardware investment or IT team support. Advanced scheduling functions (such as automatic early warning, production resource optimization, etc.) can be realized by upgrading the spreadsheet scheduling plug-in. Compared with traditional manual scheduling, efficiency can be improved by 80%, and the deployment cost can be reduced by 90% compared with a professional APS system. It significantly reduces the technical threshold and cost for enterprises, and small and medium-sized enterprises can quickly implement it.

[0163] (ii) Easy to operate and low learning cost: Relying on the popularity of spreadsheets, users can operate them without professional training, and the plugin interface simplifies complex processes, such as data entry and result viewing, which can be completed in a familiar spreadsheet environment. (III) Real-time data linkage and high flexibility: It supports automatic data capture and input from the table and upload to the server. The server and the cloud work together to dynamically perform production scheduling calculations. Finally, the production scheduling calculation results are dynamically sent back to fill the scheduling result table on the local terminal, forming a "local-cloud-local" closed loop. This avoids the lag of manual scheduling. At the same time, the cloud collaborative architecture breaks through the limitations of traditional Excel local calculations. It relies on the distributed computing capabilities of the cloud to handle complex constraints (such as capacity and process priority), realizes multi-dimensional optimization, and has high production planning flexibility.

[0164] (iv) Short Algorithm Verification Cycle: In the initial stage of implementing a professional APS system, it supports seamless integration with the professional APS system, forming an efficient transition path of "verification-implementation". This allows for rapid verification of the effectiveness of the production scheduling algorithm logic in advance, without waiting for system interface integration, avoiding delays caused by interface failures and shortening the project cycle. For example, manufacturing enterprises can quickly assess the optimization effect of the production scheduling algorithm on capacity bottlenecks by simulating multiple production order scenarios, thus significantly shortening the project cycle and reducing trial and error costs.

[0165] Before deploying a professional APS system, the plug-in-based and cloud-collaborative APS method provided in this embodiment can serve as an independent, lightweight scheduling solution for SMEs without a professional APS system, directly implementing advanced scheduling functions through Excel / WPS plugins. During the deployment of a professional APS system, the table scheduling plugins involved in the plug-in-based and cloud-collaborative APS method provided in this embodiment can serve as pre-validation tools for the professional APS system. For enterprises planning to launch a professional APS system, during the interface integration and hardware deployment cycle, these table scheduling plugins can be used to verify the effectiveness of the professional APS system's production scheduling algorithm logic. After the professional APS system goes live, the validated production scheduling algorithm logic can be directly reused, representing an extended application scenario for table scheduling plugins.

[0166] (v) Data security and controllability: Data is stored on the enterprise's own server to avoid the risk of cloud leakage; the table scheduling plugin supports local encrypted transmission to protect the privacy of production data.

[0167] The APS method based on plug-in and cloud collaboration provided in this application embodiment is an automated scheduling process that realizes "local operation - cloud optimization - result implementation". It starts from the interface integration to provide an entry point, then initializes and prepares data locally, optimizes through cloud computing, and finally sends back the visualization results.

[0168] Figure 3 A structural block diagram of an APS-related device based on plug-in architecture and cloud collaboration according to an embodiment of this application is shown. Figure 3As shown, the device may include: Deployment unit 210 is used to deploy a dedicated table scheduling plugin on a local terminal to meet the production scheduling needs of the manufacturing industry. After deployment, a scheduling plugin function tab is added to the table menu bar. The scheduling plugin function tab includes function buttons for the dedicated table scheduling plugin. The dedicated table scheduling plugin includes a standardized scheduling table model plugin that supports custom field configuration, a data upload plugin that supports encrypted transmission, and a result download plugin. The first processing unit 220 is used to respond to the first click operation and trigger the standardized scheduling table model plugin to generate various standardized scheduling tables required by the manufacturing industry on the table workbook currently displayed on the local terminal. The first click operation is the operation of the user clicking the function button of the standardized scheduling table model plugin on the scheduling plugin function tab. The various standardized scheduling tables include a scheduling result table. The data entry unit 230 is used to enter the relevant data of the production plan information into the corresponding standardized scheduling tables in various standardized scheduling tables. The verification unit 240 is used to verify the data in the corresponding standardized scheduling table, mark the detected abnormal data and prompt for correction, until there is no abnormal data in the corresponding standardized scheduling table. The second processing unit 250 is used to respond to the second click operation and trigger the data upload plugin to encrypt and upload the entered data in the corresponding standardized scheduling table to the server. The second click operation is the operation of the user clicking the function button of the data upload plugin on the scheduling plugin function tab. The calculation unit 260 is used to call the cloud through the server to perform production scheduling calculation based on the input data using a multi-constraint dynamic adaptation and optimization strategy to obtain the final production scheduling result; the server sends the final production scheduling result back to the local terminal as structured data, triggering the result download plugin to fill the structured data into the corresponding table of the scheduling result table.

[0169] In one implementation, the dedicated table scheduling plugin also includes a parameter update plugin, and the calculation unit 260 is further configured to: In response to the parameter update operation, the parameter update plugin is triggered to upload the changed data in the corresponding standardized scheduling table to the server. The parameter update operation is the operation of the user clicking the parameter update plugin function button on the scheduling plugin function tab after correcting and / or adding data in the corresponding standardized scheduling table. The server calls the cloud to update the final production scheduling result based on the changed data, and then sends the updated data in the final production scheduling result back to the local terminal, triggering the result download plugin to fill the corresponding table in the scheduling result table with the updated data.

[0170] The functions of each unit in the plug-in and cloud-collaborative APS device of this application embodiment can be found in the corresponding description in the above method, and will not be repeated here.

[0171] Figure 4 A structural block diagram of an electronic device according to an embodiment of this application is shown. Figure 4 As shown, the electronic device includes a memory 310 and a processor 320. The memory 310 stores instructions, which are loaded and executed by the processor 320 to implement the plug-in-based and cloud-collaborative APS method in the above embodiments. The number of memories 310 and processors 320 can be one or more.

[0172] The electronic device also includes: The communication interface 330 is used to communicate with external devices and perform data exchange and transmission.

[0173] If the memory 310, processor 320, and communication interface 330 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0174] Optionally, in a specific implementation, if the memory 310, processor 320 and communication interface 330 are integrated on a single chip, the memory 310, processor 320 and communication interface 330 can communicate with each other through an internal interface.

[0175] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The aforementioned memory can include read-only memory and random access memory, and can also include non-volatile random access memory.

[0176] This application provides a computer-readable storage medium storing a computer program. When the computer program is run on a computer, it implements the method provided in this application.

[0177] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0178] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0179] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0180] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0181] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0182] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0183] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An APS method based on plug-in architecture and cloud collaboration, characterized in that, include: To address the production scheduling needs of the manufacturing industry, a dedicated table scheduling plugin is deployed on the local terminal. After deployment, a scheduling plugin function tab is added to the table menu bar. The scheduling plugin function tab includes function buttons for the dedicated table scheduling plugin. The dedicated table scheduling plugin includes a standardized scheduling table model plugin that supports custom field configuration, a data upload plugin that supports encrypted transmission, and a result download plugin. The standardized scheduling table model plugin supports custom configuration of table fields. In response to the first click operation, the standardized scheduling table model plugin is triggered to generate various standardized scheduling tables required by the manufacturing industry on the table workbook currently displayed on the local terminal. The first click operation is the operation of the user clicking the function button of the standardized scheduling table model plugin on the scheduling plugin function tab. The various standardized scheduling tables include a scheduling result table. The relevant data of the production plan information are entered into the corresponding standardized scheduling tables in the various standardized scheduling tables; The corresponding standardized scheduling table is validated, and any abnormal data detected is marked and prompted for correction until there is no abnormal data in the corresponding standardized scheduling table. In response to the second click operation, the data upload plugin is triggered to encrypt and upload the entered data in the corresponding standardized scheduling table to the server. The second click operation is the operation of the user clicking the function button of the data upload plugin on the scheduling plugin function tab. The server calls the cloud to perform production scheduling calculations based on the input data using a multi-constraint dynamic adaptation and optimization strategy to obtain the final production scheduling result. The server transmits the final production scheduling result back to the local terminal in the form of encrypted structured data, triggering the result download plugin to fill the structured data into the corresponding table of the scheduling result table.

2. The method according to claim 1, characterized in that, To address the production scheduling needs of the manufacturing industry, a dedicated spreadsheet scheduling plugin is deployed on the local terminal. After deployment, a new scheduling plugin function tab is added to the spreadsheet menu bar, including: The various production scheduling functions required by the manufacturing industry are pre-packaged into table-recognizable table scheduling plugins to obtain the dedicated table scheduling plugins. The dedicated table scheduling plugin is deployed on the local terminal. After deployment, the dedicated table scheduling plugin is automatically integrated into the table menu bar, resulting in the scheduling plugin function tab.

3. The method according to claim 1, characterized in that, The dedicated table scheduling plugin also includes a data validation plugin; it performs data validation on the corresponding standardized scheduling table, marks detected abnormal data and prompts for correction, until there is no abnormal data in the corresponding standardized scheduling table, including: In response to the third click operation, the data verification plugin is triggered to perform data verification on the corresponding standardized scheduling table to detect whether there is abnormal data in the corresponding standardized scheduling table. The third click operation is the operation of the user clicking the function button of the data verification plugin on the scheduling plugin function tab. If abnormal data is detected in the corresponding standardized scheduling table, the data verification plugin marks the abnormal data in the corresponding standardized scheduling table and outputs a verification report, wherein the verification report is used to prompt the correction of the abnormal data. The system returns to the execution response of the third click operation, triggering the data verification plugin to perform data verification on the corresponding standardized scheduling table, detecting whether there is abnormal data in the corresponding standardized scheduling table, until there is no abnormal data in the corresponding standardized scheduling table.

4. The method according to claim 1, characterized in that, The server calls the cloud to perform production scheduling calculations based on the entered data using a multi-constraint dynamic adaptation and optimization strategy, resulting in the following final production scheduling results: The server calls the cloud to perform the following production scheduling calculation process: The production orders corresponding to the entered data are sorted according to work filtering and assignment rules to obtain the production order sorting; Allocate appropriate production resources to any work task corresponding to each target production order, wherein each target production order is a production order in the production order sorting; Based on the entered data, the current production constraints are identified, including order priority constraints, capacity constraints, changeover loss constraints, inventory limit constraints, minimum interval time constraints, and bottleneck resource constraints. Each target production order's work task is broken down into four task segments, and the time for each of the four task segments is calculated in conjunction with the production constraints. Based on the production order sorting, the production resources of any work task corresponding to each target production order, and the time of its four task segments, a preliminary production scheduling result is obtained; The preliminary production scheduling results are optimized to obtain the final production scheduling results.

5. The method according to claim 4, characterized in that, The preliminary production scheduling results are optimized to obtain the final production scheduling results, which include: The preliminary production scheduling results are optimized using a local search optimization strategy to obtain the preliminary optimized production scheduling results; The preliminary optimized production scheduling results are verified from multiple dimensions to confirm whether there are any anomalies in the preliminary optimized production scheduling results. If it is confirmed that there are no abnormalities in the preliminary optimized production scheduling results, then the preliminary optimized production scheduling results will be confirmed as the final production scheduling results. If it is confirmed that there is an anomaly in the preliminary optimized production scheduling result, the preliminary optimized production scheduling result is optimized a second time, and the production scheduling result after the second optimization is confirmed as the final production scheduling result.

6. The method according to any one of claims 1-5, characterized in that, The dedicated table scheduling plugin also includes a parameter update plugin that supports incremental data updates, and the method further includes: In response to the parameter update operation, the parameter update plugin is triggered to upload the changed data in the corresponding standardized scheduling table to the server. The parameter update operation is the operation of the user clicking the function button of the parameter update plugin on the scheduling plugin function tab after correcting and / or adding data in the corresponding standardized scheduling table. The server calls the cloud to update the final production scheduling result based on the changed data, and then sends the updated data in the final production scheduling result back to the local terminal, triggering the result download plugin to fill the corresponding table in the scheduling result table with the updated data.

7. The method according to claim 6, characterized in that, The process of updating the final production scheduling result by calling the cloud based on the changed data through the server includes: The server calls the cloud to execute the following production scheduling result update process: Identify the constraints corresponding to the changed data, and based on the constraints, determine the target work tasks affected by the changed data in the final production scheduling result; The production scheduling is recalculated for the target work task, and the final production scheduling result is updated based on the calculation result.

8. An APS device based on plug-in architecture and cloud collaboration, characterized in that, include: The deployment unit is used to deploy a dedicated table scheduling plugin on a local terminal to meet the production scheduling needs of the manufacturing industry. After deployment, a scheduling plugin function tab is added to the table menu bar. The scheduling plugin function tab includes function buttons for the dedicated table scheduling plugin. The dedicated table scheduling plugin includes a standardized scheduling table model plugin that supports custom field configuration, a data upload plugin that supports encrypted transmission, and a result download plugin. The first processing unit is configured to respond to the first click operation and trigger the standardized scheduling table model plugin to generate various standardized scheduling tables required by the manufacturing industry on the table workbook currently displayed on the local terminal. The first click operation is the operation of the user clicking the function button of the standardized scheduling table model plugin on the scheduling plugin function tab. The various standardized scheduling tables include a scheduling result table. The data entry unit is used to input the relevant data of the production plan information into the corresponding standardized scheduling tables in the various standardized scheduling tables. The verification unit is used to verify the data in the corresponding standardized scheduling table, mark the detected abnormal data and prompt for correction, until there is no abnormal data in the corresponding standardized scheduling table. The second processing unit is used to respond to the second click operation and trigger the data upload plugin to encrypt and upload the entered data in the corresponding standardized scheduling table to the server. The second click operation is the operation of the user clicking the function button of the data upload plugin on the scheduling plugin function tab. The calculation unit is used to call the cloud through the server to perform production scheduling calculation based on the input data using a multi-constraint dynamic adaptation and optimization strategy to obtain the final production scheduling result; the server transmits the final production scheduling result back to the local terminal as structured data in encrypted form, triggering the result download plugin to fill the structured data into the corresponding table of the scheduling result table.

9. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores instructions which are loaded and executed by the processor to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a computer, implements the method as described in any one of claims 1-7.

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