Production management system based on low-code platform

Abstract

The invention provides a production management system based on a low-code platform, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting production resource state data, order task data and process parameter data; the task decomposition module is used for decomposing the production task to generate task unit data; the configuration generation module is used for generating initial task configuration data according to the task unit data; the task scheduling module is used for receiving the initial task configuration data and generating a task scheduling scheme; the execution control module is used for distributing a production instruction to production equipment according to the task scheduling scheme and controlling the execution process of the production task; the dynamic updating module is used for dynamically adjusting task configuration and feeding back to the task scheduling module when a production task adjusting request is received in the production task execution process; the resource allocation scheme of the production management system can be automatically updated based on the data feedback of the production resources.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a production management system based on a low-code platform. Background Art

[0002] With the widespread application of production management systems, production management systems based on low-code platforms have gradually gained attention. Low-code platforms lower the threshold for system development through visual configuration tools and logic rule engines, and can quickly build functional modules of production management systems, such as data collection, task decomposition, resource scheduling and execution control. However, existing production management systems based on low-code platforms still have some technical defects in practical applications.

[0003] Specifically, the existing system has limitations in dynamic task scheduling and process adjustment. For example, during the workshop production process, when the state of production resources changes, such as temporary failures or overload, the system can only perform task execution order and resource allocation based on static rules, and it is difficult to implement dynamic adjustments based on data feedback of production resources, such as equipment, manpower, materials and energy. Once there is a conflict or anomaly in task scheduling, technicians still need to manually modify the configuration rules, which makes it difficult to quickly update the task configuration, resulting in production process delays and affecting production efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a production management system based on a low-code platform, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A production management system based on a low-code platform, the system comprising:

[0007] A data acquisition module is used to collect original production data, wherein the original production data includes production resource status data, order task data and process parameter data;

[0008] A task decomposition module is used to decompose the production task according to the original production data and generate task unit data, wherein the task unit data includes a task identifier, a task priority, a task production resource requirement and a task estimated processing time;

[0009] A configuration generation module, used to generate initial task configuration data according to the task unit data, wherein the initial task configuration data includes a task unit execution order, a resource allocation scheme and a task execution time;

[0010] The task scheduling module is used to receive the initial task configuration data and generate a task scheduling plan through the following steps:

[0011] According to the task execution sequence data in the initial task configuration data and the production resource status data in the original production data, the execution sequence of the task units is dynamically optimized to generate optimized execution sequence data;

[0012] According to the optimized execution sequence data, the idle time and real-time occupancy status of production resources are scheduled to generate resource scheduling plans;

[0013] Generate a task scheduling plan based on the optimized execution sequence data and resource scheduling plan;

[0014] An execution control module is used to distribute production instructions to production equipment according to the task scheduling plan, control the execution process of production tasks, and obtain production feedback data in real time, wherein the production feedback data includes production progress data, production resource status change data, and resource consumption data;

[0015] The dynamic update module is used to dynamically adjust the task configuration and provide feedback to the task scheduling module when a production task adjustment request is received during the production task execution process.

[0016] Preferably, the data acquisition module includes:

[0017] A data acquisition unit, used to acquire raw data, wherein the raw data includes production resource status data, order task data and process parameter data;

[0018] The data cleaning unit is used to remove duplications, fill in missing values, and standardize the data format of the original data to generate original production data.

[0019] Preferably, the task decomposition module includes:

[0020] The task identification unit is used to identify the production process and resource requirements in the order task based on the original production data and generate preliminary task data;

[0021] The task splitting unit is used to split the production task into several independent task units according to the preliminary task data, and assign a task identifier, task priority, task production resource requirements and task estimated processing time to each task unit to generate task unit data; the calculation formula of task priority is: ,in, Task Unit The priority of Task Unit The deadline for completion, is the current time, Task Unit The task execution time, Task Unit The set of all dependent task units, task units Other task units that need to be completed before execution, For dependent task units The degree of completion, For the task The total number of dependent task units, Task Unit A collection of all the production resources needed, For production resources Available time, Task Unit The total amount of production resources required, To obtain and The larger value of , and is the weight coefficient.

[0022] Preferably, the configuration generation module includes:

[0023] The resource evaluation unit is used to screen the available production resources according to the real-time occupancy of the current production resources and generate the available production resource data;

[0024] The resource matching unit is used to calculate the initial adaptability between the task unit and the available production resources according to the resource requirements of the task unit and the available production resource data, and generate resource adaptation data;

[0025] A task sorting unit is used to sort the task units according to their priorities, task dependencies and estimated processing time, and generate task sorting data;

[0026] The configuration calculation unit is used to calculate the comprehensive adaptability of the task unit and the production resources according to the resource adaptation data and the task sorting data, combined with the current state of the production resources, and generate resource allocation data; the calculation formula for the comprehensive adaptability of the task unit and the production resources is: ,in, Task Unit With The comprehensive adaptability of various production resources, For the The number of production resources, Task Unit For The first production resource The initial fitness of a resource instance, Task Unit For The first production resource The demand value of a resource instance, For the The first production resource The available capacity value of a resource instance, For the The first production resource The current load of resource instances, , and is the weight coefficient; , For the The weight coefficient of each demand item, Task Unit No. The requirements and The first production resource The matching degree of each resource instance;

[0027] , Task Unit No. The demand value of the demand item, For the The first production resource The available capacity value of a resource instance, for and The smaller value of ;

[0028] The configuration generation unit is used to combine the task sorting data and the resource allocation data to generate initial task configuration data, wherein the initial task configuration data includes the task unit execution order, the resource allocation scheme and the task execution time.

[0029] Preferably, according to the task execution sequence data in the initial task configuration data, combined with the production resource status data in the original production data, the execution sequence of the task units is dynamically optimized to generate the optimized execution sequence data, including:

[0030] According to the production resource status data in the original production data, the evaluation score of the operation status and availability of the current production resources is calculated to generate the production resource status evaluation data; the calculation formula of the evaluation score of the operation status and availability of the production resources is: ,in, For the The first production resource The running status and availability of each resource instance are evaluated. To be assigned to The number of task units per resource instance, Task Unit With The first production resource The resource adaptation weight of a resource instance, For the The first production resource The current load of resource instances, For the The first production resource The available time of each resource instance, For the The first production resource The health status score of each resource instance. is the weight coefficient; ;in, , For the The total number of parameters for resource instances, For the The weight coefficient of the parameter, is the production resource type discrimination function, For parameters The current value of For parameters The maximum allowed value of ;

[0031] According to the production resource status evaluation data and the task execution order in the initial task configuration data, the task units with abnormal production resource status or overload are reordered according to the task priority to generate task sequence optimization data;

[0032] According to the task sequence optimization data, the dependency of task units is verified and dynamically adjusted according to task priorities to generate optimized execution sequence data.

[0033] Preferably, according to the optimized execution sequence data, the idle time and real-time occupancy status of the production resources are scheduled to generate a resource scheduling plan, including:

[0034] According to the optimized execution sequence data, combined with the real-time occupancy information of production resources in the production resource status data, the resource allocation conflicts between task units are detected to generate resource conflict detection data;

[0035] According to the resource conflict detection data, the production resource allocation time or production resource occupancy of the task unit is adjusted according to the task priority to generate optimized resource allocation data;

[0036] According to the optimized resource allocation data, the idle time of production resources and the current load status of production resources in the production resource status data are extracted to calculate the load ratio of each production resource;

[0037] Calculate the system load balancing target value based on the load ratio of each production resource;

[0038] When the system balancing target value is greater than the preset balancing threshold, the production resources are adjusted according to the task priority, the allocation of high-load production resources is reduced, and the task units are allocated to low-load production resources according to the task priority to generate load balancing data;

[0039] Based on the load balancing data, the remaining resources are redistributed according to the task priority to generate a resource scheduling plan.

[0040] Preferably, the calculation formula of the system load balancing target value is: , is the system load balancing target value, is the total number of types of production resources, is the total number of task units, Task Unit Is it assigned to The first production resource resource instance, the value is 1 or 0, Task Unit The task execution time, For the The first production resource The maximum available time of a resource instance, For the The first production resource The load value of a resource instance in the optimized resource allocation data, For the The first production resource The load value of a resource instance in the resource allocation scheme, The cost of conflicts between task priorities and resource allocation, = , and is the adjustment coefficient;

[0041] Preferably, the execution control module includes:

[0042] An instruction distribution unit is used to send task execution instructions to production resources according to the task scheduling plan to start the production task;

[0043] The task monitoring unit is used to obtain production progress data, production resource status change data and resource consumption data in real time, and generate production feedback data;

[0044] The anomaly detection unit is used to identify abnormal situations in the execution process according to the production feedback number and output abnormal status information.

[0045] Preferably, the dynamic update module includes:

[0046] The status analysis unit is used to analyze the execution status of the current task and the use of production resources according to the production feedback number and abnormal status information, and generate task status analysis data;

[0047] A task adjustment unit is used to update the initial task configuration data in real time according to the task status analysis data and the production task adjustment request, and generate optimized task configuration data;

[0048] The configuration feedback unit is used to provide feedback to the task scheduling module based on the optimized task configuration data and regenerate the task scheduling plan.

[0049] Preferably, the system further comprises:

[0050] A feedback data closed-loop unit, used to associate the initial task configuration data with the optimized task configuration data to generate closed-loop feedback data;

[0051] The data analysis unit is used to analyze the execution efficiency, resource utilization and abnormality frequency of production tasks based on closed-loop feedback data, and generate a task execution optimization report;

[0052] The template generation unit is used to extract the configuration data and scheduling scheme for efficient task execution according to the task execution optimization report, and generate a reusable task template.

[0053] The above solution of the present invention includes at least the following beneficial effects:

[0054] First, the present invention uses a data acquisition module to comprehensively acquire the status data of production resources, order task data, and process parameter data, providing basic support for dynamic task scheduling. Compared with the existing static rule-based system, this module can not only update the operating status of production resources in real time, but also collect multi-dimensional raw data, including equipment load, manpower allocation, material consumption, and energy utilization, providing a more accurate data basis for task configuration and resource allocation.

[0055] Secondly, through the task decomposition module, complex production tasks are broken down into task units, and structured management is performed based on parameters such as task identifiers, task priorities, production resource requirements, and estimated processing time. The introduction of the task decomposition module has greatly improved the refinement of task management, avoiding resource waste and scheduling conflicts caused by task ambiguity or unclear priorities in the existing system. At the same time, the module assigns priorities to task units based on original production data, ensuring that production resources are allocated first to key tasks, thereby improving overall production efficiency.

[0056] Thirdly, the configuration generation module of this system generates initial task configuration data based on task decomposition and task unit data, including the execution order of task units, resource allocation plan and task execution time. This module builds a more scientific task configuration plan by comprehensively considering the current state of production resources, task priorities and dependencies between tasks. Especially when production resources change dynamically, the module can respond quickly and avoid delays or conflicts in task scheduling due to the limitations of static rules by adjusting the task order and resource allocation plan.

[0057] In addition, the task scheduling module in the present invention dynamically optimizes the task execution order by combining the original production data with the initial task configuration data, reasonably allocates the idle time and real-time occupancy status of production resources, and generates an optimized task scheduling plan. Unlike the traditional scheduling mode based on static rules, this module can dynamically adjust the scheduling plan based on real-time data. For example, in workshop production, when a certain device suspends operation due to a fault, the system can automatically reallocate tasks to other idle devices without the need for technicians to manually modify the configuration rules. This dynamic scheduling capability not only improves the efficiency of task execution, but also significantly reduces production interruptions caused by resource conflicts or anomalies.

[0058] Based on the task scheduling scheme, the execution control module can automatically distribute production instructions to production equipment, and obtain production progress data, production resource status change data and resource consumption data in real time, forming a production feedback closed loop. Through the dynamic collection and analysis of feedback data, the system can further optimize task configuration and resource scheduling to ensure the smooth execution of tasks. In addition, the real-time monitoring capability provided by this module enables managers to grasp the execution status of the production process at any time, improving the transparency and controllability of production management.

[0059] Finally, the dynamic update module in the present invention significantly improves the adaptability and flexibility of the system. During the production process, when the production resource status or production task changes, the dynamic update module can quickly adjust the task configuration based on real-time feedback, and promptly feed back the adjustment results to the task scheduling module. This dynamic adjustment capability solves the tedious process of manually modifying rules in the existing system, and can quickly update the scheduling plan to ensure that the production task does not affect production efficiency due to configuration delays.

[0060] In summary, the present invention realizes the intelligent and automated functions of the low-code platform production management system through modular design. The system's dynamic task scheduling and process adjustment capabilities make full use of production resource status data, realize real-time optimization and adjustment based on data feedback, overcome the limitations of static rules in the prior art, and avoid the impact of production scheduling lag on production efficiency. The system not only improves the flexibility of task execution, but also greatly improves resource utilization efficiency and the intelligent level of production management, providing technical support for the efficient management of modern production workshops. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is an architecture diagram of a production management system based on a low-code platform provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0063] like Figure 1 As shown, an embodiment of the present invention proposes a production management system based on a low-code platform, the system comprising:

[0064] A data acquisition module is used to collect original production data, wherein the original production data includes production resource status data, order task data and process parameter data;

[0065] A task decomposition module is used to decompose the production task according to the original production data and generate task unit data, wherein the task unit data includes a task identifier, a task priority, a task production resource requirement and a task estimated processing time;

[0066] A configuration generation module, used to generate initial task configuration data according to the task unit data, wherein the initial task configuration data includes a task unit execution order, a resource allocation scheme and a task execution time;

[0067] The task scheduling module is used to receive the initial task configuration data and generate a task scheduling plan through the following steps:

[0068] According to the task execution sequence data in the initial task configuration data and the production resource status data in the original production data, the execution sequence of the task units is dynamically optimized to generate optimized execution sequence data;

[0069] According to the optimized execution sequence data, the idle time and real-time occupancy status of production resources are scheduled to generate resource scheduling plans;

[0070] Generate a task scheduling plan based on the optimized execution sequence data and resource scheduling plan;

[0071] An execution control module is used to distribute production instructions to production equipment according to the task scheduling plan, control the execution process of production tasks, and obtain production feedback data in real time, wherein the production feedback data includes production progress data, production resource status change data, and resource consumption data;

[0072] The dynamic update module is used to dynamically adjust the task configuration and provide feedback to the task scheduling module when a production task adjustment request is received during the production task execution process.

[0073] In an embodiment of the present invention, a modular, dynamic and intelligent production management system is provided through an architectural design based on a low-code platform, which comprehensively improves the task decomposition, resource scheduling and dynamic update capabilities, and solves the problems of production scheduling lag and complex adjustment in the prior art.

[0074] The introduction of the low-code platform significantly lowers the development threshold. Based on the visual configuration tool and the logic rule engine, users can quickly implement the functional modules of the production management system, including data collection, task decomposition, resource scheduling, and execution control modules, reducing the reliance on complex code development. Compared with the traditional production management system, the present invention not only shortens the system development cycle, but also improves the flexibility and adaptability of production task management.

[0075] The data collection module makes full use of the data integration capabilities of the low-code platform to collect production resource status data, order task data, and process parameter data in real time through visual configuration, avoiding the inefficiency of traditional manual or offline collection modes. The task decomposition module uses the logic rule engine provided by the low-code platform to decompose complex tasks into task units and dynamically adjust the execution order between tasks based on priority rules. The configuration generation module combines real-time data with the visualization tools of the low-code platform to achieve rapid matching between task units and production resources, significantly improving the efficiency of task configuration.

[0076] The task scheduling module is based on the logic engine of the low-code platform, dynamically combining the real-time collected production resource status data with the task configuration data, and automatically regenerates the optimized task scheduling plan when the production resource status changes, such as equipment failure or overload, avoiding the need to write complex code for adjustment in traditional systems. The execution control module uses the rapid iteration capability of the low-code platform to convert the task scheduling plan into production instructions and distribute them to the equipment, while feeding back the status change data of the production process to the system in real time, forming a dynamic closed-loop management.

[0077] The dynamic update module combines the visual configuration capabilities of the low-code platform to achieve real-time and ease of use in production task adjustment. For example, when the order task requirements change, users can quickly adjust the task configuration through a drag-and-drop interface without relying on professional developers to make modifications. Overall, the present invention, with the support of a low-code platform, achieves rapid development of modular functions, efficient adjustment of dynamic scheduling, and intelligent management of the production process, which comprehensively improves the response speed of production management and resource utilization efficiency.

[0078] In a preferred embodiment of the present invention, the data acquisition module includes:

[0079] A data acquisition unit, used to acquire raw data, wherein the raw data includes production resource status data, order task data and process parameter data;

[0080] The data cleaning unit is used to remove duplications, fill in missing values, and standardize the data format of the original data to generate original production data.

[0081] In an embodiment of the present invention, the data acquisition module provides a solution for rapid deployment and efficient management of production data based on the characteristics of the low-code platform. Through the integration function of the low-code platform, the data acquisition module can quickly connect to various types of data sources, such as databases, production equipment sensors, and order management systems, so as to efficiently collect production resource status data, order task data, and process parameter data. Compared with traditional development methods, the low-code platform significantly shortens the development cycle of the data acquisition module and ensures the accuracy and reliability of data collection through standardized interface management.

[0082] The data cleaning unit uses the built-in data processing components of the low-code platform to automatically clean and standardize the original production data. Deduplication, missing value completion, and data format standardization can all be flexibly configured through the rule engine of the low-code platform, and users can optimize data quality without writing complex code. This data cleaning method based on the low-code platform not only improves data processing efficiency, but also ensures the visualization and traceability of the cleaning results, providing a high-quality data foundation for task decomposition and configuration generation of downstream modules.

[0083] In addition, the visual dashboard function provided by the low-code platform can monitor the data collection and cleaning process in real time and provide users with operational suggestions. Through the modular deployment method of the low-code platform, the data collection module can be quickly applied in different production environments, significantly improving the flexibility and operability of data management.

[0084] In a preferred embodiment of the present invention, the task decomposition module includes:

[0085] The task identification unit is used to identify the production process and resource requirements in the order task based on the original production data and generate preliminary task data;

[0086] The task splitting unit is used to split the production task into several independent task units according to the preliminary task data, and assign a task identifier, task priority, task production resource requirements and task estimated processing time to each task unit to generate task unit data; the calculation formula of task priority is: ,in, Task Unit The priority of Task Unit The deadline for completion, is the current time, Task Unit The task execution time, Task Unit The set of all dependent task units, task units Other task units that need to be completed before execution, For dependent task units The degree of completion, For the task The total number of dependent task units, Task Unit A collection of all the production resources needed, For production resources Available time, Task Unit The total amount of production resources required, To obtain and The larger value of , and is the weight coefficient.

[0087] In the embodiment of the present invention, the task decomposition module based on the low-code platform realizes the automation and visualization of production task decomposition, greatly improving the efficiency and usability of task management. The task identification unit can quickly parse the original production data, identify the production process and resource requirements of the order task, and generate preliminary task data through the intelligent rule engine built into the low-code platform. Users can adjust the rule parameters through the visual interface of the low-code platform to achieve rapid optimization and customization of the task identification logic.

[0088] The task splitting unit uses the automated workflow function of the low-code platform to split complex order tasks into independent task units, and dynamically assigns priority to each task unit based on factors such as the urgency of the task, the completion of dependent tasks, and resource waiting time. Through the real-time interactive capabilities of the low-code platform, users can view the status information of each task unit in real time during the task splitting process, including task identifiers, resource requirements, and estimated processing time, so as to more intuitively control the production plan.

[0089] The calculation formula for task priority is implemented in a configurable manner with the support of the low-code platform, and the parameters in the formula can be adjusted through the parameterized configuration tool of the low-code platform. The implementation process of the formula is transparent to the user, and the user can quickly modify the setting of the weight coefficient through the drag-and-drop visual configuration interface to meet the production needs in different scenarios. This feature of the low-code platform makes the task decomposition module not only efficient and reliable, but also greatly reduces the threshold for using the system, improving the system's adaptability and user experience.

[0090] In a preferred embodiment of the present invention, the configuration generation module includes:

[0091] The resource evaluation unit is used to screen the available production resources according to the real-time occupancy of the current production resources and generate the available production resource data;

[0092] The resource matching unit is used to calculate the initial adaptability between the task unit and the available production resources according to the resource requirements of the task unit and the available production resource data, and generate resource adaptation data;

[0093] A task sorting unit is used to sort the task units according to their priorities, task dependencies and estimated processing time, and generate task sorting data;

[0094] The configuration calculation unit is used to calculate the comprehensive adaptability of the task unit and the production resources according to the resource adaptation data and the task sorting data, combined with the current state of the production resources, and generate resource allocation data; the calculation formula for the comprehensive adaptability of the task unit and the production resources is: ,in, Task Unit With The comprehensive adaptability of various production resources, For the The number of production resources, Task Unit For The first production resource The initial fitness of a resource instance, Task Unit For The first production resource The demand value of a resource instance, For the The first production resource The available capacity value of a resource instance, For the The first production resource The current load of resource instances, , and is the weight coefficient; , For the The weight coefficient of each demand item, Task Unit No. The requirements and The first production resource The matching degree of each resource instance; , Task Unit No. The demand value of the demand item, For the The first production resource The available capacity value of a resource instance, for and The smaller value of ;

[0095] The configuration generation unit is used to combine the task sorting data and the resource allocation data to generate initial task configuration data, wherein the initial task configuration data includes the task unit execution order, the resource allocation scheme and the task execution time.

[0096] In an embodiment of the present invention, the configuration generation module relies on the low-code platform to realize the automation and visual management of the entire process of resource evaluation, resource matching, task sorting and configuration calculation. The resource evaluation unit uses the real-time data processing capability provided by the low-code platform to efficiently analyze the real-time occupancy of production resources, screen out available resources and generate available production resource data. This processing method ensures the accuracy and efficiency of the resource evaluation process, and supports users to monitor and adjust the evaluation results in real time through the visual interface of the low-code platform.

[0097] The resource matching unit uses the intelligent computing components of the low-code platform to automatically calculate the initial compatibility between the task unit and the resource based on the resource requirements of the task unit and the available production resource data, and generates resource adaptation data. With the support of the low-code platform, the adaptation calculation process is completely modular, and users can flexibly adjust the adaptation logic without writing code.

[0098] The task sorting unit automatically sorts the task units according to the task priority, task dependency and estimated processing time through the priority calculation tool built into the low-code platform, and generates task sorting data. This sorting mechanism based on the low-code platform is not only efficient and reliable, but also supports users to customize the sorting logic to meet the needs of different production scenarios.

[0099] The configuration calculation unit is the core of the module, and its comprehensive adaptability calculation formula fully reflects the flexibility and intelligence of the low-code platform. The formula dynamically balances the impact of various parameters by considering the number of resources, current status, and needs of task units to ensure the scientific and rational allocation of resources. The configurable formula tool of the low-code platform allows users to easily adjust the weight coefficients and calculation logic in the formula to quickly adapt to the needs of different production environments.

[0100] Through the visualization and automation capabilities of the low-code platform, the configuration generation module greatly improves the efficiency and accuracy of production resource allocation, providing strong support for the efficient execution of complex production tasks.

[0101] In a preferred embodiment of the present invention, according to the task execution sequence data in the initial task configuration data, combined with the production resource status data in the original production data, the execution sequence of the task units is dynamically optimized to generate the optimized execution sequence data, including:

[0102] According to the production resource status data in the original production data, the evaluation score of the operation status and availability of the current production resources is calculated to generate the production resource status evaluation data; the calculation formula of the evaluation score of the operation status and availability of the production resources is: ,in, For the The first production resource The running status and availability of each resource instance are evaluated. To be assigned to The number of task units per resource instance, Task Unit With The first production resource The resource adaptation weight of a resource instance, For the The first production resource The current load of resource instances, For the The first production resource The available time of each resource instance, For the The first production resource The health status score of each resource instance. is the weight coefficient; ;in, , For the The total number of parameters for resource instances, For the The weight coefficient of the parameter, is the production resource type discrimination function, For parameters The current value of For parameters The maximum allowed value of ;

[0103] According to the production resource status evaluation data and the task execution order in the initial task configuration data, the task units with abnormal production resource status or overload are reordered according to the task priority to generate task sequence optimization data;

[0104] According to the task sequence optimization data, the dependency of task units is verified and dynamically adjusted according to task priorities to generate optimized execution sequence data.

[0105] In an embodiment of the present invention, the task execution sequence dynamic optimization module based on the low-code platform significantly improves the intelligent level of production management through real-time analysis and dynamic adjustment of task configuration data. The real-time data integration function provided by the low-code platform enables the system to quickly obtain production resource status data, including the operating status and availability of resources. The module automatically generates production resource status assessment data through the rule engine and computing power of the low-code platform. Compared with the traditional manual assessment method, this low-code-based implementation method not only improves the accuracy of resource status analysis, but also greatly shortens the calculation time.

[0106] The production resource operation status and availability evaluation formula is flexibly implemented in a parameterized manner with the support of the low-code platform. The formula fully reflects the real-time operation status of resources by dynamically calculating the number of task units, resource adaptation weights, resource loads, and health status scores. Through the formula visualization function provided by the low-code platform, users can clearly and intuitively understand the calculation logic of each parameter and adjust the weights and parameter ranges according to actual needs. For example, for resources with high loads but good health, their evaluation scores will be adjusted accordingly to optimize allocation decisions.

[0107] In addition, the automated process engine of the low-code platform supports the real-time optimization of the task execution order by the module. Based on the production resource status evaluation data, the system reorders the task units with abnormal resources or overload, and dynamically adjusts the task dependencies. The entire optimization process is displayed through the visual interface of the low-code platform, allowing users to grasp the process and results of task adjustment in real time. The optimized execution order data finally generated significantly improves resource utilization efficiency and production plan flexibility.

[0108] In a preferred embodiment of the present invention, the idle time and real-time occupancy status of production resources are scheduled according to the optimized execution sequence data to generate a resource scheduling scheme, including:

[0109] According to the optimized execution sequence data, combined with the real-time occupancy information of production resources in the production resource status data, the resource allocation conflicts between task units are detected to generate resource conflict detection data;

[0110] According to the resource conflict detection data, the production resource allocation time or production resource occupancy of the task unit is adjusted according to the task priority to generate optimized resource allocation data;

[0111] According to the optimized resource allocation data, the idle time of production resources and the current load status of production resources in the production resource status data are extracted to calculate the load ratio of each production resource;

[0112] According to the load ratio of each production resource, the system load balancing target value is calculated; the calculation formula of the system load balancing target value is: , is the system load balancing target value, is the total number of types of production resources, is the total number of task units, Task Unit Is it assigned to The first production resource resource instance, the value is 1 or 0, Task Unit The task execution time, For the The first production resource The maximum available time of a resource instance, For the The first production resource The load value of a resource instance in the optimized resource allocation data, For the The first production resource The load value of a resource instance in the resource allocation scheme, The cost of conflicts between task priorities and resource allocation, = , and is the adjustment coefficient;

[0113] When the system balancing target value is greater than the preset balancing threshold, the production resources are adjusted according to the task priority, the allocation of high-load production resources is reduced, and the task units are allocated to low-load production resources according to the task priority to generate load balancing data;

[0114] Based on the load balancing data, the remaining resources are redistributed according to the task priority to generate a resource scheduling plan.

[0115] In an embodiment of the present invention, the module further performs in-depth scheduling of the optimized execution order data and generates a resource scheduling plan through the real-time processing capability of the low-code platform. Using the data integration function provided by the low-code platform, the module can efficiently detect resource allocation conflicts between task units and generate resource conflict detection data. Users can intuitively view the conflict points and impact range through the visualization tools of the low-code platform, so as to quickly formulate adjustment strategies.

[0116] The core of the module is to dynamically adjust the resource allocation time and occupancy of the task unit based on the resource conflict detection data. The process automation function of the low-code platform allows the adjustment process to flexibly adapt to the changing production environment. For example, the system calculates the task priority and prioritizes the allocation of critical tasks to highly adaptable resources, while avoiding conflicts caused by low-priority tasks occupying resources. The optimized resource allocation data reflects the system's dual guarantee of resource utilization efficiency and task priority.

[0117] The module uses the computing power of the low-code platform to automatically extract the idle time and real-time load status of production resources and calculate the load ratio of each resource. Subsequently, the system further uses the formula to calculate the system load balancing target value to evaluate the overall balance of resource allocation. If the target value exceeds the threshold, the module uses the task adjustment function of the low-code platform to dynamically reduce the task allocation of high-load resources and transfer task units to low-load resources to generate load balancing data. This process significantly improves the rationality of resource allocation and production efficiency.

[0118] Finally, based on the load balancing data, the system optimizes the remaining resources through the secondary allocation function of the low-code platform and generates the final resource scheduling plan. The visualization of the entire process allows users to easily track the adjustment process of tasks and resources, greatly enhancing the transparency and convenience of scheduling management.

[0119] More specifically, based on the optimized execution sequence data and combined with the real-time occupancy information of production resources in the production resource status data, resource allocation conflicts between task units are detected and resource conflict detection data is generated, specifically including:

[0120] Analysis of task execution order: Based on the optimized execution order data, the system extracts the execution time, priority and resource requirements of each task unit. The optimized execution order data is the result generated by the previous dynamic optimization process, which contains the scheduling order and resource allocation information of the tasks.

[0121] Collection of real-time occupancy information of production resources: The system obtains the occupancy status information of all current production resources through the data collection module, including the resource instances occupied by the current task, the available time period of the resources, and the load information.

[0122] Implementation of conflict detection logic: For each task unit, the system matches its resource requirements with the occupancy status of production resources. If there is a time overlap between the resource requirements of the task unit and the currently occupied resources or if there is an over-allocation of resources, it is considered a conflict. For example, when two task units are assigned to the same device at the same time, the system will identify the conflict.

[0123] When detecting a conflict, the system calculates the severity of the conflict, including the degree of time overlap of resource usage and the difference in priority of the conflicting tasks. In this way, the system is able to distinguish between conflicts between high-priority tasks and low-priority tasks.

[0124] Generate resource conflict detection data: The system stores the detected conflict records in a structured form, for example, recording the number of the conflicting task, the number of the resource instance, the time period of the conflict, and the severity of the conflict. The generated data can be used in the subsequent resource allocation adjustment module to guide the resolution of conflicts.

[0125] Through the conflict detection function, the system can proactively discover and locate problems in resource allocation, provide data support for subsequent resource adjustments, and avoid resource contention and task delays in the production process.

[0126] In a preferred embodiment of the present invention, the execution control module includes:

[0127] An instruction distribution unit is used to send task execution instructions to production resources according to the task scheduling plan to start the production task;

[0128] The task monitoring unit is used to obtain production progress data, production resource status change data and resource consumption data in real time, and generate production feedback data;

[0129] The anomaly detection unit is used to identify abnormal situations in the execution process according to the production feedback number and output abnormal status information.

[0130] In an embodiment of the present invention, the execution control module based on the low-code platform realizes the full process automation management from task instruction distribution to anomaly detection. The module quickly distributes the execution instructions in the scheduling plan to relevant production equipment or resources through the task scheduling tool provided by the low-code platform. Compared with the traditional system, the real-time instruction distribution function provided by the low-code platform significantly shortens the task startup time while ensuring the accuracy and reliability of instruction delivery.

[0131] The task monitoring unit uses the real-time data stream processing function of the low-code platform to dynamically collect production progress data, resource status change data, and resource consumption data, and generate production feedback data. The built-in visual dashboard function of the low-code platform supports users to monitor the key indicators of task execution in real time and clearly understand the progress of task completion and resource usage.

[0132] The anomaly detection unit relies on the rule engine of the low-code platform to quickly identify and analyze anomalies in production feedback data. For example, when resource consumption data is abnormally high or the task execution time exceeds expectations, the system can issue an alarm in real time through the rule detection module and generate abnormal status information. Through the automated response mechanism of the low-code platform, users can quickly handle anomalies and reduce production losses caused by failures or delays.

[0133] Overall, this module optimizes the full life cycle management of production task execution through the intelligence and visualization capabilities of the low-code platform, realizes automation and intelligence from task distribution to feedback processing, and improves the stability of the system and production efficiency.

[0134] In a preferred embodiment of the present invention, the dynamic update module includes:

[0135] The status analysis unit is used to analyze the execution status of the current task and the use of production resources according to the production feedback number and abnormal status information, and generate task status analysis data;

[0136] A task adjustment unit is used to update the initial task configuration data in real time according to the task status analysis data and the production task adjustment request, and generate optimized task configuration data;

[0137] The configuration feedback unit is used to provide feedback to the task scheduling module based on the optimized task configuration data and regenerate the task scheduling plan.

[0138] In the embodiment of the present invention, the dynamic update module, with the support of the low-code platform, realizes the real-time update and closed-loop feedback of the task configuration, providing an efficient solution for task adjustment in a complex production environment. The status analysis unit analyzes the production feedback data and abnormal status information in real time through the analysis component of the low-code platform to generate task status analysis data. These data can fully reflect the execution status of the current task and the use of production resources, providing a scientific basis for subsequent adjustments.

[0139] The task adjustment unit uses the dynamic rule engine of the low-code platform to update the initial task configuration data in real time and generate optimized task configuration data based on task status analysis data and production task adjustment requests. The parameterized design of the low-code platform allows users to flexibly adjust task configuration rules according to actual needs, and preview the adjusted results in real time to ensure the rationality and accuracy of the adjustment. For example, when a task needs to be reallocated due to insufficient resources, the system can quickly identify and update the resource allocation plan for the task to avoid production interruptions.

[0140] The configuration feedback unit transmits the optimized task configuration data to the task scheduling module through the feedback mechanism of the low-code platform to achieve closed-loop management. The real-time interactive capability of the low-code platform ensures that the updated data takes effect quickly and provides support for the dynamic adjustment of the system. The closed-loop feedback mechanism not only improves the flexibility and stability of the system, but also significantly reduces production delays caused by task adjustments.

[0141] More specifically, the task adjustment unit includes:

[0142] Collection of production feedback data: The anomaly detection unit obtains real-time feedback data during the production process through the task monitoring unit. These data include production progress data (such as task completion rate), resource status change data (such as equipment failure rate) and resource consumption data (such as raw material usage).

[0143] Setting of anomaly detection rules: The system predefines a series of anomaly rules, such as: the task execution time exceeds the threshold of the expected time. The load of the equipment exceeds the safe operating range. The raw material consumption rate exceeds the normal range. Users can customize new anomaly detection rules through the rule engine of the low-code platform to meet the needs of different production scenarios.

[0144] Real-time anomaly detection logic: The anomaly detection unit analyzes feedback data in real time through the rule engine. For example, when the task execution time exceeds the predetermined threshold, the system triggers an anomaly alarm and generates a detailed anomaly record, including the task identifier, anomaly type, trigger time, and related resource information.

[0145] Generation of abnormal status information: For each abnormal situation, the system generates abnormal status information and notifies the user in a visual form. At the same time, the system can pass the abnormal information to the dynamic update module for adjusting task configuration or resource allocation.

[0146] The introduction of the anomaly detection unit has greatly improved the reliability and safety of the production task execution process, and can help users quickly identify and handle emergencies, reducing production losses caused by anomalies.

[0147] In a preferred embodiment of the present invention, the system further comprises:

[0148] A feedback data closed-loop unit, used to associate the initial task configuration data with the optimized task configuration data to generate closed-loop feedback data;

[0149] The data analysis unit is used to analyze the execution efficiency, resource utilization and abnormality frequency of production tasks based on closed-loop feedback data, and generate a task execution optimization report;

[0150] The template generation unit is used to extract the configuration data and scheduling scheme for efficient task execution according to the task execution optimization report, and generate a reusable task template.

[0151] In the embodiment of the present invention, by adding a feedback data closed-loop unit, a data analysis unit and a template generation unit, the system further enhances the data management and optimization functions. The feedback data closed-loop unit associates the initial task configuration data with the optimized task configuration data to generate closed-loop feedback data. Through the association analysis of historical task configurations, the unit provides important basic data for the continuous optimization of production management.

[0152] Based on closed-loop feedback data, the data analysis unit conducts in-depth analysis of the execution efficiency, resource utilization, and frequency of abnormalities of production tasks, and generates task execution optimization reports. This function not only improves the transparency of production management, but also provides strong decision-making support for production scheduling. For example, by analyzing the changing trend of resource utilization, potential resource waste problems can be discovered, and resource allocation strategies can be adjusted in a timely manner.

[0153] The template generation unit extracts the configuration data and scheduling scheme for efficient task execution based on the task execution optimization report, and generates a reusable task template. This template-based management method greatly improves the efficiency of production planning and accumulates sustainable production experience for enterprises. For example, when the system recognizes that a specific configuration scheme for a certain type of task has high execution efficiency, it can automatically generate a template for it to be quickly called by subsequent tasks. This method not only improves the standardization of task configuration, but also significantly reduces the repetitive workload of task configuration.

[0154] More specifically, the feedback data closed-loop unit includes:

[0155] The initial task configuration is associated with the optimized task configuration through the task identifier and resource identifier. For example, task 1 is assigned resource A in the initial configuration and is changed to resource B in the optimized configuration. The system records this adjustment.

[0156] The system compares the task allocation, resource allocation, and execution time in the initial configuration and the optimized configuration, and automatically generates a change record. For example:

[0157] Original allocation: Task unit 1 → Resource A, time period 10:00-12:00;

[0158] After adjustment: Task unit 1 → Resource B, the time period is changed to 11:00-13:00.

[0159] The system records these change data as feedback data, including the reasons for the adjustment (such as resource conflicts, priority changes) and the performance differences before and after the adjustment.

[0160] The system uses a database (such as a relational database or a NoSQL database) to store the generated feedback data. The data table structure includes: task identifier, initial resource configuration, optimized resource configuration, adjustment time, and adjustment reason.

[0161] While storing data, it also provides data indexing function to facilitate subsequent query and analysis.

[0162] The system displays the changing trend of feedback data through a visual interface, such as the multiple adjustment records of a task configuration. Through charts (such as Gantt charts or line charts), users can intuitively understand the effect of optimization.

[0163] The feedback data closed-loop unit forms a dynamic learning mechanism that can help users identify deficiencies in production management and make continuous improvements, providing a solid data foundation for the data analysis unit and template generation unit.

[0164] More specifically, the data analysis unit includes:

[0165] Collect closed-loop feedback data, including task execution efficiency, resource utilization, exception frequency, etc.

[0166] The data analysis unit cleans the collected data, such as removing duplicate data, filling in missing data, and standardizing the data into a unified structured format (such as CSV or JSON).

[0167] By comparing the actual completion time of the task unit with the estimated time, the execution efficiency of the task unit is calculated. Tasks with efficiency lower than the preset threshold will be marked as inefficient tasks.

[0168] By calculating resource utilization through resource allocation and idle time in the feedback data, resource instances with low utilization will be identified as potential resource waste points.

[0169] Count the number of exceptions during the execution of each task or resource instance and generate an exception distribution graph.

[0170] The system identifies the root cause of problems through correlation analysis. For example, low resource utilization may be related to improper task prioritization or resource allocation conflicts.

[0171] The system supports multi-dimensional cross-analysis (such as the relationship between task type and resource performance) and evaluates the optimization space of production management from multiple angles.

[0172] The core content of the report includes the characteristics of efficient task units and applicable experience. The specific reasons for inefficient task units and resource waste. Optimization suggestions are provided, such as adjusting resource allocation strategies or improving the rationality of task priorities. The report is presented in a visual way, including pie charts, bar charts, heat maps, etc., which is convenient for users to quickly understand.

[0173] The data analysis unit conducts scientific analysis and modeling based on closed-loop data to help users fully understand the operating status of the production task unit and provide data-driven optimization suggestions.

[0174] More specifically, the template generation unit includes:

[0175] The system selects tasks with high execution efficiency, high resource utilization and low exception rate from the optimization report.

[0176] Extract the key configuration parameters of these task units, including task allocation rules, resource adaptation schemes, and execution order. For example, if the optimization report shows that task A has the highest execution efficiency when using resource type X, then extract the allocation scheme.

[0177] Generate templated rules based on the extracted feature parameters. For example, if the task priority is high and the resource type is Y, it is recommended to allocate resource instance Z. Template rules use conditional statements and parameterized configurations to facilitate flexible application in different task scenarios.

[0178] The generated task templates are stored in the template library in a structured manner. For example, a template library entry contains information such as template number, applicable task type, template parameters, creation time, etc. The system provides version management functions for templates, supporting users to iteratively update templates.

[0179] Users can quickly call templates through the template library for the configuration of new task units. When calling, the system will dynamically adjust the template parameters according to the characteristics of the current task unit. For example: if the execution environment of the new task unit is different from the recommended resources in the template, the system will automatically adjust the resource type with the highest adaptability.

[0180] The template generation unit improves the standardization and automation level of task configuration through efficient task feature extraction and rule templateization. The reusability of templates reduces the complexity of task unit configuration and accumulates sustainable optimization production experience for enterprises.

[0181] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A production management system based on a low-code platform, characterized in that: The system comprises: A data acquisition module is used to collect original production data, wherein the original production data includes production resource status data, order task data and process parameter data; A task decomposition module is used to decompose the production task according to the original production data and generate task unit data, wherein the task unit data includes a task identifier, a task priority, a task production resource requirement and a task estimated processing time; A configuration generation module, used to generate initial task configuration data according to the task unit data, wherein the initial task configuration data includes a task unit execution order, a resource allocation scheme and a task execution time; The task scheduling module is used to receive the initial task configuration data and generate a task scheduling plan through the following steps: According to the task execution sequence data in the initial task configuration data and the production resource status data in the original production data, the execution sequence of the task units is dynamically optimized to generate optimized execution sequence data; According to the optimized execution sequence data, the idle time and real-time occupancy status of production resources are scheduled to generate resource scheduling plans; Generate a task scheduling plan based on the optimized execution sequence data and resource scheduling plan; An execution control module is used to distribute production instructions to production equipment according to the task scheduling plan, control the execution process of production tasks, and obtain production feedback data in real time, wherein the production feedback data includes production progress data, production resource status change data, and resource consumption data; The dynamic update module is used to dynamically adjust the task configuration and provide feedback to the task scheduling module when a production task adjustment request is received during the production task execution process.

2. A production management system based on a low-code platform according to claim 1, characterized in that: The data acquisition module comprises: A data acquisition unit, used to acquire raw data, wherein the raw data includes production resource status data, order task data and process parameter data; The data cleaning unit is used to remove duplications, fill in missing values, and standardize the data format of the original data to generate original production data.

3. A production management system based on a low-code platform according to claim 2, characterized in that: The task decomposition module includes: The task identification unit is used to identify the production process and resource requirements in the order task based on the original production data and generate preliminary task data; The task splitting unit is used to split the production task into several independent task units according to the preliminary task data, and assign a task identifier, task priority, task production resource requirements and task estimated processing time to each task unit to generate task unit data; the calculation formula of task priority is: ,in, Task Unit The priority of Task Unit The deadline for completion, is the current time, Task Unit The task execution time, Task Unit The set of all dependent task units, task units Other task units that need to be completed before execution, For dependent task units The degree of completion, For the task The total number of dependent task units, Task Unit A collection of all the production resources needed, For production resources Available time, Task Unit The total amount of production resources required, To obtain and The larger value of , and is the weight coefficient.

4. A production management system based on a low-code platform according to claim 3, characterized in that: The configuration generation module includes: The resource evaluation unit is used to screen the available production resources according to the real-time occupancy of the current production resources and generate the available production resource data; The resource matching unit is used to calculate the initial adaptability between the task unit and the available production resources according to the resource requirements of the task unit and the available production resource data, and generate resource adaptation data; A task sorting unit is used to sort the task units according to their priorities, task dependencies and estimated processing time, and generate task sorting data; The configuration calculation unit is used to calculate the comprehensive adaptability of the task unit and the production resources according to the resource adaptation data and the task sorting data, combined with the current state of the production resources, and generate resource allocation data; the calculation formula for the comprehensive adaptability of the task unit and the production resources is: ,in, Task Unit With The comprehensive adaptability of various production resources, For the The number of production resources, Task Unit For The first production resource The initial fitness of a resource instance, Task Unit For The first production resource The demand value of a resource instance, For the The first production resource The available capacity value of a resource instance, For the The first production resource The current load of resource instances, , and is the weight coefficient; , For the The weight coefficient of each demand item, Task Unit No. The requirements and The first production resource The matching degree of each resource instance; , Task Unit No. The demand value of the demand item, For the The first production resource The available capacity value of a resource instance, for and The smaller value of ; The configuration generation unit is used to combine the task sorting data and the resource allocation data to generate initial task configuration data, wherein the initial task configuration data includes the task unit execution order, the resource allocation scheme and the task execution time.

5. A production management system based on a low-code platform according to claim 4, characterized in that: According to the task execution sequence data in the initial task configuration data, combined with the production resource status data in the original production data, the execution sequence of the task units is dynamically optimized to generate optimized execution sequence data, including: According to the production resource status data in the original production data, the evaluation score of the operation status and availability of the current production resources is calculated to generate the production resource status evaluation data; the calculation formula of the evaluation score of the operation status and availability of the production resources is: ,in, For the The first production resource The running status and availability of each resource instance are evaluated. To be assigned to The number of task units per resource instance, Task Unit With The first production resource The resource adaptation weight of a resource instance, For the The first production resource The current load of resource instances, For the The first production resource The available time of each resource instance, For the The first production resource The health status score of each resource instance. is the weight coefficient; ;in, , For the The total number of parameters for resource instances, For the The weight coefficient of the parameter, is the production resource type discrimination function, For parameters The current value of For parameters The maximum allowed value of ; According to the production resource status evaluation data and the task execution order in the initial task configuration data, the task units with abnormal production resource status or overload are reordered according to the task priority to generate task sequence optimization data; According to the task sequence optimization data, the dependency of task units is verified and dynamically adjusted according to task priorities to generate optimized execution sequence data.

6. A production management system based on a low-code platform according to claim 5, characterized in that: According to the optimized execution sequence data, the idle time and real-time occupancy status of production resources are scheduled to generate a resource scheduling plan, including: According to the optimized execution sequence data, combined with the real-time occupancy information of production resources in the production resource status data, the resource allocation conflicts between task units are detected to generate resource conflict detection data; According to the resource conflict detection data, the production resource allocation time or production resource occupancy of the task unit is adjusted according to the task priority to generate optimized resource allocation data; According to the optimized resource allocation data, the idle time of production resources and the current load status of production resources in the production resource status data are extracted to calculate the load ratio of each production resource; Calculate the system load balancing target value based on the load ratio of each production resource; When the system balancing target value is greater than the preset balancing threshold, the production resources are adjusted according to the task priority, the allocation of high-load production resources is reduced, and the task units are allocated to low-load production resources according to the task priority to generate load balancing data; Based on the load balancing data, the remaining resources are redistributed according to the task priority to generate a resource scheduling plan.

7. A production management system based on a low-code platform according to claim 6, characterized in that: The calculation formula of the system load balancing target value is: , is the system load balancing target value, is the total number of types of production resources, is the total number of task units, Task Unit Is it assigned to The first production resource resource instance, the value is 1 or 0, Task Unit The task execution time, For the The first production resource The maximum available time of a resource instance, For the The first production resource The load value of a resource instance in the optimized resource allocation data, For the The first production resource The load value of a resource instance in the resource allocation scheme, The cost of conflicts between task priorities and resource allocation, = , and is the adjustment coefficient.

8. A production management system based on a low-code platform according to claim 7, characterized in that: The execution control module comprises: An instruction distribution unit is used to send task execution instructions to production resources according to the task scheduling plan to start the production task; The task monitoring unit is used to obtain production progress data, production resource status change data and resource consumption data in real time, and generate production feedback data; The anomaly detection unit is used to identify abnormal situations in the execution process according to the production feedback number and output abnormal status information.

9. A production management system based on a low-code platform according to claim 8, characterized in that: The dynamic update module includes: The status analysis unit is used to analyze the execution status of the current task and the use of production resources according to the production feedback number and abnormal status information, and generate task status analysis data; A task adjustment unit is used to update the initial task configuration data in real time according to the task status analysis data and the production task adjustment request, and generate optimized task configuration data; The configuration feedback unit is used to provide feedback to the task scheduling module based on the optimized task configuration data and regenerate the task scheduling plan.

10. A production management system based on a low-code platform according to claim 9, characterized in that: The system further comprises: A feedback data closed-loop unit, used to associate the initial task configuration data with the optimized task configuration data to generate closed-loop feedback data; The data analysis unit is used to analyze the execution efficiency, resource utilization and abnormality frequency of production tasks based on closed-loop feedback data, and generate a task execution optimization report; The template generation unit is used to extract the configuration data and scheduling scheme for efficient task execution according to the task execution optimization report, and generate a reusable task template.

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