Intelligent manufacturing-oriented digital workshop production process management platform

Abstract

The invention provides a digital workshop production process management platform for intelligent manufacturing, and relates to the technical field of data processing, and the platform comprises a production data collection module which is used for obtaining the real-time state data of production equipment in a workshop; the resource state evaluation module is used for analyzing the current state of the production resources; the task decomposition module is used for decomposing the production task into a plurality of mutually associated process tasks; the dynamic task allocation module is used for dynamically allocating equipment resources for each process task; the abnormal data processing module is used for detecting abnormal data in the process of executing the process distribution plan; the task optimization adjustment module is used for updating the resource state information in the resource state evaluation module; the execution instruction issuing module is used for converting the newest process allocation plan into a control instruction and sending the control instruction to corresponding equipment for execution; according to the invention, the autonomy and accuracy of resource allocation in the production process are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a digital workshop production process management platform oriented to intelligent manufacturing. Background Art

[0002] At present, the existing digital workshop production process management mainly relies on the combination of manufacturing execution system (MES) and Internet of Things (IoT) technology, and realizes digital management by collecting real-time data of production equipment, dynamically scheduling production processes, and monitoring product quality status. For example, the sensor network is used to monitor the operating status of equipment, and the data is centrally stored and analyzed through the cloud platform to optimize and manage the production process. However, in actual applications, some of these management platforms use preset rules or simple algorithm models for scheduling, which makes it difficult to quickly respond to emergencies or resource conflicts in complex and changeable production environments.

[0003] In actual application scenarios, such as flexible manufacturing production lines for certain high-precision mechanical parts, the production process management platform in the existing technology is prone to bottlenecks when handling task scheduling between multiple processes due to the inability to dynamically adjust resource allocation. For example, when a piece of production equipment fails, the system cannot quickly analyze the scope of impact and develop the best alternative plan, but needs to rely on manual intervention and experience judgment. This defect leads to interruptions in the production process and reduced resource utilization, and cannot meet the intelligent manufacturing's demand for efficient and intelligent production management. Summary of the invention

[0004] The purpose of the present invention is to provide a digital workshop production process management platform for intelligent manufacturing, 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 digital workshop production process management platform for intelligent manufacturing, the platform comprising:

[0007] The production data acquisition module is used to obtain the real-time status data of the production equipment in the workshop and pre-process the real-time status data to obtain a pre-processed data set. The real-time status data includes equipment operating parameters, workpiece processing status data and process execution progress data;

[0008] The resource status assessment module is used to analyze the current status of production resources based on the preprocessed data set and generate resource status information, including equipment availability, process dependency and resource conflict information;

[0009] The task decomposition module is used to decompose the production task into multiple interrelated process tasks according to process dependencies, and generate a process serialization plan, including process task numbers, the relationship between the preceding and following process tasks, and task identifiers;

[0010] Dynamic task allocation module, which is used to dynamically allocate equipment resources to each process task according to resource status information and process serialization plan, and generate process allocation plan, including the execution time of each process task, equipment allocation information, task priority and its execution order;

[0011] The abnormal data processing module is used to detect abnormal data during the execution of the process allocation plan and generate impact analysis results based on the abnormal data, including resource abnormality information and impact range information;

[0012] The task optimization and adjustment module is used to update the resource status information in the resource status evaluation module according to the impact analysis results, so as to optimize the process allocation plan;

[0013] The execution instruction sending module is used to convert the latest process allocation plan into control instructions that can be recognized by the equipment, and send the control instructions to the corresponding equipment for execution.

[0014] Preferably, the resource status assessment module includes:

[0015] The equipment status evaluation submodule is used to generate equipment health status indicators and equipment availability scores based on the equipment operating parameters in the preprocessed data set by comparing the dynamic difference between the real-time equipment operating parameters and the equipment historical operating parameters;

[0016] The process dependency analysis submodule is used to extract the logical dependency relationship between processes based on the process execution progress data in the preprocessing data set, and establish a process dependency matrix. The process dependency matrix is ​​used to describe the sequence constraints, parallel relationships, and resource sharing relationships between the processes, and mark the critical path processes in the matrix;

[0017] The resource conflict detection submodule is used to analyze the resource competition problems existing in the process tasks according to the equipment health status indicators, equipment availability scores and process dependency matrix, and generate resource conflict information, including the unique identifiers of the conflicting equipment and conflicting processes.

[0018] Preferably, the equipment status assessment submodule includes:

[0019] The equipment operation parameter monitoring unit is used to extract the real-time equipment operation parameters from the preprocessed data set, and generate operation status comparison data by comparing the real-time operation parameters with the equipment historical operation parameters;

[0020] The equipment health assessment unit is used to calculate the equipment health status index according to the operation status comparison data result; the calculation formula of the equipment health status index is: ,in, For equipment health status indicators, For equipment Real-time power consumption vs. standard power The difference, For equipment The standard power, For equipment Real-time temperature and standard temperature The difference, For equipment The standard temperature, For equipment The real-time vibration amplitude, For equipment The maximum vibration amplitude tolerance, For equipment The cumulative number of failures, For equipment The cumulative running time of , and is the weight coefficient and satisfies , and is the adjustment coefficient;

[0021] The device availability score calculation unit is used to calculate the device availability score according to the device health status indicator and the current task load status of the device; the calculation formula of the device availability score is: ,in, For equipment Availability rating, For equipment The current task load, For equipment Maximum load capacity, For equipment The remaining execution time in the current process task, For equipment The total execution time to complete the current process task, is the adjustment coefficient.

[0022] Preferably, the dynamic task allocation module includes:

[0023] The task priority calculation submodule is used to assign a priority and a task number to each process task and generate task priority data according to the critical path priority rule, the minimum resource occupancy rule and the task execution urgency rule;

[0024] The task-equipment matching submodule is used to allocate suitable equipment resources to each process task according to resource status information and task priority data, and generate task-equipment matching data, including equipment allocation information, task priority and its execution order;

[0025] The task execution time calculation submodule is used to calculate the expected execution time of each process task based on the task equipment matching data and the equipment operation parameters, and generate the execution time data of the process task, including the planned start time, planned end time and total duration of each process task;

[0026] The process allocation plan generation submodule is used to generate a process allocation plan based on task priority data, task equipment matching data and task execution time data.

[0027] Preferably, the task priority calculation submodule includes:

[0028] A critical path analysis unit is used to extract the critical path processes in the task according to the process dependency matrix in the resource status information, and generate the critical path process priority in combination with the sequence constraints between the processes in the critical path process;

[0029] The resource occupancy analysis unit is used to calculate the resource occupancy cost of the process task according to the occupancy of the equipment resources required by the process task; the calculation formula of the resource occupancy cost is: ,in, For process tasks The resource occupation cost, For equipment operating efficiency, For process tasks About equipment The resource usage ratio, For equipment In process tasks The resource conflict rate in To avoid constants with zero denominators, For process tasks The total number of devices required;

[0030] The execution urgency calculation unit is used to calculate the execution urgency score of the process task according to the deadline of the process task in the process allocation plan and the current process execution progress; the calculation formula of the execution urgency score is: ,in, For process tasks The execution urgency score of For process tasks The planned deadline, is the current time, For process tasks The estimated execution time, For process tasks The delay affects the critical path time. is the maximum allowed delay time, For process tasks The total resource usage in all devices, and is the adjustment coefficient;

[0031] The priority generation unit is used to calculate the comprehensive priority score of the process task according to the critical path process priority, resource occupation cost and execution urgency score, and generate task priority data; the calculation formula of the comprehensive priority score is: ,in, For process tasks The overall priority score of For process tasks Is it a critical path? Indicates belonging to, Indicates that it does not belong to, For process tasks The current amount of work completed, For process tasks The total workload, , and is the weight coefficient and satisfies .

[0032] Preferably, the task device matching submodule includes:

[0033] The equipment screening unit is used to screen out equipment that meets the process execution requirements from all candidate equipment according to the equipment availability score in the resource status information and the requirements of the current process task, and generate candidate equipment data;

[0034] The device allocation unit is used to allocate devices according to the candidate device data and the task priority data to generate device allocation data;

[0035] The task device matching generation unit is used to adjust the device allocation data according to the resource conflict information and generate the task device matching data, including the device allocation information, the task priority and the execution order thereof.

[0036] Preferably, the abnormal data processing module includes:

[0037] The abnormal data detection submodule is used to detect abnormal data from the real-time data during the execution of the process allocation plan according to the preset safety threshold, including abnormal equipment operation data and abnormal process execution data;

[0038] The abnormal data classification submodule is used to classify and associate abnormal data and generate resource abnormality information, including abnormal equipment and its fault category, abnormal process and related process task number and abnormal occurrence time;

[0039] The impact range analysis submodule is used to analyze the impact range of abnormal data on the process allocation plan based on resource abnormality information and generate process impact range information.

[0040] Preferably, the abnormal data classification submodule includes:

[0041] The device abnormality classification unit is used to classify abnormal devices according to the device fault type based on the device operation abnormality data, and generate device abnormality information, including abnormal device identification, fault type, abnormality occurrence time and expected recovery time;

[0042] The process abnormality analysis unit is used to analyze the impact of abnormal processes on subsequent processes and critical path processes based on process execution abnormality data and combined with the process dependency matrix, and generate process abnormality information, including abnormal process number, process task number, delay time and affected critical path processes;

[0043] The comprehensive abnormal information generation unit is used to generate resource abnormal information based on the equipment abnormal information and process abnormal information, including the abnormal equipment identification and its fault category, the abnormal process number and its related process task number, the abnormal occurrence time and the delay prediction for the entire task.

[0044] Preferably, the impact range analysis submodule includes:

[0045] The equipment abnormality impact analysis unit is used to analyze the impact range of equipment failure on the process allocation plan according to the equipment abnormality information in the resource abnormality information, and generate equipment impact range information, including the identification of the affected equipment, the relevant process task number and the recommendation of alternative equipment;

[0046] The process abnormality impact analysis unit is used to analyze the impact of the process abnormality on the process dependency and task priority according to the process abnormality information in the resource abnormality information, and generate process impact range information, including the affected process number, the related process task number and the process dependency update information;

[0047] The impact scope comprehensive generation unit is used to generate impact scope analysis results based on the equipment impact scope information and the process impact scope information, including updated equipment allocation information, adjusted process dependencies and task priorities, and delay impact assessments on processes and tasks affected by the anomalies.

[0048] Preferably, the task optimization and adjustment module includes:

[0049] The device availability adjustment submodule is used to update the device availability in the resource status information according to the impact analysis results, including:

[0050] For faulty equipment, mark it as unavailable;

[0051] For some devices with limited functions, adjust their health status indicators and maximum load capacity to generate updated device availability scores;

[0052] The process dependency adjustment submodule is used to update the process dependency in the resource status information according to the process impact range information in the impact analysis result, specifically including:

[0053] If the preceding process is delayed due to equipment failure, adjust the dependency of the subsequent processes and generate a new process dependency matrix;

[0054] Re-label critical path steps to reflect the impact of changes in dependencies on the overall execution of the task;

[0055] The resource conflict optimization submodule is used to analyze and resolve resource conflicts based on the updated equipment availability score and process dependency matrix, including:

[0056] For resource conflicts caused by equipment failure, we recommend alternative equipment based on the equipment availability score and reallocate the process equipment matching plan;

[0057] To solve the resource competition problem caused by process sequence adjustment, dynamically adjust the task priority and time arrangement of process tasks;

[0058] The optimization result generation submodule is used to generate an optimized process allocation plan based on the adjusted equipment availability score, process dependency and resource conflict information.

[0059] The above solution of the present invention includes at least the following beneficial effects:

[0060] The digital workshop production process management platform for intelligent manufacturing provided by the present invention effectively solves the bottleneck problem of the existing technology that is difficult to cope with complex production environment changes, resource conflicts and emergencies by introducing a multi-module collaborative design. Compared with traditional management platforms that rely on preset rules or simple algorithm models, the present invention has significant advantages in dynamic scheduling and intelligent optimization.

[0061] First, the present invention uses the production data acquisition module to achieve real-time acquisition and preprocessing of the operating parameters of the production equipment in the workshop, the workpiece processing status and the process execution progress data, and generates a high-quality preprocessed data set. This method not only improves the utilization efficiency of data, but also provides an accurate and timely decision-making basis for subsequent modules, overcoming the problems of low data acquisition accuracy and high latency in the prior art.

[0062] Secondly, the resource status assessment module of the present invention comprehensively grasps the real-time status of production resources through equipment status assessment, process dependency analysis and resource conflict detection submodules. Equipment health status indicators and equipment availability scores can dynamically evaluate the operating status of equipment, thereby discovering potential equipment failure problems in advance. The process dependency matrix and critical path information generated by the process dependency analysis make the task scheduling logic clearer and more scientific. At the same time, the resource conflict detection module can promptly discover conflict problems of equipment or task resources and provide support for dynamic task allocation. Through the above functions, the present invention effectively solves the problem of production bottlenecks caused by static resource allocation in the prior art.

[0063] Thirdly, the dynamic task allocation module of the present invention can dynamically allocate equipment and adjust priorities for process tasks according to resource status information and process serialization plan, and generate an optimized process allocation plan. Through task priority calculation, task equipment matching, and task execution time calculation, the platform realizes dynamic scheduling of tasks and efficient use of equipment resources, significantly improving the intelligent level of task scheduling. This dynamic optimization mechanism overcomes the defect of the prior art that the system is difficult to respond quickly to emergencies due to rigid scheduling rules. Especially in the case of production equipment failure or process delay, the present invention can quickly adjust the scheduling plan to avoid interruption of the production process.

[0064] In addition, the abnormal data processing module and task optimization and adjustment module of the present invention generate impact analysis results and dynamically optimize task allocation plans through real-time detection, classification and scope analysis of equipment operation anomalies and process execution anomalies. This function significantly improves the platform's ability to perceive and respond to abnormal events, avoiding response delays and resource waste caused by reliance on manual intervention in the prior art. Especially in complex production scenarios, such as flexible manufacturing production lines for high-precision mechanical parts, the present invention can automatically generate optimal alternatives when equipment fails or resources conflict, ensuring the continuity of production tasks and improving resource utilization.

[0065] Finally, the execution instruction sending module of the present invention converts the optimized process allocation plan into control instructions that can be recognized by the equipment and sends them to the equipment end in real time, realizing closed-loop management from plan adjustment to equipment execution. This design not only reduces the reliance on human intervention, but also ensures the accuracy and timeliness of task execution, further improving the automation and flexible production capabilities of intelligent manufacturing.

[0066] In summary, the present invention has significant technical advantages in dynamic task scheduling, resource status assessment, abnormal event processing and plan optimization, and can significantly improve the level of production management in intelligent manufacturing, especially in dealing with complex production environments and resource conflicts. It shows excellent adaptability and intelligent effects, and meets the needs of modern manufacturing industry for efficient and intelligent production management. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is an architecture diagram of a digital workshop production process management platform for intelligent manufacturing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] 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.

[0069] like Figure 1 As shown, an embodiment of the present invention proposes a digital workshop production process management platform for intelligent manufacturing, the platform comprising:

[0070] The production data acquisition module is used to obtain the real-time status data of the production equipment in the workshop and pre-process the real-time status data to obtain a pre-processed data set. The real-time status data includes equipment operating parameters, workpiece processing status data and process execution progress data;

[0071] The resource status assessment module is used to analyze the current status of production resources based on the preprocessed data set and generate resource status information, including equipment availability, process dependency and resource conflict information;

[0072] The task decomposition module is used to decompose the production task into multiple interrelated process tasks according to process dependencies, and generate a process serialization plan, including process task numbers, the relationship between the preceding and following process tasks, and task identifiers;

[0073] Dynamic task allocation module, which is used to dynamically allocate equipment resources to each process task according to resource status information and process serialization plan, and generate process allocation plan, including the execution time of each process task, equipment allocation information, task priority and its execution order;

[0074] The abnormal data processing module is used to detect abnormal data during the execution of the process allocation plan and generate impact analysis results based on the abnormal data, including resource abnormality information and impact range information;

[0075] The task optimization and adjustment module is used to update the resource status information in the resource status evaluation module according to the impact analysis results, so as to optimize the process allocation plan;

[0076] The execution instruction sending module is used to convert the latest process allocation plan into control instructions that can be recognized by the equipment, and send the control instructions to the corresponding equipment for execution.

[0077] In an embodiment of the present invention, the platform significantly improves the production process management level in intelligent manufacturing by including a production data acquisition module, a resource status assessment module, a task decomposition module, a dynamic task allocation module, an abnormal data processing module, a task optimization and adjustment module, and an execution instruction issuance module. The production data acquisition module is responsible for acquiring the operating parameters of the equipment in the workshop, the processing status data of the workpiece, and the execution progress data of the process in real time, and preprocessing the acquired data to form a preprocessing data set. This preprocessing method not only improves the utilization efficiency of data through data cleaning, format conversion and structured storage, but also provides a high-quality input data source for subsequent modules.

[0078] The resource status assessment module deeply analyzes the current status of production resources based on the preprocessed data set provided by the production data acquisition module, and generates comprehensive data including equipment availability, process dependency and resource conflict information. These data can not only accurately reflect the real-time health status of the equipment, but also analyze the logical relationship and resource usage conflict of each process in the production task through process dependency, providing comprehensive support for task scheduling. Based on the results of process dependency analysis, the task decomposition module decomposes the production task into multiple interrelated process tasks according to the logical relationship, and generates a process serialization plan, including process task number, predecessor and successor relationship and task identification. This logical optimization makes the management of complex production tasks more efficient.

[0079] The dynamic task allocation module dynamically allocates equipment resources for each process task based on resource status information and process serialization plan, thereby generating a process allocation plan. The plan includes the specific execution time of the process task, the required equipment, the task priority and the execution order, so as to maximize the equipment utilization and avoid resource idleness or conflict. During the process execution, the abnormal data processing module monitors the abnormal situation in real time, including equipment failure and process delay, and generates abnormal impact range and resource abnormality information through analysis. This capability significantly improves the ability to respond to sudden problems in the production process. The task optimization and adjustment module updates the resource status information and dynamically adjusts the process allocation plan by using the results of abnormal data analysis to ensure continuous optimization and stability of task execution even under abnormal conditions. Finally, the execution instruction issuance module converts the optimized process allocation plan into control instructions that can be recognized by the equipment and directly issues them to the production equipment, thus realizing closed-loop management.

[0080] Overall, the platform has performed excellently in dynamic task scheduling, resource allocation optimization, and exception response, significantly improving the production efficiency, resource utilization, and adaptability to complex production environments of smart manufacturing workshops.

[0081] In a preferred embodiment of the present invention, the resource status assessment module includes:

[0082] The equipment status evaluation submodule is used to generate equipment health status indicators and equipment availability scores based on the equipment operating parameters in the preprocessed data set by comparing the dynamic difference between the real-time equipment operating parameters and the equipment historical operating parameters;

[0083] The process dependency analysis submodule is used to extract the logical dependency relationship between processes based on the process execution progress data in the preprocessing data set, and establish a process dependency matrix. The process dependency matrix is ​​used to describe the sequence constraints, parallel relationships, and resource sharing relationships between the processes, and mark the critical path processes in the matrix;

[0084] The resource conflict detection submodule is used to analyze the resource competition problems existing in the process tasks according to the equipment health status indicators, equipment availability scores and process dependency matrix, and generate resource conflict information, including the unique identifiers of the conflicting equipment and conflicting processes.

[0085] In the embodiment of the present invention, the resource status assessment module realizes a comprehensive assessment and analysis of workshop resources through the coordinated operation of the equipment status assessment submodule, the process dependency analysis submodule and the resource conflict detection submodule. First, the equipment status assessment submodule monitors the equipment operating parameters such as power, temperature and vibration amplitude in real time, compares them with the historical operating parameters, and generates equipment health status indicators and equipment availability scores. These indicators not only reflect the current operating status of the equipment, but also predict future operating trends based on the historical data of the equipment, so as to perceive the potential risk of equipment failure in advance.

[0086] The process dependency analysis submodule extracts the logical dependencies between processes and generates a process dependency matrix by analyzing the process execution progress data in the preprocessing data set. This matrix comprehensively describes the sequence constraints, parallel relationships and resource sharing requirements between processes, and marks the critical path processes. This data result provides an important basis for task decomposition and scheduling. The resource conflict detection submodule combines equipment health status indicators, equipment availability scores and process dependency matrices to detect resource competition problems in the current tasks of the workshop and generate resource conflict information. Resource conflict information specifically includes the unique identifier of the conflicting equipment and the task number of the conflicting process. This detection process significantly reduces the risk of production stagnation caused by resource competition, and at the same time provides optimization input for dynamic task allocation.

[0087] Through the implementation of the resource status assessment module, it is possible to achieve all-round control of equipment and process resources, effectively solve problems such as production resource conflicts and unclear equipment status, and provide a solid foundation for subsequent task scheduling and allocation.

[0088] More specifically, the process dependency analysis submodule is used to extract the logical relationship between processes and generate a process dependency matrix to support the decomposition and scheduling of production tasks. This module determines the front-end relationship, parallel relationship and resource sharing relationship of process tasks by analyzing the process execution progress data in the preprocessing data set.

[0089] In actual implementation, the module first extracts the start time, expected end time, and current execution status of each process from the preprocessed data set by reading the process planning information of the production task. Then, based on the extracted information, the module divides the dependencies between the processes into three relationships:

[0090] Sequential dependency: When the completion of one process is a prerequisite for the start of another process, there is a sequential dependency between the two. The module will mark all pairs of processes with sequential dependencies by analyzing the planned time of the processes.

[0091] Parallel relationships: When multiple processes can be performed in the same time period without waiting for other processes to complete, these processes are marked as parallel relationships. The module automatically identifies parallel processes based on the overlap of time windows.

[0092] Resource sharing relationship: When multiple processes need to use the same equipment or resources, there is a resource sharing relationship between them. The module detects the allocation of resources required by the process and marks the process with possible resource conflicts.

[0093] Marking critical path processes: You can use existing task scheduling software, such as the critical path method in project management tools, to implement critical path analysis. For example, scheduling tools based on the critical path method (CPM) can automatically generate task network diagrams and critical paths.

[0094] Finally, the process dependency analysis submodule generates a process dependency matrix, which represents the relationship between processes in the form of a two-dimensional table, including sequential dependency, parallel relationship and resource sharing. At the same time, the matrix will mark the critical path processes, that is, those processes that have a decisive impact on the completion time of the entire production plan. Through the implementation of this submodule, comprehensive process logic relationship support can be provided for the task decomposition module and the dynamic task allocation module.

[0095] More specifically, the main function of the resource conflict detection submodule is to analyze the resource competition situation in the current production task, detect and mark possible resource conflicts, so as to avoid production plan delays or interruptions caused by resource competition.

[0096] In actual implementation, the module first obtains the equipment health status indicators and equipment availability scores provided by the equipment status assessment submodule, as well as the process dependency matrix generated by the process dependency analysis submodule. Based on these data, the module analyzes the equipment resources required for each process task and determines the following resource conflicts:

[0097] Equipment conflict: When multiple processes need to use the same equipment at the same time, resource conflicts may occur. The module compares the process planning time and the equipment availability time to mark the conflicting equipment and the corresponding process tasks.

[0098] Task load conflict: When the current load of the equipment exceeds its maximum load capacity, the module will mark the equipment with overload and the corresponding process tasks.

[0099] Priority conflict: When multiple high-priority tasks require the same device at the same time, the module compares the priority information of the tasks, determines the tasks that need to be processed first, and recommends new resource allocation plans for other tasks.

[0100] The resource conflict detection submodule eventually generates a resource conflict information, including the unique identifier of the conflicting equipment, the task number of the conflicting process, and the cause of the conflict. This function provides comprehensive resource status information support for the dynamic task allocation module, ensuring the rationality of task allocation and efficient use of equipment resources.

[0101] In a preferred embodiment of the present invention, the device status assessment submodule includes:

[0102] The equipment operation parameter monitoring unit is used to extract the real-time equipment operation parameters from the preprocessed data set, and generate operation status comparison data by comparing the real-time operation parameters with the equipment historical operation parameters;

[0103] The equipment health assessment unit is used to calculate the equipment health status index according to the operation status comparison data result; the calculation formula of the equipment health status index is: ,in, For equipment health status indicators, For equipment Real-time power consumption vs. standard power The difference, For equipment The standard power, For equipment Real-time temperature and standard temperature The difference, For equipment The standard temperature, For equipment The real-time vibration amplitude, For equipment The maximum vibration amplitude tolerance, For equipment The cumulative number of failures, For equipment The cumulative running time of , and is the weight coefficient and satisfies , and is the adjustment coefficient;

[0104] The device availability score calculation unit is used to calculate the device availability score according to the device health status indicator and the current task load status of the device; the calculation formula of the device availability score is: ,in, For equipment Availability rating, For equipment The current task load, For equipment Maximum load capacity, For equipment The remaining execution time in the current process task, For equipment The total execution time to complete the current process task, is the adjustment coefficient.

[0105] In an embodiment of the present invention, the equipment status assessment submodule realizes a comprehensive assessment of the status of the workshop equipment by including an equipment operation parameter monitoring unit, an equipment health assessment unit and an equipment availability score calculation unit. The equipment operation parameter monitoring unit collects the equipment's operating parameters in real time, including core indicators such as power consumption, temperature and vibration amplitude, and dynamically compares these parameters with the equipment's historical operation data to generate operation status comparison data. On this basis, the equipment health assessment unit further analyzes the equipment's operating trend, and calculates the health status index in combination with the equipment's cumulative operating time and fault history. This index can accurately reflect the equipment's operating stability and provide support for predicting potential risks of the equipment.

[0106] The equipment availability score calculation unit combines the health status indicators and the equipment's task load to calculate the equipment's availability score. This score takes into account the equipment's current health status, historical load, and operating efficiency, and can dynamically evaluate whether the equipment is suitable for being assigned to a specific task. Through this multi-dimensional analysis, the equipment status evaluation submodule provides scientific data support for the platform's task allocation, while also improving the efficiency and safety of equipment utilization.

[0107] In a preferred embodiment of the present invention, the dynamic task allocation module includes:

[0108] The task priority calculation submodule is used to assign a priority and a task number to each process task and generate task priority data according to the critical path priority rule, the minimum resource occupancy rule and the task execution urgency rule;

[0109] The task-equipment matching submodule is used to allocate suitable equipment resources to each process task according to resource status information and task priority data, and generate task-equipment matching data, including equipment allocation information, task priority and its execution order;

[0110] The task execution time calculation submodule is used to calculate the expected execution time of each process task based on the task equipment matching data and the equipment operation parameters, and generate the execution time data of the process task, including the planned start time, planned end time and total duration of each process task;

[0111] The process allocation plan generation submodule is used to generate a process allocation plan based on task priority data, task equipment matching data and task execution time data.

[0112] In the embodiment of the present invention, the dynamic task allocation module fully considers multiple key factors such as task priority, equipment resource status, and process task execution time during the specific implementation process, and constructs an efficient task allocation mechanism. The task priority calculation submodule calculates the priority for each process task according to the critical path priority rule, the minimum resource occupancy rule, and the task execution urgency rule to ensure that the key processes can be completed first. The task equipment matching submodule allocates the most suitable equipment for each process task based on resource status information and task priority data, and generates task equipment matching data. This data contains equipment allocation information, task priority, and execution order, thereby ensuring the execution stability of tasks while improving equipment utilization.

[0113] The task execution time calculation submodule accurately calculates the estimated execution time of each process task based on the equipment's operating parameters and task-equipment matching data, and generates the planned start time, end time, and total duration. This process ensures that the entire production scheduling has a clear time frame and reduces time conflicts in process execution. The process allocation plan generation submodule generates a process allocation plan by integrating task priority data, equipment matching data, and execution time data, and ultimately determines the specific execution arrangements for each process task.

[0114] Through the implementation of the dynamic task allocation module, the platform can dynamically adjust the task priority and equipment allocation strategy according to the real-time status of production resources and task requirements, significantly improving task scheduling efficiency and equipment utilization, while also enhancing the ability to respond to emergencies.

[0115] More specifically, the task execution time calculation submodule is used to calculate the estimated execution time of each process task based on the task equipment matching data and equipment operating parameters, and generate execution time data including the planned start time, planned end time and total duration.

[0116] In actual implementation, the module first obtains the equipment allocation information and task sequence information generated by the task equipment matching submodule, and then combines the equipment operation parameters provided by the equipment status evaluation submodule, including the current load of the equipment, health status indicators, and task execution efficiency data. Based on these data, the module analyzes the execution time of each process task, which mainly involves the following steps:

[0117] Planned time allocation: Based on the task sequence information and the current task completion time of the equipment, the module assigns a planned start time to each process task.

[0118] Execution time calculation: The module combines the equipment operating efficiency and task complexity to calculate the estimated execution time required for the task. For example, for a processing task, the module will analyze the processing speed of the equipment, the processing difficulty of the material, and other information to generate total time data.

[0119] Calculation of interval time between tasks: For tasks with sequential dependencies, the module will calculate the start time of the subsequent task based on the end time of the predecessor task and the idle time of the device to ensure that there is no time conflict between tasks.

[0120] Finally, the task execution time calculation submodule generates a complete set of execution time data, including the planned start time, planned end time and total execution time of each process task. This data provides accurate time control information for the dynamic task allocation module and the task optimization and adjustment module, significantly improving the efficiency and executability of task scheduling.

[0121] In a preferred embodiment of the present invention, the task priority calculation submodule includes:

[0122] A critical path analysis unit is used to extract the critical path processes in the task according to the process dependency matrix in the resource status information, and generate the critical path process priority in combination with the sequence constraints between the processes in the critical path process;

[0123] The resource occupancy analysis unit is used to calculate the resource occupancy cost of the process task according to the occupancy of the equipment resources required by the process task; the calculation formula of the resource occupancy cost is: ,in, For process tasks The resource occupation cost, For equipment operating efficiency, For process tasks About equipment The resource usage ratio, For equipment In process tasks The resource conflict rate in To avoid constants with zero denominators, For process tasks The total number of devices required;

[0124] The execution urgency calculation unit is used to calculate the execution urgency score of the process task according to the deadline of the process task in the process allocation plan and the current process execution progress; the calculation formula of the execution urgency score is: ,in, For process tasks The execution urgency score of For process tasks The planned deadline, is the current time, For process tasks The estimated execution time, For process tasks The delay affects the critical path time. is the maximum allowed delay time, For process tasks The total resource usage in all devices, and is the adjustment coefficient;

[0125] The priority generation unit is used to calculate the comprehensive priority score of the process task according to the critical path process priority, resource occupation cost and execution urgency score, and generate task priority data; the calculation formula of the comprehensive priority score is: ,in, For process tasks The overall priority score of For process tasks Is it a critical path? Indicates belonging to, Indicates that it does not belong to, For process tasks The current amount of work completed, For process tasks The total workload, , and is the weight coefficient and satisfies .

[0126] In the embodiment of the present invention, through the implementation of the task priority calculation submodule, the priority evaluation problem of multiple tasks in parallel in complex workshop production tasks can be effectively solved. Specifically, the task priority calculation submodule includes a critical path analysis unit, a resource occupancy analysis unit, an execution urgency calculation unit and a priority generation unit, which work together to realize the multi-dimensional calculation of task priority.

[0127] The critical path analysis unit extracts the critical path processes in the task by analyzing the process dependency matrix in the resource status information, and generates priority data for the critical path processes by combining the sequence constraints and parallel relationships of the processes. This analysis method can ensure that important processes in production are executed first, thereby minimizing the risk of delays in the critical path. The resource occupancy analysis unit calculates the resource occupancy cost by quantitatively analyzing the occupancy of the equipment resources required for each process task. By combining the equipment's operating efficiency, availability score, and resource conflict, the unit can provide accurate resource occupancy evaluation indicators for each process task.

[0128] The execution urgency calculation unit generates an execution urgency score based on the planned deadline of the process task, the current execution progress, and the impact of delays on the critical path. Specifically, this unit can dynamically calculate the urgency of tasks and prioritize urgent tasks, thereby reducing planning deviations caused by delays. The priority generation unit performs weighted calculations on the critical path priority, resource occupation cost, and execution urgency score to generate comprehensive priority data for process tasks. Through the implementation of this module, the platform can dynamically adjust the priorities of multiple tasks, thereby achieving scientific and efficient task scheduling.

[0129] In summary, the task priority calculation submodule not only solves the priority problem of multi-task parallelization in the workshop, but also provides a reliable basis for task scheduling through comprehensive consideration of multi-dimensional indicators, significantly improving the task execution efficiency and planning accuracy.

[0130] In a preferred embodiment of the present invention, the task device matching submodule includes:

[0131] The equipment screening unit is used to screen out equipment that meets the process execution requirements from all candidate equipment according to the equipment availability score in the resource status information and the requirements of the current process task, and generate candidate equipment data;

[0132] The device allocation unit is used to allocate devices according to the candidate device data and the task priority data to generate device allocation data;

[0133] The task device matching generation unit is used to adjust the device allocation data according to the resource conflict information and generate the task device matching data, including the device allocation information, the task priority and the execution order thereof.

[0134] In the embodiment of the present invention, the task equipment matching submodule plays a vital role in the dynamic task allocation module, mainly through the equipment screening unit, the equipment allocation unit and the task equipment matching generation unit to achieve accurate matching of tasks and equipment. The equipment screening unit first selects equipment that meets the process execution requirements from the candidate equipment according to the equipment availability score in the resource status information and the requirements of the task for equipment resources. During the screening process, the equipment health status indicators and the current load conditions are combined to ensure that the selected equipment can efficiently complete the task.

[0135] Based on the equipment screening unit, the equipment allocation unit generates the optimal equipment allocation data by further analyzing the compatibility of the candidate equipment with the process tasks. The optimization process fully considers the execution priority of the process tasks, the operating efficiency of the equipment, and possible resource conflicts, thereby ensuring the optimality and rationality of equipment resource allocation. The task equipment matching generation unit adjusts the equipment allocation plan based on the generated equipment allocation data and resource conflict information, and finally generates the task equipment matching data. The matching data includes equipment allocation information, task priority, and execution order, providing a complete equipment allocation basis for the subsequent process allocation plan.

[0136] Through the implementation of the task-equipment matching submodule, the platform can achieve efficient matching of equipment and tasks under limited resources, maximize the utilization of equipment resources, and ensure the smooth execution of process tasks. The introduction of this module has significantly improved the platform's capabilities in equipment scheduling and resource management.

[0137] More specifically, the equipment allocation unit is responsible for allocating appropriate equipment resources to process tasks based on resource status information to maximize equipment utilization and avoid resource conflicts.

[0138] In actual implementation, the unit obtains the task priority information from the task priority calculation submodule and the equipment health status indicator and availability score from the equipment status evaluation submodule. Based on these data, the unit allocates equipment through the following logic:

[0139] Equipment screening: Based on the equipment availability score and task requirements, select candidate equipment that meets the process task requirements. For example, when a task requires high-precision processing equipment, the module will select equipment from the candidate equipment that meets the accuracy requirements and is in good health.

[0140] Conflict avoidance: When multiple tasks require the same device at the same time, the module compares the task priorities and allocates the device to the high-priority task first.

[0141] Load balancing: When multiple candidate devices meet the requirements, the module gives priority to devices with lower loads for allocation to achieve load balancing.

[0142] Through the implementation of the equipment allocation unit, the platform can achieve efficient equipment allocation under limited resources and avoid resource waste or production bottlenecks caused by improper equipment allocation.

[0143] More specifically, the function of the task-device matching generation unit is to integrate the device allocation data generated by the device allocation unit with the task priority information to generate task-device matching data, thereby providing a complete device matching solution for dynamic task allocation.

[0144] In actual implementation, the unit generates task device matching data through the following steps:

[0145] Integrate equipment allocation data: Integrate the equipment allocation information provided by the equipment allocation unit with the task priority information to ensure that each task is assigned to the most appropriate equipment.

[0146] Task order optimization: Dynamically adjust the execution order of tasks based on their priorities and the current state of the device. For example, for tasks that share the same device, the unit will prioritize high-priority tasks while deferring the execution of low-priority tasks.

[0147] Generate matching data: Generate complete equipment matching data for each process task, including equipment allocation information, task priority, and execution order.

[0148] Through the implementation of the task equipment matching generation unit, the platform can generate an optimized equipment allocation plan, provide reliable equipment matching support for task execution, and significantly improve the utilization efficiency of equipment resources and the continuity of task execution.

[0149] In a preferred embodiment of the present invention, the abnormal data processing module includes:

[0150] The abnormal data detection submodule is used to detect abnormal data from the real-time data during the execution of the process allocation plan according to the preset safety threshold, including abnormal equipment operation data and abnormal process execution data;

[0151] The abnormal data classification submodule is used to classify and associate abnormal data and generate resource abnormality information, including abnormal equipment and its fault category, abnormal process and related process task number and abnormal occurrence time;

[0152] The impact range analysis submodule is used to analyze the impact range of abnormal data on the process allocation plan based on resource abnormality information and generate process impact range information.

[0153] In an embodiment of the present invention, the abnormal data processing module significantly improves the perception and response capabilities of abnormal situations in the workshop production process through the collaborative work of the abnormal data detection submodule, the abnormal data classification submodule and the impact range analysis submodule. The abnormal data detection submodule is responsible for detecting abnormal situations from real-time data during the execution of the process allocation plan, including equipment operation abnormality data and process execution abnormality data. Equipment operation abnormality detects whether it exceeds the preset safety threshold by real-time monitoring of the equipment's operating parameters, such as power consumption, temperature and vibration amplitude. Process execution abnormality identifies process delays or incomplete situations by comparing the real-time process progress with the task execution time data in the process allocation plan.

[0154] The abnormal data classification submodule performs further classification and correlation analysis based on the detected abnormal data. By refining the classification of equipment operation abnormal data and process execution abnormal data, this submodule can generate resource abnormality information, including abnormal equipment identification, fault category, abnormal process number, related task number and abnormal occurrence time. Through classification and correlation analysis, resource abnormality information can accurately reflect the impact of abnormalities on equipment and processes, providing detailed data support for subsequent analysis and adjustment.

[0155] The impact range analysis submodule further analyzes the impact range of abnormal data on the process allocation plan through resource abnormality information. For equipment operation abnormalities, this submodule evaluates the impact range of abnormal equipment on the current task and subsequent tasks, and generates equipment impact range information. For process execution abnormalities, this submodule re-evaluates the dependency of the abnormal process on the preceding and following processes, and generates process impact range information. This analysis not only helps the platform clarify the specific impact of abnormalities on resources and tasks, but also provides adjustment basis for the task optimization and adjustment module.

[0156] Through the implementation of the abnormal data processing module, the platform can monitor abnormal situations in the production process in real time, and quickly respond and adjust according to the scope of abnormal impact, which significantly improves the robustness and flexibility of the production plan and provides strong guarantees for intelligent production in the workshop.

[0157] More specifically, the abnormal data detection submodule is used to detect equipment operation abnormalities and process execution abnormalities by collecting production data in real time during the execution of the process allocation plan, ensuring that abnormalities can be quickly captured and responded to when they occur.

[0158] The abnormal data detection submodule mainly includes two detection functions:

[0159] Equipment operation anomaly detection: This module extracts key indicators (such as power consumption, temperature, vibration amplitude, etc.) from the real-time collected equipment operation parameters and compares them with the preset thresholds or standard parameters of the equipment. When it is detected that the equipment operation parameters exceed the safety threshold range, it will be identified as equipment operation anomaly and generate abnormal data records. For example, when the vibration amplitude of a CNC machine tool exceeds the safety threshold, the module can immediately capture this anomaly and mark the equipment identification, occurrence time and anomaly type.

[0160] Process execution anomaly detection: This module extracts the progress of process tasks from the process allocation plan and real-time execution data, and compares the real-time data with the planned data. It mainly detects abnormal situations such as delay, incompleteness or premature completion of process tasks. For example, when a process task fails to be completed within the planned deadline, the module will automatically record the anomaly and generate abnormal data, including the abnormal process number, task ID and delay time.

[0161] Detection of equipment operation anomalies can be achieved through existing sensor technologies, such as installing temperature sensors, vibration sensors and power monitoring devices to provide real-time operation data.

[0162] The detection of process execution anomalies can be achieved through the real-time status feedback module in the industrial control system (such as PLC), comparing the real-time progress of task execution with the planned progress to capture anomalies.

[0163] This module captures equipment and process anomalies that occur in production in real time, providing high-quality basic data for subsequent anomaly classification and impact range analysis.

[0164] In a preferred embodiment of the present invention, the abnormal data classification submodule includes:

[0165] The device abnormality classification unit is used to classify abnormal devices according to the device fault type based on the device operation abnormality data, and generate device abnormality information, including abnormal device identification, fault type, abnormality occurrence time and expected recovery time;

[0166] The process abnormality analysis unit is used to analyze the impact of abnormal processes on subsequent processes and critical path processes based on process execution abnormality data and combined with the process dependency matrix, and generate process abnormality information, including abnormal process number, process task number, delay time and affected critical path processes;

[0167] The comprehensive abnormal information generation unit is used to generate resource abnormal information based on the equipment abnormal information and process abnormal information, including the abnormal equipment identification and its fault category, the abnormal process number and its related process task number, the abnormal occurrence time and the delay prediction for the entire task.

[0168] In an embodiment of the present invention, the implementation of the impact range analysis submodule accurately evaluates the impact range of abnormal events on resource status and process allocation plans through the coordinated operation of the equipment abnormality impact analysis unit, the process abnormality impact analysis unit and the impact range comprehensive generation unit, providing a solid foundation for the optimization of subsequent tasks. The equipment abnormality impact analysis unit uses the equipment operation abnormality data generated by the abnormal data classification submodule to analyze the impact range of equipment failures on the process allocation plan. Specifically, the unit can identify equipment allocation failure tasks caused by equipment failures, and combine the equipment health status and historical operation data to evaluate the resource dependency impact of faulty equipment on current tasks and subsequent tasks. Through detailed calculations, equipment impact range information is generated, including the identification of affected equipment, related task numbers, and equipment allocation adjustment suggestions. This analysis provides an accurate basis for resource scheduling under equipment abnormalities.

[0169] The process abnormality impact analysis unit analyzes the impact of process delays or interruptions on process dependencies and task priorities through process execution abnormality data. Based on the process dependency matrix, the unit re-evaluates the impact of abnormal processes on the preceding and following processes, updates the priorities of critical path processes, and generates critical path adjustment suggestions. At the same time, the unit can identify the need to adjust the task sequence due to process delays, providing support for optimized scheduling. The generated process impact range information includes the affected process number, related task number, and dependency adjustment suggestions.

[0170] The impact scope comprehensive generation unit integrates the equipment impact scope information and process impact scope information to generate the impact scope analysis results. The analysis results not only include updated equipment allocation information and adjusted process dependencies, but also provide optimization suggestions for the priority and execution order of tasks affected by the anomaly. Through this multi-dimensional comprehensive analysis, the impact scope analysis submodule significantly improves the accuracy and comprehensiveness of the platform's response to abnormal events.

[0171] More specifically, the device anomaly classification unit is responsible for classifying and marking the detected device anomaly data according to the anomaly type, providing a basis for further anomaly analysis.

[0172] The main task of the device anomaly classification unit is to accurately classify device anomalies, including but not limited to the following types:

[0173] Operating parameter exceeded: When the operating parameters of a device (such as temperature, power, or vibration) exceed the safety threshold, the classification unit will classify it as an operating parameter exceeded anomaly. For example, if the power consumption of a device continuously exceeds the maximum load power, the anomaly will be classified as a power overload problem.

[0174] Health deterioration: When the health status indicator of a device drops to a certain critical value, the classification unit will classify it as abnormal health deterioration. For example, if the cumulative number of failures of a device reaches the set threshold or the health status score continues to decrease, the device will be marked as abnormal health status.

[0175] Fault status record: If the equipment has stopped operating or entered an unusable state, the classification unit will mark it as a fault state and record the time and type of the fault.

[0176] The unit receives the equipment operation abnormality data provided by the abnormal data detection submodule, combines it with the historical data of the equipment health status assessment submodule, and accurately classifies the abnormality.

[0177] During the classification process, reference can be made to existing equipment diagnostic technologies, such as using vibration signal analysis technology (Vibration Analysis) to determine whether the equipment is in an overloaded or unstable state.

[0178] More specifically, the process abnormality analysis unit is responsible for classifying and analyzing process execution abnormalities and evaluating the specific impact of the abnormalities on the production plan.

[0179] The process abnormality analysis unit mainly focuses on the following types of process abnormalities:

[0180] Process delay: When a process fails to complete within the planned time, the analysis unit will mark the process delay as abnormal and analyze the cause of the delay, such as equipment failure or resource allocation issues. For example, on a multi-process assembly line, if a process on the critical path is delayed, the unit can analyze the impact of the delay on subsequent processes.

[0181] Process interruption: When a process cannot continue due to equipment failure or insufficient resources, the unit will record the interruption information, including the process status when the exception occurred and the identification of related resources.

[0182] Execution deviation: If the actual execution order or status of the process task does not match the plan, such as the process is executed early or late, the analysis unit will record the execution deviation information.

[0183] By collecting real-time process execution data and comparing it with the process allocation plan, the analysis unit can capture anomalies and identify the impact range of critical path processes in combination with the process dependency matrix.

[0184] The specific implementation of process abnormality analysis can be achieved with the help of the process monitoring module in the manufacturing execution system (MES).

[0185] In a preferred embodiment of the present invention, the impact range analysis submodule includes:

[0186] The equipment abnormality impact analysis unit is used to analyze the impact range of equipment failure on the process allocation plan according to the equipment abnormality information in the resource abnormality information, and generate equipment impact range information, including the identification of the affected equipment, the relevant process task number and the recommendation of alternative equipment;

[0187] The process abnormality impact analysis unit is used to analyze the impact of the process abnormality on the process dependency and task priority according to the process abnormality information in the resource abnormality information, and generate process impact range information, including the affected process number, the related process task number and the process dependency update information;

[0188] The impact scope comprehensive generation unit is used to generate impact scope analysis results based on the equipment impact scope information and the process impact scope information, including updated equipment allocation information, adjusted process dependencies and task priorities, and delay impact assessments on processes and tasks affected by the anomalies.

[0189] In the embodiment of the present invention, the impact range analysis submodule can accurately evaluate the specific impact range of abnormal events on equipment resources and process tasks during the production process through the coordinated operation of the equipment abnormality impact analysis unit, the process abnormality impact analysis unit and the impact range comprehensive generation unit, and provide efficient decision support for the subsequent optimization module. Compared with the manual or experience-driven abnormality handling method in the prior art, the present invention has significant advantages in response speed and analysis depth.

[0190] The equipment abnormality impact analysis unit can quickly locate faulty equipment and its impact range through the equipment operation abnormality data generated by the abnormal data processing module. Specifically, the unit combines the equipment's operating parameters, health status indicators, and equipment resource allocation to evaluate the impact of faulty equipment on current production tasks. For example, when a device fails, the unit can analyze its assigned tasks, identify other affected equipment and related process tasks, and generate detailed equipment impact range information, including the identification of the affected equipment, related task numbers, and recommendations for alternative equipment. This automated analysis greatly improves the response speed to equipment abnormalities and effectively reduces the risk of task interruption due to equipment failure.

[0191] The process abnormality impact analysis unit can dynamically evaluate the impact of abnormal processes on the production plan by analyzing the abnormal data of process execution. Based on the process dependency matrix, the unit can quickly identify the dependency of abnormal processes on the preceding and following processes, and re-evaluate the critical path between processes to generate adjustment suggestions. For example, when a critical process affects subsequent tasks due to delays, the process abnormality impact analysis unit can automatically adjust the priority and execution order of subsequent tasks to generate an optimized process dependency matrix. At the same time, the unit can accurately calculate the delay impact of process abnormalities on the overall task, providing the platform with a clear impact range assessment.

[0192] The impact scope comprehensive generation unit generates comprehensive impact scope analysis results by integrating the results of equipment abnormality impact analysis and process abnormality impact analysis. The analysis results include updated equipment allocation information, adjusted process dependencies, and dynamic adjustment suggestions for task priorities. This integration method not only ensures the integrity of the analysis results, but also enables the platform to handle multiple abnormal situations at the same time and provide accurate input for subsequent optimization modules.

[0193] In summary, the impact range analysis submodule provides the platform with powerful exception response capabilities and intelligent optimization support through efficient equipment and process exception analysis capabilities, which can significantly improve resource utilization efficiency and production continuity in the intelligent manufacturing environment, and meet the needs of modern manufacturing for flexible and intelligent production.

[0194] More specifically, the function of the equipment abnormality impact analysis unit is to analyze the impact range of the equipment abnormality on the current production task and subsequent process tasks.

[0195] The equipment abnormality impact analysis unit mainly completes the following tasks:

[0196] Task impact range analysis: When an abnormality occurs in a piece of equipment, the analysis unit can identify the affected current tasks and related subsequent tasks. For example, when a machine tool stops running due to overload, the unit can mark all process tasks that depend on the machine tool as affected tasks.

[0197] Alternative solution suggestion: For the affected tasks, the unit can recommend alternative equipment or resources. For example, when a piece of equipment fails, the unit will find a device with a higher health score from the resource status information and recommend it as a replacement.

[0198] Through the data provided by the equipment health status assessment submodule and the resource conflict detection submodule, the unit can analyze the specific impact of equipment failure on task allocation.

[0199] The recommendation of alternative solutions can be combined with existing resource scheduling algorithms, such as resource allocation optimization based on heuristic methods.

[0200] More specifically, the process abnormality impact analysis unit is responsible for evaluating the impact of process abnormalities on other processes and the overall task plan, and providing adjustment suggestions.

[0201] The functions of the process abnormality impact analysis unit include:

[0202] Process dependency adjustment: This unit identifies changes in the dependencies of abnormal processes on the preceding and following processes by re-evaluating the process dependency matrix. For example, when a critical process is delayed due to equipment failure, the unit will readjust the execution order of subsequent processes and update the critical path process.

[0203] Task priority adjustment: Based on the delay impact of abnormal processes on the overall task plan, the unit can dynamically adjust the priority of the affected processes to ensure that the overall task plan is completed in the shortest time.

[0204] The unit uses the dependency matrix information provided by the process dependency analysis submodule and the data of abnormal processes to reschedule the affected processes.

[0205] The implementation of priority adjustment can be combined with existing scheduling algorithms, such as a dynamic priority adjustment method based on the critical path method (CPM).

[0206] In a preferred embodiment of the present invention, the task optimization and adjustment module includes:

[0207] The device availability adjustment submodule is used to update the device availability in the resource status information according to the impact analysis results, including:

[0208] For faulty equipment, mark it as unavailable;

[0209] For some devices with limited functions, adjust their health status indicators and maximum load capacity to generate updated device availability scores;

[0210] The process dependency adjustment submodule is used to update the process dependency in the resource status information according to the process impact range information in the impact analysis result, specifically including:

[0211] If the preceding process is delayed due to equipment failure, adjust the dependency of the subsequent processes and generate a new process dependency matrix;

[0212] Re-label critical path steps to reflect the impact of changes in dependencies on the overall execution of the task;

[0213] The resource conflict optimization submodule is used to analyze and resolve resource conflicts based on the updated equipment availability score and process dependency matrix, including:

[0214] For resource conflicts caused by equipment failure, we recommend alternative equipment based on the equipment availability score and reallocate the process equipment matching plan;

[0215] To solve the resource competition problem caused by process sequence adjustment, dynamically adjust the task priority and time arrangement of process tasks;

[0216] The optimization result generation submodule is used to generate an optimized process allocation plan based on the adjusted equipment availability score, process dependency and resource conflict information.

[0217] In the embodiment of the present invention, the task optimization and adjustment module realizes dynamic optimization and adjustment of the process allocation plan by comprehensively considering the equipment status, process dependency and resource conflict. The equipment availability adjustment submodule incorporates the equipment's fault status and some functional limitations into the optimization plan by recalculating the equipment's health status index and availability score based on the equipment's abnormal operation data. This process ensures the scientific nature of resource allocation and avoids scheduling failures or production interruptions caused by unclear equipment status.

[0218] The process dependency adjustment submodule combines process exception data and process impact range information to recalculate the dependencies between processes and update the process dependency matrix. When a process task is delayed due to an exception, this submodule can dynamically adjust the execution order of subsequent processes and re-mark the critical path processes to ensure the sequential execution and logical optimization of tasks. By dynamically adjusting the critical path, the platform can minimize the impact of plan delays on the overall task execution under abnormal circumstances.

[0219] The resource conflict optimization submodule proposes an optimization adjustment plan for resource conflict problems in production based on the updated equipment availability score and process dependency matrix. Specifically, this submodule can reallocate resources according to the optimization formula, eliminate resource competition problems caused by equipment conflicts and task sequence adjustments, and dynamically adjust the priority and schedule of process tasks. The optimization result generation unit integrates the optimized data into a new process allocation plan, and generates a complete task plan containing task priority, equipment allocation information, and adjusted execution order. By converting the optimization plan into control instructions that can be executed by the equipment, closed-loop control of the production process is finally achieved.

[0220] The implementation of the task optimization and adjustment module has demonstrated significant technical effects in complex production scenarios. Especially in abnormal situations, by dynamically adjusting equipment status and process logic, the platform can maximize resource utilization efficiency and task execution stability.

[0221] 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 digital workshop production process management platform for intelligent manufacturing, characterized in that: The platform includes: The production data acquisition module is used to obtain the real-time status data of the production equipment in the workshop and pre-process the real-time status data to obtain a pre-processed data set. The real-time status data includes equipment operating parameters, workpiece processing status data and process execution progress data; The resource status assessment module is used to analyze the current status of production resources based on the preprocessed data set and generate resource status information, including equipment availability, process dependency and resource conflict information; The task decomposition module is used to decompose the production task into multiple interrelated process tasks according to process dependencies, and generate a process serialization plan, including process task numbers, the relationship between the preceding and following process tasks, and task identifiers; Dynamic task allocation module, which is used to dynamically allocate equipment resources to each process task according to resource status information and process serialization plan, and generate process allocation plan, including the execution time of each process task, equipment allocation information, task priority and its execution order; The abnormal data processing module is used to detect abnormal data during the execution of the process allocation plan and generate impact analysis results based on the abnormal data, including resource abnormality information and impact range information; The task optimization and adjustment module is used to update the resource status information in the resource status evaluation module according to the impact analysis results, so as to optimize the process allocation plan; The execution instruction sending module is used to convert the latest process allocation plan into control instructions that can be recognized by the equipment, and send the control instructions to the corresponding equipment for execution.

2. According to the digital workshop production process management platform for intelligent manufacturing according to claim 1, it is characterized in that: The resource status assessment module comprises: The equipment status evaluation submodule is used to generate equipment health status indicators and equipment availability scores based on the equipment operating parameters in the preprocessed data set by comparing the dynamic difference between the real-time equipment operating parameters and the equipment historical operating parameters; The process dependency analysis submodule is used to extract the logical dependency relationship between processes based on the process execution progress data in the preprocessing data set, and establish a process dependency matrix. The process dependency matrix is ​​used to describe the sequence constraints, parallel relationships, and resource sharing relationships between the processes, and mark the critical path processes in the matrix; The resource conflict detection submodule is used to analyze the resource competition problems existing in the process tasks according to the equipment health status indicators, equipment availability scores and process dependency matrix, and generate resource conflict information, including the unique identifiers of the conflicting equipment and conflicting processes.

3. According to the digital workshop production process management platform for intelligent manufacturing according to claim 2, it is characterized in that: The equipment status assessment submodule includes: The equipment operation parameter monitoring unit is used to extract the real-time equipment operation parameters from the preprocessed data set, and generate operation status comparison data by comparing the real-time operation parameters with the equipment historical operation parameters; The equipment health assessment unit is used to calculate the equipment health status index according to the operation status comparison data result; the calculation formula of the equipment health status index is: ,in, For equipment health status indicators, For equipment Real-time power consumption vs. standard power The difference, For equipment The standard power, For equipment Real-time temperature and standard temperature The difference, For equipment The standard temperature, For equipment The real-time vibration amplitude, For equipment The maximum vibration amplitude tolerance, For equipment The cumulative number of failures, For equipment The cumulative running time of , and is the weight coefficient and satisfies , and is the adjustment coefficient; The device availability score calculation unit is used to calculate the device availability score according to the device health status indicator and the current task load status of the device; the calculation formula of the device availability score is: ,in, For equipment Availability rating, For equipment The current task load, For equipment Maximum load capacity, For equipment The remaining execution time in the current process task, For equipment The total execution time to complete the current process task, is the adjustment coefficient.

4. A digital workshop production process management platform for intelligent manufacturing according to claim 3, characterized in that: The dynamic task allocation module includes: The task priority calculation submodule is used to assign a priority and a task number to each process task and generate task priority data according to the critical path priority rule, the minimum resource occupancy rule and the task execution urgency rule; The task-equipment matching submodule is used to allocate suitable equipment resources to each process task according to resource status information and task priority data, and generate task-equipment matching data, including equipment allocation information, task priority and its execution order; The task execution time calculation submodule is used to calculate the expected execution time of each process task based on the task equipment matching data and the equipment operation parameters, and generate the execution time data of the process task, including the planned start time, planned end time and total duration of each process task; The process allocation plan generation submodule is used to generate a process allocation plan based on task priority data, task equipment matching data and task execution time data.

5. A digital workshop production process management platform for intelligent manufacturing according to claim 4, characterized in that: The task priority calculation submodule includes: A critical path analysis unit is used to extract the critical path processes in the task according to the process dependency matrix in the resource status information, and generate the critical path process priority in combination with the sequence constraints between the processes in the critical path process; The resource occupancy analysis unit is used to calculate the resource occupancy cost of the process task according to the occupancy of the equipment resources required by the process task; the calculation formula of the resource occupancy cost is: ,in, For process tasks The resource occupation cost, For equipment operating efficiency, For process tasks About equipment The resource usage ratio, For equipment In process tasks The resource conflict rate in To avoid constants with zero denominators, For process tasks The total number of devices required; The execution urgency calculation unit is used to calculate the execution urgency score of the process task according to the deadline of the process task in the process allocation plan and the current process execution progress; the calculation formula of the execution urgency score is: ,in, For process tasks The execution urgency score of For process tasks The planned deadline, is the current time, For process tasks The estimated execution time, For process tasks The delay affects the critical path time. is the maximum allowed delay time, For process tasks The total resource usage in all devices, and is the adjustment coefficient; The priority generation unit is used to calculate the comprehensive priority score of the process task according to the critical path process priority, resource occupation cost and execution urgency score, and generate task priority data; the calculation formula of the comprehensive priority score is: ,in, For process tasks The overall priority score of For process tasks Is it a critical path? Indicates belonging to, Indicates that it does not belong to, For process tasks The current amount of work completed, For process tasks The total workload, , and is the weight coefficient and satisfies .

6. A digital workshop production process management platform for intelligent manufacturing according to claim 5, characterized in that: The task device matching submodule includes: The equipment screening unit is used to screen out equipment that meets the process execution requirements from all candidate equipment according to the equipment availability score in the resource status information and the requirements of the current process task, and generate candidate equipment data; The device allocation unit is used to allocate devices according to the candidate device data and the task priority data to generate device allocation data; The task device matching generation unit is used to adjust the device allocation data according to the resource conflict information and generate the task device matching data, including the device allocation information, the task priority and the execution order thereof.

7. A digital workshop production process management platform for intelligent manufacturing according to claim 6, characterized in that: The abnormal data processing module comprises: The abnormal data detection submodule is used to detect abnormal data from the real-time data during the execution of the process allocation plan according to the preset safety threshold, including abnormal equipment operation data and abnormal process execution data; The abnormal data classification submodule is used to classify and associate abnormal data and generate resource abnormality information, including abnormal equipment and its fault category, abnormal process and related process task number and abnormal occurrence time; The impact range analysis submodule is used to analyze the impact range of abnormal data on the process allocation plan based on resource abnormality information and generate process impact range information.

8. The digital workshop production process management platform for intelligent manufacturing according to claim 7 is characterized in that: The abnormal data classification submodule includes: The device abnormality classification unit is used to classify abnormal devices according to the device fault type based on the device operation abnormality data, and generate device abnormality information, including abnormal device identification, fault type, abnormality occurrence time and expected recovery time; The process abnormality analysis unit is used to analyze the impact of abnormal processes on subsequent processes and critical path processes based on process execution abnormality data and combined with the process dependency matrix, and generate process abnormality information, including abnormal process number, process task number, delay time and affected critical path processes; The comprehensive abnormal information generation unit is used to generate resource abnormal information based on the equipment abnormal information and process abnormal information, including the abnormal equipment identification and its fault category, the abnormal process number and its related process task number, the abnormal occurrence time and the delay prediction for the entire task.

9. A digital workshop production process management platform for intelligent manufacturing according to claim 8, characterized in that: The impact range analysis submodule includes: The equipment abnormality impact analysis unit is used to analyze the impact range of equipment failure on the process allocation plan according to the equipment abnormality information in the resource abnormality information, and generate equipment impact range information, including the identification of the affected equipment, the relevant process task number and the recommendation of alternative equipment; The process abnormality impact analysis unit is used to analyze the impact of the process abnormality on the process dependency and task priority according to the process abnormality information in the resource abnormality information, and generate process impact range information, including the affected process number, the related process task number and the process dependency update information; The impact scope comprehensive generation unit is used to generate impact scope analysis results based on the equipment impact scope information and the process impact scope information, including updated equipment allocation information, adjusted process dependencies and task priorities, and delay impact assessments on processes and tasks affected by the anomalies.

10. A digital workshop production process management platform for intelligent manufacturing according to claim 9, characterized in that: The task optimization and adjustment module includes: The device availability adjustment submodule is used to update the device availability in the resource status information according to the impact analysis results, including: For faulty equipment, mark it as unavailable; For some devices with limited functions, adjust their health status indicators and maximum load capacity to generate updated device availability scores; The process dependency adjustment submodule is used to update the process dependency in the resource status information according to the process impact range information in the impact analysis result, specifically including: If the preceding process is delayed due to equipment failure, adjust the dependency of the subsequent processes and generate a new process dependency matrix; Re-label critical path steps to reflect the impact of changes in dependencies on the overall execution of the task; The resource conflict optimization submodule is used to analyze and resolve resource conflicts based on the updated equipment availability score and process dependency matrix, including: For resource conflicts caused by equipment failure, we recommend alternative equipment based on the equipment availability score and reallocate the process equipment matching plan; To solve the resource competition problem caused by process sequence adjustment, dynamically adjust the task priority and time arrangement of process tasks; The optimization result generation submodule is used to generate an optimized process allocation plan based on the adjusted equipment availability score, process dependency and resource conflict information.

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