A digital management platform and management method based on intelligent manufacturing

Abstract

The present invention discloses a digital management platform and management method based on intelligent manufacturing, relating to the technical field of intelligent manufacturing management. The platform includes: acquiring a target manufacturing task queue of a target manufacturing production line and obtaining a target manufacturing process information queue; obtaining a task preset start time queue; obtaining a resource reliability coefficient queue; performing equipment failure probability analysis to obtain an equipment reliability coefficient queue; performing maximum likelihood estimation of mapping task reliability to obtain a task reliability evaluation value queue; obtaining a task execution time tolerance threshold queue; obtaining a task adjustment start time queue; and performing production management on the target manufacturing production line based on the task adjustment start time queue. It solves the technical problems of inaccurate production task scheduling, unoptimized resource allocation, and insufficient equipment failure prediction existing in the prior art, and achieves the technical effects of improving task scheduling efficiency, optimizing resource allocation, and strengthening the ability of equipment reliability prediction and adjustment.

Description

Technical Field

[0001] This application relates to the technical field of intelligent manufacturing management, and specifically relates to a digital management platform and management method based on intelligent manufacturing. Background Art

[0002] With the rapid development of intelligent manufacturing and industry, the traditional production management mode can no longer fully meet the requirements of modern manufacturing for flexibility, efficiency, and precision. In modern manufacturing, the complexity and variability of production task arrangement, resource scheduling, equipment management, etc. have greatly increased the uncertainty in the production process. Traditional manual management or experience-based management models often cannot effectively address these challenges. How to achieve efficient production scheduling, resource management, equipment maintenance, and fault prediction in a digital and networked environment has become an urgent problem in the current manufacturing industry. Especially during the production task scheduling process, manufacturing enterprises need to face multiple issues such as reasonable scheduling of tasks, maintenance and fault management of equipment, and configuration and scheduling of resources. Currently, the scheduling methods cannot dynamically respond to the uncertainty in production tasks.

[0003] Therefore, in the related technologies at the present stage, there are technical problems such as inaccurate production task scheduling, unoptimized resource configuration, and insufficient equipment fault prediction. Summary of the Invention

[0004] This application provides a digital management platform and management method based on intelligent manufacturing, solves the technical problems of inaccurate production task scheduling, unoptimized resource configuration, and insufficient equipment fault prediction existing in the prior art, and achieves the technical effects of improving task scheduling efficiency, optimizing resource configuration, and strengthening the ability of equipment reliability prediction and adjustment.

[0005] The present application provides a digital management platform based on intelligent manufacturing. The platform includes: an information queue acquisition module, configured to acquire a target manufacturing task queue of a target manufacturing production line, and perform manufacturing processes for each target manufacturing task according to the target manufacturing task queue to obtain a target manufacturing process information queue; a start time queue acquisition module, configured to respectively extract the task preset start times of the target manufacturing task queue to obtain a task preset start time queue; a resource reliability analysis module, configured to traverse the target manufacturing task queue and the target manufacturing process information queue to perform resource reliability analysis to obtain a resource reliability coefficient queue; an equipment failure probability analysis module, configured to traverse the target manufacturing task queue and the target manufacturing process information queue to perform equipment failure probability analysis, and use the difference between the analysis result and 1 as the equipment reliability coefficient to obtain an equipment reliability coefficient queue; a reliability estimation module, configured to perform maximum likelihood estimation of task reliability mapping on the resource reliability coefficient queue and the equipment reliability coefficient queue to obtain a task reliability evaluation value queue; a task duration analysis module, configured to perform task duration analysis based on the target manufacturing process information queue, the task reliability evaluation value queue, and the task preset start time queue to obtain a task execution time tolerance threshold queue; a conflict identification module, configured to identify conflicts in the task execution time tolerance threshold queue, and perform associated adjustment of the task preset start time according to the conflict identification result to obtain a task adjusted start time queue; a production management module, configured to perform production management on the target manufacturing production line based on the task adjusted start time queue.

[0006] In a possible implementation manner, the digital management method based on intelligent manufacturing further performs the following processing: respectively construct a resource requirement matrix according to the target manufacturing task queue and the target manufacturing process information queue to obtain a resource requirement matrix queue; acquire a resource configuration list of the target manufacturing production line; perform resource reliability analysis on the resource requirement matrix queue based on the resource configuration list to obtain the resource reliability coefficient queue.

[0007] In a possible implementation manner, the digital management method based on intelligent manufacturing further performs the following processing: pre-construct a task reliability likelihood function; perform one-to-one mapping on the resource reliability coefficient queue and the equipment reliability coefficient queue to obtain a mapped task reliability analysis result queue; use the maximum likelihood method to perform likelihood estimation on the mapped task reliability analysis result queue respectively through the task reliability likelihood function to obtain a task reliability maximum likelihood estimator queue; use the task reliability maximum likelihood estimator queue as the task reliability evaluation value queue.

[0008] In a possible implementation, the digital management method based on intelligent manufacturing further performs the following processing: The task reliability likelihood function is:

[0009] ;

[0010] where, is the resource reliability coefficient, is the equipment reliability coefficient, is the estimate of the subsample , is the estimate of the subsample , is the sample value corresponding probability value, is the number of subsamples, .

[0011] In a possible implementation, the digital management method based on intelligent manufacturing further performs the following processing: According to the mapping task reliability analysis result queue, obtain the subsample sequence queue; respectively input the subsample sequence queue into the task reliability likelihood function for maximum likelihood estimation to obtain the task reliability maximum likelihood estimator queue.

[0012] In a possible implementation, the digital management method based on intelligent manufacturing further performs the following processing: Pre-build a task execution time analyzer; use the task execution time analyzer to analyze the target manufacturing process information queue and the task reliability evaluation value queue to obtain the task duration tolerance threshold queue; combine the task duration tolerance threshold queue and the task preset start time queue for time integration to obtain the task execution time tolerance threshold queue.

[0013] In a possible implementation, the digital management method based on intelligent manufacturing further performs the following processing: Obtain multiple sample target manufacturing process information, multiple sample task reliability evaluation values, and multiple sample task duration tolerance thresholds as training samples; use the training samples to perform supervised learning on the framework constructed based on the feedforward neural network until the training converges to obtain the trained task execution time analyzer.

[0014] The present application also provides a digital management method based on intelligent manufacturing. The method includes: obtaining a target manufacturing task queue of a target manufacturing production line, and performing manufacturing processes of each target manufacturing task according to the target manufacturing task queue to obtain a target manufacturing process information queue; respectively extracting the task preset start times of the target manufacturing task queue to obtain a task preset start time queue; traversing the target manufacturing task queue and the target manufacturing process information queue to perform resource reliability analysis to obtain a resource reliability coefficient queue; traversing the target manufacturing task queue and the target manufacturing process information queue to perform equipment failure probability analysis, and taking the difference between the analysis result and 1 as the equipment reliability coefficient to obtain an equipment reliability coefficient queue; performing maximum likelihood estimation of mapping task reliability on the resource reliability coefficient queue and the equipment reliability coefficient queue to obtain a task reliability evaluation value queue; performing task duration analysis based on the target manufacturing process information queue, the task reliability evaluation value queue, and the task preset start time queue to obtain a task execution time tolerance threshold queue; performing conflict identification on the task execution time tolerance threshold queue, and performing associated adjustment of the task preset start time according to the conflict identification result to obtain a task adjusted start time queue; and performing production management on the target manufacturing production line based on the task adjusted start time queue.

[0015] It is intended to obtain a target manufacturing task queue of a target manufacturing production line and obtain a target manufacturing process information queue through a digital management platform and management method based on intelligent manufacturing proposed in the present application; obtain a task preset start time queue; obtain a resource reliability coefficient queue; perform equipment failure probability analysis to obtain an equipment reliability coefficient queue; perform maximum likelihood estimation of mapping task reliability to obtain a task reliability evaluation value queue; obtain a task execution time tolerance threshold queue; obtain a task adjusted start time queue; and perform production management on the target manufacturing production line based on the task adjusted start time queue. The technical problems existing in the prior art, such as inaccurate production task scheduling, unoptimized resource allocation, and insufficient equipment failure prediction, are solved, and the technical effects of improving task scheduling efficiency, optimizing resource allocation, and strengthening the ability of equipment reliability prediction and adjustment are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in the present application to illustrate the operations performed by the platform according to the embodiments of the present application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1Schematic diagram of the structure of a digital management platform based on intelligent manufacturing provided by an embodiment of the present application;

[0018] Figure 2 Schematic flow chart of a digital management method based on intelligent manufacturing provided by an embodiment of the present application.

[0019] Explanation of reference numerals: Information queue acquisition module 10, start time queue acquisition module 20, resource reliability analysis module 30, equipment failure probability analysis module 40, reliability estimation module 50, task duration analysis module 60, conflict identification module 70, production management module 80. Detailed implementation manners

[0020] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented in accordance with the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0022] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first" and "second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, platform, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0023] An embodiment of the present application provides a digital management platform based on intelligent manufacturing, as Figure 1 shown, the platform includes:

[0024] An information queue acquisition module 10 is configured to acquire a target manufacturing task queue of a target manufacturing production line, and perform manufacturing processes for each target manufacturing task according to the target manufacturing task queue to obtain a target manufacturing process information queue.

[0025] Preferably, a set of manufacturing tasks to be executed is extracted from the target manufacturing production line as the target manufacturing task queue, which may include different production steps, processes or production instructions, representing the specific operations to be completed in the entire production process. Then, according to the target manufacturing task queue, the manufacturing processes of each target manufacturing task are executed to generate specific manufacturing process information, which may include the processing time of the process, the required equipment, resource consumption, process path, etc. When executing the manufacturing task, all the process information is recorded and stored in the order of the tasks to form a target manufacturing process information queue, which can reflect the progress of each task on the production line, the required resource allocation, and potential bottlenecks or problems, providing an accurate data basis for subsequent resource reliability analysis, equipment failure prediction and production optimization.

[0026] A start time queue acquisition module 20 is configured to respectively extract the task preset start times of the target manufacturing task queue to obtain a task preset start time queue.

[0027] Preferably, respectively extracting the task preset start times of the target manufacturing task queue means that for each manufacturing task, the preset start time of the task is extracted from the task queue, and then multiple task preset start time queues are obtained. The preset start time refers to the predetermined start time set for the production task according to the production scheduling plan, that is, the preset start time of the task, which is usually determined according to the urgency of the task, the availability of resources and the load of the production line during the production planning stage. The preset start times of all the manufacturing tasks extracted are organized in the order of the task queue to form a task preset start time queue. Each element of this queue corresponds to the start time of the corresponding task in the task queue, recording and displaying the planned start times of all tasks, and the start time of the task can be dynamically adjusted according to this queue to optimize the production scheduling.

[0028] A resource reliability analysis module 30 is configured to traverse the target manufacturing task queue and the target manufacturing process information queue to perform resource reliability analysis to obtain a resource reliability coefficient queue.

[0029] Preferably, traverse the target manufacturing task queue (production requirements of multiple production links, such as material preparation, processing, assembly, etc.) and the target manufacturing process information queue (detailed manufacturing process information for each task, including resources required for each task, equipment required, production sequence, etc.), obtain each task and the associated manufacturing process information, and perform resource reliability analysis, that is, evaluate the reliability of various resources (such as raw materials, tools, etc.) involved in the production manufacturing process. Specifically, the reliability evaluation refers to the material reliability evaluation (such as whether the raw materials arrive within the scheduled time, whether the supply chain is smooth, etc.). The core goal of the resource reliability analysis is to evaluate the possible problems of various resources in the actual production process, such as material shortages, etc., and predict the impact of these problems on the production tasks. By analyzing the resource reliability of each task, a plurality of resource reliability coefficients are obtained, indicating the reliability degree of the resources required for each task. The resource reliability coefficient is usually a numerical value, indicating the reliability of a certain resource (such as personnel, materials, etc.) in the production process, and the possible value range is from 0 (completely unavailable) to 1 (completely reliable). Finally, a resource reliability coefficient queue is formed, corresponding to the resource reliability scores of the corresponding tasks in the task queue, helping the platform understand the resource risks that each task may encounter during execution, and taking measures (such as adjusting the task order, preparing materials in advance, etc.) to ensure the smooth progress of the production tasks.

[0030] Further, the specific configuration of the resource reliability analysis module 30 further includes constructing a resource demand matrix according to the target manufacturing task queue and the target manufacturing process information queue respectively, to obtain a resource demand matrix queue; obtaining a resource configuration list of the target manufacturing production line; and performing resource reliability analysis on the resource demand matrix queue based on the resource configuration list to obtain the resource reliability coefficient queue.

[0031] Preferably, construct a resource requirement matrix according to the target manufacturing task queue and the target manufacturing process information queue respectively. That is, for each manufacturing task, extract all the resource requirements it depends on, including equipment resources (such as machines, production lines, etc.), personnel resources (such as operators, technicians, inspectors, etc.), and material resources (such as raw materials, components, etc.). Then, construct the resource requirements of each task in the task queue into a matrix. The rows of the matrix represent tasks, and the columns represent resource types (such as equipment, personnel, raw materials, etc.). Each element of the matrix represents the demand for a certain resource by a specific task, that is, obtain a resource requirement matrix queue, which intuitively represents the resource requirement relationship of all tasks. Then, obtain the resource configuration list of the target manufacturing production line, that is, obtain a list of all currently available resources on the production line, including information such as the type, quantity, status, and usage time of the resources. Then, conduct a resource reliability analysis on the resource requirement matrix queue based on the resource configuration list to evaluate the reliability of the production line resources in meeting the requirements of each task. Specifically, compare the demand of each task in the resource requirement matrix with the actual supply in the resource configuration list. If the resources in the configuration list can meet the demand in the matrix, the resource reliability is high. If the resource configuration is insufficient (such as insufficient equipment time, insufficient inventory materials), the resource reliability is low. Furthermore, calculate the resource reliability coefficient to quantify the ability of the resources to meet the requirements. For example, calculate the ratio of the actual available resources to the task demand resources as the resource reliability coefficient. The higher the resource reliability coefficient, the more reliable the support of the resources for the task; the lower the resource reliability coefficient, the more likely it is that the resources may not fully meet the task requirements. A resource reliability coefficient queue is composed of multiple resource reliability coefficients, where each coefficient corresponds to a manufacturing task, which helps to improve the resource utilization efficiency.

[0032] The equipment failure probability analysis module 40 is used to traverse the target manufacturing task queue and the target manufacturing process information queue to conduct equipment failure probability analysis, and use the difference between the analysis result and 1 as the equipment reliability coefficient to obtain an equipment reliability coefficient queue.

[0033] Preferably, traverse the target manufacturing task queue and the target manufacturing process information queue, and perform equipment failure probability analysis on each task and its corresponding manufacturing process information one by one to determine the failure risk of the production equipment included in each task. Specifically, the equipment failure probability analysis is a quantitative assessment of the risk of possible failures of the equipment required in the manufacturing process, usually including calculating the failure probability of the equipment based on the historical data of the equipment (such as failure records, maintenance frequencies, service life, etc.) and the real-time operating status (such as temperature, vibration, workload, etc.); using common failure prediction models (such as reliability mathematical models, machine learning prediction algorithms) to evaluate the possibility of each equipment failing in the current task; the analysis results include the equipment failure probability, which represents the probability of the equipment failing during the task execution process, and then taking the difference between the analysis result and 1 as the equipment reliability coefficient. Among them, the equipment reliability is used to reflect the reliability level of the equipment. The equipment reliability coefficient = 1 - the equipment failure probability. The higher the equipment reliability coefficient, the less likely the equipment is to fail and the more smoothly it can work during the task execution process. Thus, the failure risk of the equipment is quantified into a positive reliability index. Similarly, perform failure probability analysis on each task and the corresponding equipment in the target manufacturing task queue to obtain the equipment reliability coefficients of multiple equipment in the target manufacturing task, and organize these reliability coefficients in the task order to form an equipment reliability coefficient queue for risk warning, task optimization, and resource allocation, which can reduce the impact of equipment failures on the production plan during the dynamic task execution process, thereby improving the reliability and stability of the entire manufacturing process.

[0034] The reliability estimation module 50 is configured to perform maximum likelihood estimation of the mapping task reliability on the resource reliability coefficient queue and the equipment reliability coefficient queue to obtain a task reliability evaluation value queue.

[0035] Preferably, the resource reliability coefficient queue (the reliability coefficient of each resource associated with the production task, indicating the reliability of the resource when executing the task) and the equipment reliability coefficient queue (the reliability coefficient of each equipment related to the production task, indicating the probability of failure of the equipment during the task execution) are mapped to the maximum likelihood estimation of the task reliability, wherein the task reliability maximum likelihood estimation (MLE) is a statistical method that estimates the most likely task reliability by modeling given data (resource reliability coefficient and equipment reliability coefficient). Specifically, the mapping task reliability maximum likelihood estimation refers to inferring the overall reliability of the task based on resource reliability and equipment reliability. The resource reliability and equipment reliability are mapped to the task respectively, and the data of the two queues are matched according to the task order, and they are combined Together, the overall reliability of the task is formed. The overall reliability of each task is calculated by combining the reliability coefficients of all resources and equipment related to the task. The task reliability is jointly affected by the failure probability of all resources and equipment. The maximum likelihood estimation calculates the most likely task reliability through the known resource and equipment reliability, that is, the estimation error of the overall reliability of each task is minimized; the reliability coefficients of the resources and equipment of each task are comprehensively calculated through the maximum likelihood estimation method, and finally the reliability evaluation values ​​of multiple tasks are obtained, which indicates the probability of the task being successfully completed as planned. It is usually a value between 0 and 1. The larger the value, the higher the possibility of the task being completed on time. It also forms a task reliability evaluation value queue, which can improve production efficiency, reduce production risks caused by equipment failures, and optimize the overall performance of the production process.

[0036] Furthermore, the specific configuration of the reliability estimation module 50 also includes pre-building a task reliability likelihood function; performing a one-to-one mapping between the resource reliability coefficient queue and the equipment reliability coefficient queue to obtain a mapping task reliability analysis result queue; utilizing the maximum likelihood method to perform likelihood estimation on the mapping task reliability analysis result queue respectively through the task reliability likelihood function to obtain a task reliability maximum likelihood estimator queue; and using the task reliability maximum likelihood estimator queue as the task reliability evaluation value queue.

[0037] The specific configuration of the reliability estimation module 50 further includes that the task reliability likelihood function is:

[0038] ;

[0039] in, is the resource reliability coefficient, is the equipment reliability factor, It's a sample The estimated value of It's a sample The estimated value of For the sample value The corresponding probability value Is the number of subsamples .

[0040] Preferably, a task reliability likelihood function is constructed based on the resource reliability coefficient and the equipment reliability coefficient , used to quantify the combined impact of the reliability of resources and equipment on task reliability, where Is the resource reliability coefficient (such as the reliability of materials and manpower) Is the equipment reliability coefficient (such as the operating reliability of equipment) Is the subsample Estimated value of Is the subsample Estimated value of For the sample value The corresponding probability value Is the number of subsamples ; Perform a one-to-one mapping on the resource reliability coefficient queue and the equipment reliability coefficient queue, that is, combine the resource reliability coefficient and the equipment reliability coefficient in correspondence according to the task order to generate a mapped task reliability analysis result queue, indicating the combined status of the resources and equipment reliability of each task; then use the maximum likelihood method to perform likelihood estimation on the mapped task reliability analysis result queue respectively, that is, based on the task reliability likelihood function, by optimizing And The values of, make the samples And The joint probability value of Maximize, including calculating the likelihood value of each task According to the likelihood values of all tasks, optimize the parameters And Furthermore, output the maximum likelihood estimator, that is, the optimal value describing the task reliability, generate multiple task reliability maximum likelihood estimators (that is, the reliability indicators of each task) and form a task reliability maximum likelihood estimator queue, and finally use the maximum likelihood estimator queue as the task reliability evaluation value queue, directly reflecting the overall reliability of each task under the conditions of resource and equipment reliability, so as to ensure the smooth completion of the task under the conditions of resources and equipment.

[0041] Furthermore, the specific configuration of the reliability estimation module 50 further includes obtaining a subsample sequence queue according to the mapped task reliability analysis result queue; respectively inputting the subsample sequence queue into the task reliability likelihood function for maximum likelihood estimation to obtain a task reliability maximum likelihood estimator queue.

[0042] Preferably, decompose the reliable analysis result queue of mapping tasks into multiple sub-sample sequences, and extract the sub-sample sequence queue from the reliable analysis result queue of mapping tasks (including the resource and device reliability analysis results corresponding to each task) for further reliability analysis. Among them, the sub-sample sequence usually represents the refined result of reliability evaluation for a certain task under different resource and device conditions. Then, input the sub-sample sequence queue into the task reliable likelihood function for maximum likelihood estimation, that is, through the task reliable likelihood function, find the parameter value that makes the possibility of data observation the largest. Specifically, it includes taking the sub-sample sequence of each task (i.e., the resource and device reliability of the task) as input, inputting it into the task reliable likelihood function, and calculating the reliability of each task by maximizing the task reliable likelihood function. That is, calculate the probability of task execution for each sub-sample sequence. Through maximum likelihood estimation of each sub-sample sequence, obtain the reliability index of each task, and then generate a queue of maximum likelihood estimators of task reliability, reflecting the true reliability of the task under specific resource and device conditions, so as to provide strong support for production scheduling and task management.

[0043] The task duration analysis module 60 is used to perform task duration analysis based on the target manufacturing process information queue, the task reliability evaluation value queue, and the task preset start time queue, and obtain a queue of task execution time tolerance thresholds.

[0044] Preferably, the task duration analysis is performed using the target manufacturing process information queue, the task reliability evaluation value queue, and the task preset start time queue as key input data. That is, by comprehensively considering the planned duration of the task itself and possible delays or early completions, the possible time range of each task during actual execution is predicted. Specifically, it includes extracting the expected duration of the task from the target manufacturing process information (for example, a certain task is expected to take 4 hours); combining the task reliability evaluation value to analyze the possible impact of equipment or resource problems on the actual completion time of the task. For example, if the task reliability is low (such as 0.6), it indicates that the task may require additional time to complete; combining the task preset start time to analyze the possible start and end time ranges of the task, as well as the time relationship with other tasks; through the task duration analysis, predicting the time range of the task under theoretical conditions and various influences, and then obtaining multiple task execution time tolerance thresholds, indicating that each task corresponds to a time period. Among them, the task execution time tolerance threshold is the tolerable execution time range of each task, used to measure the time flexibility of task completion, that is, the maximum deviation range of the actual completion time relative to the planned time without affecting the overall production plan. Specifically, according to the target manufacturing process information, the theoretical execution time of the task is obtained, and based on the task reliability evaluation value, the possible delay time or early completion time required for the task is deduced. Combining the task preset start time and the scheduling of other tasks, the actual time limit of each task is analyzed, and finally the tolerance threshold of each task, that is, the range within which the actual execution time of the task can be adjusted, is obtained. The tolerance thresholds of each task are combined to form a task execution time tolerance threshold queue, thus realizing dynamic scheduling and risk control in the intelligent manufacturing process.

[0045] Furthermore, the specific configuration of the task duration analysis module 60 further includes pre-building a task execution time analyzer; using the task execution time analyzer to analyze the target manufacturing process information queue and the task reliability evaluation value queue to obtain a task duration tolerance threshold queue; combining the task duration tolerance threshold queue and the task preset start time queue for time integration to obtain the task execution time tolerance threshold queue.

[0046] Preferably, a task execution time analyzer is pre-constructed based on a neural network model to predict the actual execution time of each task and evaluate its time elasticity (i.e., tolerance threshold). Specifically, the neural network model is trained by combining historical task data, the status of resources and devices, and manufacturing process information. That is, historical production data is used as the training data set, and the model learns the execution time pattern of tasks and influencing factors (such as equipment failures, resource shortages, etc.) to obtain an analyzer capable of predicting task execution time. The input features include task type, equipment reliability, resource configuration, historical task execution time, process information, etc., and the output result is to predict the execution time and its time range (shortest time, longest time) of the task. Then, the task execution time analyzer is used to analyze the target manufacturing process information queue and the task reliability evaluation value queue, including inputting the target manufacturing process information (such as task type, operation steps, equipment status) and task reliability (such as reliability score) into the task execution time analyzer to predict the actual execution time of the task, considering equipment reliability, resource status, and manufacturing process complexity, generating a time range (shortest execution time, longest execution time), and calculating the time elasticity (tolerance threshold). Finally, time integration is performed by combining the task duration tolerance threshold queue and the task preset start time queue, that is, by combining the preset start time of each task, calculating the possible end time of the task (preset start time + tolerance threshold). If the end time of the task overlaps with the preset start time of the subsequent task, the start time or tolerance threshold of the task is adjusted, thereby generating a task execution time tolerance threshold queue, indicating the available time range of each task in the production plan, and avoiding time conflicts between tasks, thus improving the executability and efficiency of the production plan.

[0047] Furthermore, the specific configuration of the task duration analysis module 60 further includes obtaining a plurality of sample target manufacturing process information, a plurality of sample task reliability evaluation values, and a plurality of sample task duration tolerance thresholds as training samples; using the training samples to perform supervised learning on a framework constructed based on a feedforward neural network until the training converges to obtain the trained task execution time analyzer.

[0048] Preferably, obtain multiple sample target manufacturing process information (such as the process flow, resource allocation, equipment usage, etc. required for each task execution), multiple sample task reliability evaluation values (the task reliability calculated for each task during historical execution), and multiple sample task duration tolerance thresholds (statistical data of the actual execution time range of historical tasks, including the shortest duration, the longest duration, and the allowed time flexibility range) from historical production data records. Use these data as training samples, and then use the training samples to perform supervised learning on the framework constructed based on the feedforward neural network. Among them, the feedforward neural network (FNN) is a common artificial neural network model, including an input layer, a hidden layer, and an output layer. Specifically, use the target manufacturing process information (such as task steps, required resources, equipment load, etc.) and the task reliability evaluation value (such as the reliability of the task under resource and equipment conditions) as input features, and the task duration tolerance threshold as a label. The input data is calculated through the network framework to output the predicted task duration range. Compare the predicted task duration range of the model with the actual duration tolerance threshold, calculate the loss function value (such as mean squared error), and adjust the model parameters through methods such as gradient descent to minimize the loss function value; repeat the above process until the model training reaches a convergence state (the loss value no longer decreases significantly), and then obtain the trained task execution time analyzer, which can predict the task duration range and tolerance threshold based on the new target manufacturing process information and task reliability evaluation value to ensure that the task is completed on time.

[0049] The conflict identification module 70 is used to identify conflicts in the task execution time tolerance threshold queue and perform associated adjustment of the preset start time of the task according to the conflict identification result to obtain the task adjusted start time queue.

[0050] Preferably, conflict identification is performed on the task execution time tolerance threshold queue, that is, by analyzing the task execution time tolerance threshold, it is judged whether there are time conflicts or overlaps between tasks. Specifically, it includes that the task time ranges overlap. Check the tolerance thresholds of multiple tasks to judge whether there are conflicts in time. For example, if the tolerance threshold of task A is [08:00 - 10:00] and the tolerance threshold of task B is [09:30 - 11:30], then there is a conflict between the two because there is an overlap in the time period of [09:30 - 10:00]; resource dependence conflict. If multiple tasks need to share the same resource (such as equipment or manpower), check whether the task execution times overlap. For example, if a certain piece of equipment is reserved by two tasks at the same time and there is an overlap in the task times, it will lead to a resource conflict; equipment maintenance. The task execution time conflicts with the equipment maintenance plan or other unplanned unavailable times; according to the conflict identification result, the preset start time of the task is adjusted accordingly. It means that when a conflict is identified, according to the type and severity of the conflict, the preset start time of the task is adjusted to resolve the conflict. For example, tasks that can be flexibly changed are adjusted (the preset start time of high-priority tasks should not be moved as much as possible, while low-priority tasks can be adjusted), and when adjusting tasks, it is necessary to ensure that resources can be matched in time. For example, if adjusting the start time of a certain task may cause a certain piece of equipment to be unavailable during this time period, the adjustment plan needs to be re-evaluated. For each task, a task adjustment time will be obtained, and the adjusted start times of multiple tasks are formed into a task adjusted start time queue, recording the latest planned start time of each task, optimizing the time arrangement between tasks, ensuring that all tasks can be executed smoothly without conflicts, and ensuring the efficient operation and stable output of the production line.

[0051] The production management module 80 is used to perform production management on the target manufacturing production line based on the task adjusted start time queue.

[0052] Preferably, the production management of the target manufacturing production line is carried out according to the task adjustment time that adjusts the start time queue, that is, all resources, tasks, and equipment in the manufacturing production line are coordinated and scheduled to ensure the efficient and orderly progress of production activities. Specifically, according to the adjusted task start time, each task is allocated to a suitable position on the production line according to the latest time arrangement to ensure that the tasks are executed according to the priority and order, and to avoid interruptions in the production process caused by unreasonable scheduling; according to the adjusted task time, production resources (such as raw materials, equipment, manpower, etc.) are reasonably allocated to ensure that each task can obtain the required resources during execution, dynamically monitor the usage of resources, and reallocate according to actual needs; in combination with the task adjustment time, coordinate the use of production line equipment to ensure the efficient completion of equipment switching between tasks, arrange the maintenance or debugging time of the equipment in advance, and avoid delays caused by equipment failures during task execution; based on the adjusted schedule, track the task execution progress in real time to ensure that the tasks are completed as planned. If task delays or resource shortages are found during execution, the subsequent task times can be quickly adjusted, and the task adjustment time queue can be regenerated; according to the adjusted task time arrangement, analyze the critical path in the production line, identify possible bottleneck links (such as insufficient resource supply, excessive equipment load), and relieve the bottleneck by optimizing scheduling, dispatching standby resources, etc.; thereby ensuring the efficient utilization of resources, equipment, and time, enabling the production activities to proceed in an orderly manner, and improving production efficiency.

[0053] In the above text, reference is made to Figure 1 A digital management platform based on intelligent manufacturing according to an embodiment of the present invention is described in detail. Next, reference will be made to Figure 2 Describe a digital management method based on intelligent manufacturing according to an embodiment of the present invention.

[0054] A digital management method based on intelligent manufacturing, such as Figure 2As shown, the method includes: obtaining a target manufacturing task queue of a target manufacturing production line, and performing manufacturing processes of each target manufacturing task according to the target manufacturing task queue to obtain a target manufacturing process information queue; respectively extracting task preset start times of the target manufacturing task queue to obtain a task preset start time queue; traversing the target manufacturing task queue and the target manufacturing process information queue to perform resource reliability analysis to obtain a resource reliability coefficient queue; traversing the target manufacturing task queue and the target manufacturing process information queue to perform equipment failure probability analysis, and using the difference between the analysis result and 1 as an equipment reliability coefficient to obtain an equipment reliability coefficient queue; performing maximum likelihood estimation of mapping task reliability on the resource reliability coefficient queue and the equipment reliability coefficient queue to obtain a task reliability evaluation value queue; performing task duration analysis based on the target manufacturing process information queue, the task reliability evaluation value queue, and the task preset start time queue to obtain a task execution time tolerance threshold queue; performing conflict identification on the task execution time tolerance threshold queue, and performing associated adjustment of the task preset start time according to the conflict identification result to obtain a task adjusted start time queue; and performing production management on the target manufacturing production line based on the task adjusted start time queue.

[0055] In a possible implementation manner, the digital management method based on intelligent manufacturing further performs the following processing: respectively constructing a resource demand matrix according to the target manufacturing task queue and the target manufacturing process information queue to obtain a resource demand matrix queue; obtaining a resource configuration list of the target manufacturing production line; and performing resource reliability analysis on the resource demand matrix queue based on the resource configuration list to obtain the resource reliability coefficient queue.

[0056] In a possible implementation manner, the digital management method based on intelligent manufacturing further performs the following processing: pre-constructing a task reliability likelihood function; performing one-to-one mapping on the resource reliability coefficient queue and the equipment reliability coefficient queue to obtain a mapped task reliability analysis result queue; using the maximum likelihood method to perform likelihood estimation on the mapped task reliability analysis result queue respectively through the task reliability likelihood function to obtain a task reliability maximum likelihood estimator queue; and using the task reliability maximum likelihood estimator queue as the task reliability evaluation value queue.

[0057] In a possible implementation manner, the digital management method based on intelligent manufacturing further performs the following processing: the task reliability likelihood function is:

[0058] ;

[0059] where is the resource reliability coefficient, is the equipment reliability coefficient, is the estimate of the sub - sample, is the estimate of the sub - sample, is the probability value corresponding to the sample value, is the number of sub - samples, .

[0060] In a possible implementation, the above - mentioned digital management method based on intelligent manufacturing further performs the following processing: according to the result queue of the mapping task reliability analysis, obtain the sub - sample sequence queue; respectively input the sub - sample sequence queue into the task reliability likelihood function for maximum likelihood estimation to obtain the maximum likelihood estimator queue of the task reliability.

[0061] In a possible implementation, the above - mentioned digital management method based on intelligent manufacturing further performs the following processing: pre - construct a task execution time analyzer; use the task execution time analyzer to analyze the target manufacturing process information queue and the task reliability evaluation value queue to obtain the task duration tolerance threshold queue; combine the task duration tolerance threshold queue and the task preset start time queue for time integration to obtain the task execution time tolerance threshold queue.

[0062] In a possible implementation, the above - mentioned digital management method based on intelligent manufacturing further performs the following processing: obtain multiple sample target manufacturing process information, multiple sample task reliability evaluation values, and multiple sample task duration tolerance thresholds as training samples; use the training samples to perform supervised learning on a framework constructed based on a feed - forward neural network until the training converges to obtain the trained task execution time analyzer.

[0063] Although the present application makes various references to certain modules in the platform according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The included individual units and modules are only divided according to the functional logic, but are not limited to the above - mentioned division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0064] The above - mentioned specific implementation manners do not constitute a limitation to the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to the design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application should be included within the protection scope of the present application.

Claims

1. A digital management platform based on intelligent manufacturing, characterized in that: The platform includes: An information queue acquisition module is used to acquire a target manufacturing task queue of a target manufacturing production line, and to perform a manufacturing process of each target manufacturing task according to the target manufacturing task queue to acquire a target manufacturing process information queue; A start time queue acquisition module, used to extract the task preset start time of the target manufacturing task queue respectively, and obtain the task preset start time queue; A resource reliability analysis module, used for traversing the target manufacturing task queue and the target manufacturing process information queue to perform resource reliability analysis and obtain a resource reliability coefficient queue; An equipment failure probability analysis module, used to traverse the target manufacturing task queue and the target manufacturing process information queue to perform equipment failure probability analysis, and use the difference between the analysis result and 1 as the equipment reliability coefficient to obtain an equipment reliability coefficient queue; A reliability estimation module, used for performing a maximum likelihood estimation of task reliability on the resource reliability coefficient queue and the device reliability coefficient queue to obtain a task reliability evaluation value queue; A task duration analysis module, used to perform task duration analysis based on the target manufacturing process information queue, the task reliability evaluation value queue and the task preset start time queue, and obtain a task execution time tolerance threshold queue; A conflict identification module is used to identify conflicts in the task execution time tolerance threshold queue, and adjust the task preset start time according to the conflict identification result to obtain the task adjustment start time queue; A production management module, used for performing production management on the target manufacturing production line based on the task adjustment start time queue; Wherein, the resource reliability analysis module includes: A resource requirement matrix construction unit, used to construct a resource requirement matrix according to the target manufacturing task queue and the target manufacturing process information queue, respectively, to obtain a resource requirement matrix queue; A resource configuration list acquisition module is used to acquire a resource configuration list of a target manufacturing production line; A resource reliability analysis unit, configured to perform resource reliability analysis on the resource demand matrix queue based on the resource configuration list to obtain the resource reliability coefficient queue; Wherein, the reliability estimation module comprises: A task reliable likelihood function pre-construction unit, used to pre-construct a task reliable likelihood function; A coefficient queue mapping unit, used for performing one-to-one mapping between the resource reliability coefficient queue and the device reliability coefficient queue to obtain a mapping task reliability analysis result queue; A likelihood estimation unit, used to use the maximum likelihood method to perform likelihood estimation on the mapping task reliability analysis result queues respectively through the task reliability likelihood function to obtain a task reliability maximum likelihood estimation amount queue; The task reliability evaluation value queue obtaining unit is used to use the task reliability maximum likelihood estimation value queue as the task reliability evaluation value queue.

2. A digital management platform based on intelligent manufacturing as claimed in claim 1, characterized in that: The task reliability likelihood function is: ； in, is the resource reliability coefficient, is the equipment reliability factor, It's a sample The estimated value of It's a sample The estimated value of is the sample value The corresponding probability value is is the number of subsamples, .

3. A digital management platform based on intelligent manufacturing as claimed in claim 2, characterized in that: The reliability estimation module also includes: A sub-sample sequence queue acquisition unit, used to acquire a sub-sample sequence queue according to the mapping task reliable analysis result queue; The maximum likelihood estimation unit is used to input the sub-sample sequence queues into the task reliability likelihood function to perform maximum likelihood estimation and obtain a task reliability maximum likelihood estimation amount queue.

4. A digital management platform based on intelligent manufacturing as claimed in claim 3, characterized in that: The task duration analysis module includes: A task execution time analyzer pre-building unit, used to pre-build a task execution time analyzer; A task duration tolerance threshold queue obtaining unit is used to analyze the target manufacturing process information queue and the task reliability evaluation value queue using the task execution time analyzer to obtain a task duration tolerance threshold queue; The time integration unit is used to combine the task duration tolerance threshold queue and the task preset start time queue to perform time integration to obtain the task execution time tolerance threshold queue.

5. A digital management platform based on intelligent manufacturing as claimed in claim 4, characterized in that: The task duration analysis module also includes: A training sample acquisition unit, used to acquire a plurality of sample target manufacturing process information, a plurality of sample task reliability evaluation values ​​and a plurality of sample task duration tolerance thresholds as training samples; The task execution time analyzer obtaining unit is used to use the training samples to perform supervised learning on a framework constructed based on a feedforward neural network until the training reaches convergence, thereby obtaining the task execution time analyzer that has been trained.

6. A digital management method based on intelligent manufacturing, the method being applied to a digital management platform based on intelligent manufacturing as claimed in any one of claims 1 to 5, the method comprising: Acquire a target manufacturing task queue of a target manufacturing production line, and perform a manufacturing process of each target manufacturing task according to the target manufacturing task queue to obtain a target manufacturing process information queue; Extract the task preset start time of the target manufacturing task queue respectively to obtain the task preset start time queue; Traversing the target manufacturing task queue and the target manufacturing process information queue to perform resource reliability analysis and obtain a resource reliability coefficient queue; Traversing the target manufacturing task queue and the target manufacturing process information queue to perform equipment failure probability analysis, and taking the difference between the analysis result and 1 as the equipment reliability coefficient to obtain an equipment reliability coefficient queue; Performing a mapping task reliability maximum likelihood estimation on the resource reliability coefficient queue and the device reliability coefficient queue to obtain a task reliability evaluation value queue; Perform task duration analysis based on the target manufacturing process information queue, the task reliability evaluation value queue and the task preset start time queue to obtain a task execution time tolerance threshold queue; Conflict identification is performed on the task execution time tolerance threshold queue, and task preset start time association adjustment is performed according to the conflict identification result to obtain a task adjustment start time queue; Production management is performed on the target manufacturing production line based on the task adjustment start time queue.

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