Factory task management method and system based on Internet of Things

Abstract

The invention relates to the technical field of industrial automation, in particular to a factory task management method and system based on the Internet of Things, and the method comprises the following steps: analyzing the operation data of a plurality of devices in a production line based on a device operation log, and recognizing a plurality of key performance indexes; and predicting the task processing capability of the equipment under the differentiated working conditions, and generating an equipment capability prediction result. According to the invention, the operation data of the equipment in the production line is monitored and analyzed, key performance indexes are identified, and the equipment performance is predicted, so that the use flexibility of the equipment is improved, resource allocation is optimized, production interruption caused by equipment faults is reduced, and real-time evaluation and authorization updating of task processing requests are realized; the operation safety is ensured, the response speed is improved, the task scheduling is optimized in combination with analysis of equipment requirements and task dependency, the task execution sequence is automatically adjusted according to real-time equipment loads, the processing efficiency of a production line is optimized, and the delivery speed and quality of products are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and in particular to a factory task management method and system based on the Internet of Things. Background Art

[0002] The field of industrial automation technology focuses on minimizing manual operations and improving industrial production efficiency and reliability by utilizing a variety of control systems, information technologies, and machinery and equipment. It is applied to automated control systems, robotics, production process automation, and enterprise resource planning to achieve real-time monitoring and control of the production process, optimize production processes, reduce resource waste, and improve product quality and manufacturing flexibility. It helps with factory safety management, reduces the direct involvement of personnel in high-risk environments, and reduces the risk of workplace accidents.

[0003] Among them, the factory task management method aims to optimize the factory's operating processes and resource allocation, improve production efficiency, optimize task allocation efficiency, reduce production delays, ensure the optimal use of production resources, and enhance the timeliness and quality of product delivery by utilizing a variety of strategies and technologies. It includes task planning, scheduling, execution and monitoring, and combines information technology to enhance the accuracy and response speed of decision-making, ensure that production activities can be carried out efficiently and orderly, reduce costs and waste, improve the factory's operational efficiency, meet market demand, and enhance its ability to survive in a highly competitive market environment.

[0004] Traditional factory task management methods are insufficient in adapting to rapidly changing production needs and complex task scheduling. In terms of equipment failure prediction and task scheduling flexibility, they rely on preset programs and limited response mechanisms, making it difficult to respond to emergencies in real time, resulting in poor production efficiency and equipment utilization. In a production environment where IoT technology is not integrated, equipment maintenance and troubleshooting need to be responded to only after problems occur, increasing production costs and extending maintenance time. The lack of the ability to flexibly adjust task priorities results in urgent tasks not being processed in a timely manner, affecting the timeliness of production and the order fulfillment rate, and limiting the performance and development of factories in the highly competitive market. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a factory task management method and system based on the Internet of Things.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a factory task management method based on the Internet of Things, comprising the following steps:

[0007] S1: Based on the equipment operation log, the operation data of multiple equipment in the production line are analyzed to identify multiple key performance indicators, predict the task processing capabilities of the equipment under differentiated working conditions, and generate equipment capacity prediction results;

[0008] S2: using the device capability prediction result, evaluating the rationality and security of multiple task processing requests, and updating the authorization policy according to the evaluation result to generate a task review authorization record;

[0009] S3: Based on the task review authorization record, according to the equipment capabilities and task requirements, analyze the requirements of various task types for production line equipment and operators, and generate equipment requirement analysis results;

[0010] S4: Analyze the processing flow of multiple tasks according to the equipment demand analysis result, identify the processing order between multiple flows, identify the interdependence between multiple tasks, construct a task dependency graph, and generate a task dependency analysis result;

[0011] S5: Analyze the task list according to the task dependency analysis result, calculate the priority scores of the multiple tasks according to the dependency relationship, processing order and urgency of the multiple tasks, and generate a priority adjustment result;

[0012] S6: Using the priority adjustment result, the working status of multiple devices on the production line is identified through IoT sensors, and the task execution sequence and resource allocation are adjusted according to the equipment load conditions to generate automated task management results.

[0013] As a further scheme of the present invention, the equipment capacity prediction results include the equipment load capacity information of the equipment, the equipment task processing speed, and the expected failure rate; the task review authorization record includes the task request list, authorization status and access demand processing record; the equipment demand analysis results include the required equipment type, the required number of equipment and the estimated task completion time; the task dependency analysis results include the order of task execution, the dependency strength between multiple tasks, and key task nodes; the priority adjustment results include the priority score of the task, the adjusted task order, and the estimated completion time; the automated task management results include the optimized path of task scheduling, resource allocation adjustment records, and the estimated processing time of the task list.

[0014] As a further solution of the present invention, based on the equipment operation log, by analyzing the operation data of multiple equipment in the production line, identifying multiple key performance indicators, predicting the task processing capacity of the equipment under differentiated working conditions, the steps of generating the equipment capacity prediction result are specifically as follows:

[0015] S101: Based on the equipment operation log, collect the operation data records of multiple equipment in the production line, format and standardize the data, and generate the operation data collection results;

[0016] S102: Based on the operation data collection result, analyze the operation data, identify multiple key performance indicators, including peak operation power, mean time between failures, and start-stop response time, and generate equipment performance analysis results;

[0017] S103: Based on the equipment performance analysis results, analyze and predict the task processing capabilities of multiple equipment in the production line under differentiated working conditions, and generate equipment capacity prediction results.

[0018] As a further solution of the present invention, the rationality and security of multiple task processing requests are evaluated by using the device capability prediction results, and the authorization policy is updated according to the evaluation results. The steps of generating task review authorization records are specifically as follows:

[0019] S201: Based on the device capability prediction result, analyzing the task processing request, comparing the device operation capability and the task requirement, evaluating the executability of the task, and generating executability evaluation information;

[0020] S202: Based on the executable evaluation information, a security evaluation is performed on a plurality of task requests, and task authority settings are adjusted according to the operation restrictions and security parameters of the device, and an authority setting update record is generated;

[0021] S203: Based on the permission setting update record, atypical access requests are identified and recorded in real time, including unauthorized operations and abnormal data access, and task review authorization records are generated.

[0022] As a further solution of the present invention, based on the task review authorization record, according to the equipment capabilities and task requirements, the requirements of various task types for production line equipment and operators are analyzed, and the steps of generating equipment requirement analysis results are specifically as follows:

[0023] S301: Based on the task review authorization record, collect equipment capabilities and demand data of multiple tasks, including the maximum load capacity of multiple devices in the production line, the requirements of the tasks for equipment load and working time, and generate demand comparison data;

[0024] S302: Based on the demand comparison data, analyze the types and quantities of equipment required by various task types and the configuration requirements of operators, and generate a configuration requirement list;

[0025] S303: Based on the configuration requirement list, according to the current equipment and human resources of the production line, a resource comparison analysis is performed to evaluate the matching degree between the existing resources and the requirements, identify resource shortages and surpluses, and generate equipment requirement analysis results.

[0026] As a further solution of the present invention, based on the equipment demand analysis result, the processing flow of multiple tasks is analyzed, the processing order between multiple flows is identified, and the interdependence between multiple tasks is identified, and a task dependency graph is constructed. The steps of generating the task dependency analysis result are specifically as follows:

[0027] S401: Analyze the processing flows of multiple tasks based on the equipment demand analysis result, sort the multiple flows according to the processing order, and generate a task flow analysis result;

[0028] S402: Based on the task flow analysis result, identify the sequence and dependency relationship between multiple tasks, consider resource sharing and time overlap, and generate task relationship identification information;

[0029] S403: According to the task relationship identification information and the mutual relationships between multiple tasks and processing flows, a task dependency relationship diagram is drawn to generate a task dependency analysis result.

[0030] As a further solution of the present invention, according to the task dependency analysis result, the task list is analyzed, and the priority scores of the multiple tasks are calculated according to the dependency relationship, processing order and urgency of the multiple tasks. The steps of generating the priority adjustment result are specifically as follows:

[0031] S501: Based on the task dependency analysis result, generating task priority analysis data according to the dependency relationship, processing order and urgency among multiple tasks;

[0032] S502: Based on the task priority analysis data, priority scores of multiple tasks are calculated according to urgency and dependency, and priority score calculation results are generated;

[0033] S503: Based on the priority score calculation result, the actual execution order of the tasks on the production line is adjusted, the task processing flow is optimized, and a priority adjustment result is generated.

[0034] As a further solution of the present invention, the specific formula for calculating the priority scores of multiple tasks is:

[0035]

[0036] in, represents the task priority score, Represents the urgency of the task, which is calculated based on the difference between the task deadline and the current date. is the weight coefficient of urgency, Represents the complexity of task dependencies, which is evaluated based on the number of other tasks it depends on. is the weight coefficient of the dependency relationship, Represents the estimated completion time of the task. is the weight coefficient of the estimated completion time, Represents the resource usage ratio required by the task.

[0037] As a further solution of the present invention, the priority adjustment result is used to identify the working status of multiple devices on the production line through IoT sensors, and the task execution order and resource allocation are adjusted according to the equipment load conditions. The steps of generating the automated task management result are specifically as follows:

[0038] S601: Based on the priority adjustment result, the working status of multiple devices in the production line is collected in real time through IoT sensors, the real-time load and performance status of the devices are recorded, and device status monitoring data is generated;

[0039] S602: Based on the device status monitoring data, analyze the load conditions of multiple devices, evaluate the adjustment requirements of resource allocation and task execution sequence, calculate the optimal task execution path, and generate an execution sequence adjustment record;

[0040] S603: Based on the execution order adjustment record, according to the real-time load of the equipment, the task execution order and resource allocation are adjusted to optimize the production line operation efficiency and generate an automated task management result.

[0041] A factory task management system based on the Internet of Things, the factory task management system based on the Internet of Things is used to execute the factory task management method based on the Internet of Things, and the system includes:

[0042] The equipment performance evaluation module analyzes and identifies the key performance indicators of multiple equipment on the production line based on the equipment operation log, predicts the equipment's task processing capabilities, analyzes the received task processing requests, evaluates the rationality and security of multiple task processing requests, and generates task review authorization records;

[0043] The task requirement analysis module analyzes the requirements of various tasks for equipment types, quantities and operators based on the task review authorization records, and generates equipment requirement analysis results;

[0044] The task flow management module analyzes the processing flow of multiple tasks based on the equipment demand analysis result, identifies the dependency relationship between the multiple tasks, and generates a task dependency analysis result;

[0045] The priority management module calculates the processing priorities of multiple tasks based on the task dependency analysis results, adjusts the execution order of the tasks to match the actual production demand changes, and generates priority adjustment results;

[0046] Based on the priority adjustment results, the dynamic resource scheduling module monitors the equipment working status and load conditions in real time through the Internet of Things sensors, dynamically adjusts resource allocation and task execution order, optimizes the work efficiency and response speed of the production line, and generates automated task management results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by monitoring and analyzing the operating data of the equipment in the production line, identifying key performance indicators and predicting equipment performance, the flexibility of equipment use is improved, resource allocation is optimized and production interruptions due to equipment failures are reduced, real-time evaluation and authorization updates of task processing requests are performed to ensure the safety of operations and improve response speed, and combined with the analysis of equipment requirements and task dependencies, task scheduling is optimized, the task execution order is automatically adjusted according to the real-time equipment load, the production line processing efficiency is optimized, and the delivery speed and quality of products are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a factory task management method based on the Internet of Things, comprising the following steps:

[0060] S1: Based on the equipment operation log, the operation data of multiple equipment in the production line are analyzed to identify multiple key performance indicators, predict the task processing capabilities of the equipment under differentiated working conditions, and generate equipment capacity prediction results;

[0061] S2: Use the device capability prediction results to evaluate the rationality and security of multiple task processing requests, update the authorization policy based on the evaluation results, and generate task review authorization records;

[0062] S3: Based on the task review authorization records, according to the equipment capabilities and task requirements, analyze the requirements of various task types for production line equipment and operators, and generate equipment demand analysis results;

[0063] S4: Analyze the processing flow of multiple tasks based on the equipment demand analysis results, identify the processing order between multiple processes, identify the interdependencies between multiple tasks, build a task dependency graph, and generate task dependency analysis results;

[0064] S5: Analyze the task list according to the task dependency analysis result, calculate the priority scores of multiple tasks according to the dependency, processing order and urgency of multiple tasks, and generate the priority adjustment result;

[0065] S6: Utilize the priority adjustment results to identify the working status of multiple devices on the production line through IoT sensors, adjust the task execution order and resource allocation according to the equipment load conditions, and generate automated task management results.

[0066] The equipment capacity prediction results include the equipment load capacity information, equipment task processing speed, and expected failure rate of the equipment. The task review and authorization records include the task request list, authorization status, and access demand processing records. The equipment demand analysis results include the required equipment type, the required number of equipment, and the estimated task completion time. The task dependency analysis results include the order of task execution, the dependency strength between multiple tasks, and key task nodes. The priority adjustment results include the priority score of the task, the adjusted task order, and the estimated completion time. The automated task management results include the optimized path of task scheduling, resource allocation adjustment records, and the estimated processing time of the task list.

[0067] See also Figure 2 Based on the equipment operation log, the operation data of multiple equipment in the production line are analyzed to identify multiple key performance indicators, and the task processing capabilities of the equipment under differentiated working conditions are predicted. The specific steps for generating equipment capacity prediction results are as follows:

[0068] S101: Based on the equipment operation log, collect the operation data records of multiple equipment in the production line, format and standardize the data, and generate the operation data collection results;

[0069] In sub-step S101, based on the equipment operation log, the data acquisition system automatically receives and records the operation data of each device, including start and stop time, power consumption, and fault records. The collected data is preliminarily cleaned to remove invalid or erroneous data input. The cleaning process uses data cleaning software to optimize data quality through set rules such as value range filtering, duplicate data deletion, and timestamp correction. Formatting is performed, involving data type conversion such as converting text dates to standard formats and normalizing values. Standardized operations ensure the consistency and comparability of data in subsequent analysis. The formatting and standardization process uses a custom Python script, which is written according to the requirements of the production line and adapts to the data characteristics of different equipment. The generated operation data collection results are stored in the central database for use in subsequent steps. The process ensures the accuracy of the data and the efficiency of the operation.

[0070] S102: Based on the operation data collection results, analyze the operation data, identify multiple key performance indicators, including peak operation power, mean time between failures, and start-stop response time, and generate equipment performance analysis results;

[0071] In the above content, by analyzing the operating data, identifying the peak operating power and the mean time between failures, according to the formula , calculate the average power; according to the formula , calculate the mean time between failures.

[0072] In the formula, represents the operating power measured multiple times, is the total number of measurements, Represents the average operating power, Represents the time between multiple failures. is the number of failures, Stands for Mean Time Between Failures.

[0073] Detailed explanation of the formula and the process of formula calculation and derivation:

[0074] Assumptions The measured values ​​are 100, 200, 150, 180, and 220 watts. ,calculate :

[0075]

[0076] The calculation results show an average power of 170 watts, and the formula is used to estimate the energy consumption level of the equipment under standard operating conditions.

[0077] Assumptions The values ​​are 30, 45, 60, 25, and 50 hours respectively. ,calculate :

[0078]

[0079] The calculation results show that the mean time between failures is 42 hours. The formula is used to determine the maintenance frequency and reliability of the equipment and obtain a reasonable maintenance cycle.

[0080] S103: Based on the equipment performance analysis results, analyze and predict the task processing capabilities of multiple equipment in the production line under differentiated working conditions, and generate equipment capacity prediction results;

[0081] In sub-step S103, based on the equipment performance analysis results, the random forest model in the linear regression and machine learning algorithms is used to predict the task processing capability of the equipment. Under differentiated working conditions, the parameters of the working conditions are defined, including the operating environment temperature, production line speed, and material type. Then, the parameters and performance indicators are input into the prediction model. The model training process is carried out using historical data sets to ensure the accuracy of the prediction. The training results of each model will evaluate the performance of the model, such as accuracy and recall. After the prediction results are generated, the results are evaluated to check their effectiveness in the actual production environment. The prediction model helps the production management team understand and prepare for the performance of different equipment when working conditions change in the future. The generated equipment capability prediction results provide scientific decision support for production line management.

[0082] See also Figure 3, using the device capability prediction results, evaluate the rationality and security of multiple task processing requests, and update the authorization policy based on the evaluation results. The specific steps for generating task review authorization records are as follows:

[0083] S201: Based on the equipment capability prediction result, analyze the task processing request, compare the equipment operation capability and the task requirement, evaluate the executability of the task, and generate executability evaluation information;

[0084] In sub-step S201, based on the equipment capacity prediction results, the task processing request is analyzed, and the requirements in the task request, such as the required processing time and resource consumption, are matched with the predicted operating capacity of the equipment. Numerical comparison analysis is used to determine whether the equipment can meet the request. Decision support system software is used to automatically compare the predicted data with the actual requirements. The executableness of each task is evaluated based on the performance parameters of the equipment and the requirements of the task. A logistic regression model is used to predict the success rate of task execution. The variables include equipment power, continuous working time and failure rate. The model output includes the executable score of the task. The executable evaluation information is generated based on the score. The information helps production schedulers decide whether to start a task or adjust task parameters to ensure production efficiency and equipment safety.

[0085] S202: Based on the executable evaluation information, a security evaluation is performed on multiple task requests, and task authority settings are adjusted according to the operation restrictions and security parameters of the device, and an authority setting update record is generated;

[0086] In sub-step S202, a security evaluation of the task request is performed based on the executable assessment information. The step involves the setting and adjustment of task permissions. The security evaluation uses professional security assessment tools, such as SAP's security module, to evaluate the task request based on the equipment's operating restrictions such as maximum operating time and safety parameters such as emergency shutdown thresholds. During the evaluation process, if the task request exceeds the safety parameter range, the task permission adjustment is automatically triggered. The adjustment process is executed by the task management system, which automatically updates the task permissions based on the assessment results and generates a permission setting update record. The record lists the permission change details for each task, such as the reduction from high permissions to medium permissions and the reason for the adjustment, to ensure the compliance of task execution and the safety of equipment operation.

[0087] S203: Update records based on permission settings, identify and record atypical access requests in real time, including unauthorized operations and abnormal data access, and generate task review authorization records;

[0088] In sub-step S203, records are updated based on permission settings, and real-time monitoring is implemented to identify and record atypical access requests. Using access control and monitoring systems, such as Cisco's network access control system, the system monitors access requests based on the latest permission settings, identifies unauthorized operations and abnormal data access, including unauthorized login attempts to the system and improper access attempts to sensitive data. Each identification process is recorded in the system log, and through machine learning models, such as anomaly detection algorithms, access behavior patterns are analyzed to generate task review authorization records. The records detail the detected security incidents and their processing results, which helps to enhance the system's security protection and ensure that the security of data and equipment is not threatened.

[0089] See also Figure 4 , based on the task review authorization records, according to the equipment capabilities and task requirements, analyze the requirements of various task types for production line equipment and operators, and generate the equipment demand analysis results in the following steps:

[0090] S301: Based on the task review authorization record, collect equipment capabilities and demand data of multiple tasks, including the maximum load capacity of multiple devices in the production line, the task's requirements for equipment load and working time, and generate demand comparison data;

[0091] In sub-step S301, based on the task review authorization record, the equipment capability and task requirement data are collected. Through automated data collection, the maximum load capacity data of multiple devices are extracted from the production line management database. The specific requirements of each task for equipment load and working time are collected through the task management system. The data is uploaded in real time by the distributed sensor network and integrated through the data interface. During the collection process, data verification technology is used to ensure the accuracy and completeness of the collected data, including data type verification and range verification. After the data collection is completed, data integration processing is performed, and SQL database query and processing statements are used to associate equipment capability data and task requirements to generate demand comparison data. The comparison data lists in detail the demand conditions faced by each device, which is convenient for subsequent analysis and resource allocation.

[0092] S302: Based on the demand comparison data, analyze the equipment types and quantities and configuration requirements of operators required for various task types, and generate a configuration requirement list;

[0093] In sub-step S302, based on the demand control data, the task type and equipment demand analysis is performed. According to the requirements of different task types, the required equipment types, quantities, and operator configurations are analyzed. During the analysis, cluster analysis methods are used to identify the dependence pattern of task types on equipment, determine the specific requirements of different tasks for different equipment types, and calculate the optimal number of operators required to complete various tasks. The calculation is estimated using a predictive model based on historical data. The generated configuration requirement list describes the equipment and human resources required for each task type. This information is crucial for resource allocation and task scheduling.

[0094] S303: Based on the configuration requirement list, perform resource comparison analysis according to the current equipment and human resources of the production line, evaluate the matching degree between existing resources and requirements, identify resource shortages and surpluses, and generate equipment requirement analysis results;

[0095] In sub-step S303, a resource comparison analysis is performed based on the configuration requirement list. The analysis is performed through the resource management system. The equipment resources and human resources on the current production line are summarized and compared with the configuration requirement list. The degree of match between the existing resources and the requirements is analyzed, including the comparison of equipment types, quantities and human resources. The analysis is performed using difference analysis and resource optimization algorithms, such as linear programming models, to identify the current shortage and surplus of resources. The generated equipment demand analysis results list the shortage or surplus and reasons for each resource category. The results provide data support for resource reconfiguration and help management make decisions.

[0096] See also Figure 5 ,According to the equipment demand analysis results, analyze the processing flow of multiple tasks, identify the processing order between multiple processes, and identify the interdependencies between multiple tasks, build a task dependency graph, and generate the task dependency analysis results in the following steps:

[0097] S401: Analyze the processing flow of multiple tasks based on the equipment demand analysis result, sort the multiple flows according to the processing order, and generate the task flow analysis result;

[0098] In sub-step S401, based on the results of the equipment demand analysis, the task processing flow is analyzed. The analysis is performed through process analysis software, which automatically identifies and records the processing flow of each task, including the starting point, end point and key operation steps involved in each process. During the analysis, attention is paid to the time efficiency and resource allocation in the process, and the priority of each process is determined. The process sorting adopts a priority-based scheduling algorithm, such as the shortest remaining time priority algorithm, which takes into account the urgency and complexity of each task to determine the execution order. The generated task process analysis results show the process priority sorting, which provides a basis for subsequent task scheduling, ensures that tasks can be carried out in the optimal order, and improves the efficiency of the overall production line.

[0099] S402: Based on the task flow analysis results, identify the sequence and dependency relationship between multiple tasks, consider resource sharing and time overlap, and generate task relationship identification information;

[0100] In sub-step S402, based on the task flow analysis results, the order and dependencies between tasks are identified, the data flow and resource dependencies between tasks are analyzed, and the tasks that can be executed in parallel and the tasks that need to be executed serially are determined. In the dependency identification process, the topological sorting algorithm in graph theory is used to process the execution order of tasks and identify the sequential dependencies between tasks. The generated task relationship identification information includes the dependent nodes and execution windows of each task. This information is crucial for avoiding resource conflicts and optimizing task scheduling, ensuring that resources can be shared efficiently and time overlaps are properly managed.

[0101] S403: Draw a task dependency relationship diagram based on the task relationship identification information and the relationships between multiple tasks and processing flows, and generate a task dependency analysis result;

[0102] In sub-step S403, a task dependency diagram is drawn based on the task relationship identification information. The process uses project management software, such as Microsoft Project. The software automatically generates a dependency diagram using the provided task relationship data. The diagram clearly shows the relationships between tasks, including the specific types of dependencies. Through visual display, the task management team can clearly see the dependency structure of all tasks, which is very helpful for understanding the complexity and critical path of the entire project. The generated task dependency analysis results provide accurate guidance for project planning and resource allocation, and help the refined operation of project management.

[0103] See also Figure 6 ,According to the task dependency analysis results, the task list is analyzed, and the priority scores of multiple tasks are calculated according to the dependency, processing order and urgency of multiple tasks. The specific steps for generating the priority adjustment results are as follows:

[0104] S501: Based on the task dependency analysis result, task priority analysis data is generated according to the dependency relationship, processing order and urgency among multiple tasks;

[0105] In sub-step S501, task priority analysis data is generated based on the results of task dependency analysis. The data analysis platform SAS or SPSS is used, and the identified dependencies, processing order and urgency are used as input parameters. The weights of various parameters are set, including the weight of urgency being higher than the weight of dependencies. The weighted average algorithm is applied to calculate the initial priority score of each task. The calculation process includes digitizing the urgency and the number of dependencies of each task, and performing a comprehensive score based on the set weights. The generated task priority analysis data lists the priority score of each task and its calculation basis, providing scientific data support for task scheduling and ensuring that urgent and critical tasks are processed first.

[0106] S502: Based on the task priority analysis data, priority scores of multiple tasks are calculated according to urgency and dependency, and priority score calculation results are generated;

[0107] The specific formula for calculating the priority scores of multiple tasks is:

[0108]

[0109] in, represents the task priority score, Represents the urgency of the task, which is calculated based on the difference between the task deadline and the current date. is the weight coefficient of urgency, Represents the complexity of task dependencies, which is evaluated based on the number of other tasks it depends on. is the weight coefficient of the dependency relationship, Represents the estimated completion time of the task. is the weight coefficient of the estimated completion time, Represents the resource usage ratio required by the task.

[0110] formula:

[0111]

[0112] Detailed explanation of the formula and the process of formula calculation and derivation:

[0113] The formula is used to calculate the priority score of the task, adjust the priority of the production task, and optimize the production efficiency and the speed of responding to market demand;

[0114] Parameter meaning and setting value:

[0115] Indicates the urgency of the task, assuming that the urgency of the target task , considering the actual production environment, the weight of urgency is 0.5, and the weight reflects the importance of urgency in task priority;

[0116] represents the complexity of task dependencies, assuming that the dependency complexity , the weight of the dependency is 0.3;

[0117] Represents the estimated completion time of the task, assuming the estimated completion time , the weight of task time is 0.2, reflecting the influence of the estimated time in the total priority;

[0118] Indicates the task resource occupancy ratio. Assuming that the resource occupancy ratio ;

[0119] Substitute the parameters into the formula for calculation:

[0120]

[0121]

[0122]

[0123] result It indicates that the priority score of the task is relatively high, indicating that the task should be scheduled for execution first. The result is used to ensure that urgent and high-dependency tasks receive sufficient resources and priority.

[0124] S503: Based on the priority score calculation result, adjust the actual execution order of the tasks on the production line, optimize the task processing flow, and generate a priority adjustment result;

[0125] In sub-step S503, based on the priority score calculation results, the actual execution order of tasks on the production line is adjusted and executed through the production line management system. The system receives the priority score calculation results, adjusts the task scheduling according to the results, optimizes the task processing flow, and uses dynamic scheduling algorithms such as genetic algorithms or simulated annealing algorithms to find the optimal task execution order in a complex production environment. Resource allocation conflicts are automatically detected and resolved during the adjustment process to ensure the smoothness and efficiency of task execution. The generated priority adjustment results improve the operating efficiency of the production line, ensure that important tasks can be given priority at critical time points, and maximize the output and efficiency of the production line.

[0126] See also Figure 7, using the priority adjustment results, through the IoT sensors, identify the working status of multiple devices on the production line, and adjust the task execution order and resource allocation according to the equipment load. The specific steps to generate automated task management results are:

[0127] S601: Based on the priority adjustment result, the working status of multiple devices in the production line is collected in real time through IoT sensors, the real-time load and performance status of the devices are recorded, and the device status monitoring data is generated;

[0128] In the above content, by collecting the working status of the production line equipment in real time and calculating the real-time load of the equipment, according to the formula

[0129] , calculate the device load percentage.

[0130] In the formula, Represents the current power usage of the device. Represents the maximum power capacity of the device, Represents the real-time load percentage of the device.

[0131] Detailed explanation of the formula and the process of formula calculation and derivation:

[0132] Assume the current power usage of the device The maximum power capacity is 320 kW For 400 kW, calculate :

[0133]

[0134] The calculation result shows that the real-time load of the equipment is 80%. The formula is used to monitor the operating efficiency and safe operating boundary of the equipment. The result of 80% indicates that the equipment is operating at a high load. This information helps the maintenance team to perform maintenance or adjustments in a timely manner to prevent equipment overload and ensure the stability and safety of the production line.

[0135] S602: Based on the equipment status monitoring data, analyze the load conditions of multiple equipment, evaluate the adjustment requirements of resource allocation and task execution sequence, calculate the optimal task execution path, and generate an execution sequence adjustment record;

[0136] In sub-step S602, load analysis and resource allocation evaluation are performed based on equipment status monitoring data using the complex event processing system EsperTech. The system analyzes the load balance of the equipment based on real-time monitoring data, evaluates the effectiveness of the current resource allocation, and adjusts the task execution order based on the equipment load. Dynamic path optimization algorithms, such as the Dijkstra algorithm, are used in the calculation process to calculate the optimal task execution path based on the equipment layout and task requirements of the production line. The optimization process takes into account the reduction of equipment conversion time and waiting time. The generated execution order adjustment record lists each task execution order and the corresponding equipment load adjustment, thereby improving resource utilization efficiency and reducing production delays.

[0137] S603: Based on the execution order adjustment record, adjust the task execution order and resource allocation according to the real-time load of the equipment, optimize the production line operation efficiency, and generate automated task management results;

[0138] In sub-step S603, based on the execution order adjustment records, the task execution order and resource allocation are optimized and executed through the automated task management system. The system automatically adjusts the task execution order according to the real-time load of the equipment, and uses genetic algorithms to simulate the genetic mechanism in the natural selection process to find the optimal task allocation plan. The automation process takes into account minimizing job interruptions and maximizing output efficiency. The system updates the task status and resource allocation in real time. The generated automated task management results include detailed task scheduling and resource utilization, ensuring that the production line operates at the highest efficiency, while reducing operation and maintenance costs and improving overall production efficiency.

[0139] See also Figure 8 , a factory task management system based on the Internet of Things, the factory task management system based on the Internet of Things is used to execute the above-mentioned factory task management method based on the Internet of Things, the system includes:

[0140] The equipment performance evaluation module analyzes and identifies the key performance indicators of multiple equipment on the production line based on the equipment operation log, predicts the equipment's task processing capabilities, analyzes the received task processing requests, evaluates the rationality and security of multiple task processing requests, and generates task review authorization records;

[0141] The task demand analysis module analyzes the requirements of various tasks for equipment types, quantities and operators based on the task review authorization records, and generates equipment demand analysis results;

[0142] The task flow management module analyzes the processing flow of multiple tasks based on the equipment demand analysis results, identifies the dependencies between multiple tasks, and generates task dependency analysis results;

[0143] The priority management module calculates the processing priorities of multiple tasks based on the task dependency analysis results, adjusts the execution order of tasks to match the actual production demand changes, and generates priority adjustment results;

[0144] Based on the priority adjustment results, the dynamic resource scheduling module monitors the equipment working status and load conditions in real time through IoT sensors, dynamically adjusts resource allocation and task execution order, optimizes the work efficiency and response speed of the production line, and generates automated task management results.

[0145] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A factory task management method based on the Internet of Things, characterized in that: The following steps are involved: Based on the equipment operation log, the operation data of multiple equipment in the production line are analyzed to identify multiple key performance indicators, predict the task processing capabilities of the equipment under differentiated working conditions, and generate equipment capacity prediction results; Using the device capability prediction results, evaluate the rationality and security of multiple task processing requests, and update the authorization policy according to the evaluation results to generate task review authorization records; Based on the task review authorization record, according to the equipment capabilities and task requirements, the requirements of various task types for production line equipment and operators are analyzed to generate equipment requirement analysis results; Analyze the processing flow of multiple tasks according to the equipment demand analysis result, identify the processing order between multiple flows, identify the interdependencies between multiple tasks, construct a task dependency graph, and generate task dependency analysis results; Analyzing the task list according to the task dependency analysis result, calculating the priority scores of the multiple tasks according to the dependency relationship, processing order and urgency of the multiple tasks, and generating a priority adjustment result; By utilizing the priority adjustment result, the working status of multiple devices on the production line is identified through IoT sensors, and the task execution sequence and resource allocation are adjusted according to the equipment load conditions to generate automated task management results.

2. The factory task management method based on the Internet of Things according to claim 1 is characterized in that: The equipment capacity prediction results include the equipment load capacity information, equipment task processing speed, and expected failure rate of the equipment; the task review authorization records include task request lists, authorization status, and access demand processing records; the equipment demand analysis results include required equipment types, required equipment quantities, and estimated task completion time; the task dependency analysis results include the order of task execution, the dependency strength between multiple tasks, and key task nodes; the priority adjustment results include the priority score of the task, the adjusted task order, and the estimated completion time; the automated task management results include the optimized path of task scheduling, resource allocation adjustment records, and the estimated processing time of the task list.

3. The factory task management method based on the Internet of Things according to claim 1 is characterized in that: Based on the equipment operation log, the operation data of multiple equipment in the production line are analyzed to identify multiple key performance indicators, and the task processing capabilities of the equipment under differentiated working conditions are predicted. The specific steps for generating equipment capacity prediction results are as follows: Based on the equipment operation log, collect the operation data records of multiple equipment in the production line, format and standardize the data, and generate the operation data collection results; Based on the operation data collection results, analyze the operation data, identify multiple key performance indicators, including peak operation power, mean time between failures, and start-stop response time, and generate equipment performance analysis results; Based on the equipment performance analysis results, the task processing capabilities of multiple equipment in the production line under differentiated working conditions are analyzed and predicted to generate equipment capacity prediction results.

4. The factory task management method based on the Internet of Things according to claim 1 is characterized in that: The device capability prediction result is used to evaluate the rationality and security of multiple task processing requests, and the authorization policy is updated according to the evaluation result. The specific steps of generating task review authorization records are as follows: Based on the equipment capability prediction result, analyzing the task processing request, comparing the equipment operation capability and the task requirement, evaluating the executability of the task, and generating executability evaluation information; Based on the executable evaluation information, a plurality of task requests are evaluated for safety, and task authority settings are adjusted according to the operation restrictions and safety parameters of the device, and an authority setting update record is generated; Based on the permission setting update record, atypical access requests, including unauthorized operations and abnormal data access, are identified and recorded in real time, and task review authorization records are generated.

5. The factory task management method based on the Internet of Things according to claim 1 is characterized in that: Based on the task review authorization record, according to the equipment capabilities and task requirements, the requirements of various task types for production line equipment and operators are analyzed, and the steps for generating equipment requirement analysis results are specifically as follows: Based on the task review authorization record, collect equipment capacity and demand data of multiple tasks, including the maximum load capacity of multiple devices in the production line, the demand of the task for equipment load and working time, and generate demand comparison data; Based on the demand comparison data, analyze the equipment types, quantities and configuration requirements of operators required for various task types, and generate a configuration requirement list; Based on the configuration requirement list, a resource comparison analysis is performed according to the current equipment and human resources of the production line, the matching degree between the existing resources and the requirements is evaluated, resource shortages and surpluses are identified, and equipment requirement analysis results are generated.

6. The factory task management method based on the Internet of Things according to claim 1 is characterized in that: According to the equipment demand analysis result, the processing flow of multiple tasks is analyzed, the processing order between multiple flows is identified, and the interdependence between multiple tasks is identified, and a task dependency graph is constructed. The steps of generating the task dependency analysis result are specifically as follows: Based on the equipment demand analysis result, analyzing the processing flow of multiple tasks, and sorting the multiple flows according to the processing order to generate a task flow analysis result; Based on the task flow analysis results, identify the order and dependency relationships between multiple tasks, consider resource sharing and time overlap, and generate task relationship identification information; According to the task relationship identification information and the mutual relationships between multiple tasks and processing flows, a task dependency relationship diagram is drawn to generate a task dependency analysis result.

7. The factory task management method based on the Internet of Things according to claim 1 is characterized in that: According to the task dependency analysis result, the task list is analyzed, and the priority scores of the multiple tasks are calculated according to the dependency relationship, processing order and urgency of the multiple tasks. The steps of generating the priority adjustment result are specifically as follows: Based on the task dependency analysis results, generating task priority analysis data according to the dependency relationships, processing order and urgency among multiple tasks; Based on the task priority analysis data, priority scores of multiple tasks are calculated according to urgency and dependency, and priority score calculation results are generated; Based on the priority score calculation result, the actual execution order of the tasks on the production line is adjusted, the task processing flow is optimized, and the priority adjustment result is generated.

8. The factory task management method based on the Internet of Things according to claim 7 is characterized in that: The specific formula for calculating the priority scores of multiple tasks is: in, represents the task priority score, Represents the urgency of the task, which is calculated based on the difference between the task deadline and the current date. is the weight coefficient of urgency, Represents the complexity of task dependencies, which is evaluated based on the number of other tasks it depends on. is the weight coefficient of the dependency relationship, Represents the estimated completion time of the task. is the weight coefficient of the estimated completion time, Represents the resource usage ratio required by the task.

9. The factory task management method based on the Internet of Things according to claim 1 is characterized in that: The priority adjustment result is used to identify the working status of multiple devices on the production line through IoT sensors, and the task execution order and resource allocation are adjusted according to the equipment load. The steps for generating the automated task management result are as follows: Based on the priority adjustment result, the working status of multiple devices in the production line is collected in real time through IoT sensors, the real-time load and performance status of the devices are recorded, and device status monitoring data is generated; Based on the device status monitoring data, analyze the load conditions of multiple devices, evaluate the adjustment requirements of resource allocation and task execution sequence, calculate the optimal task execution path, and generate execution sequence adjustment records; Based on the execution order adjustment records, the task execution order and resource allocation are adjusted according to the real-time load conditions of the equipment, the production line operation efficiency is optimized, and the automated task management results are generated.

10. A factory task management system based on the Internet of Things, characterized in that: According to the factory task management method based on the Internet of Things according to any one of claims 1 to 9, the system comprises: The equipment performance evaluation module analyzes and identifies the key performance indicators of multiple equipment on the production line based on the equipment operation log, predicts the equipment's task processing capabilities, analyzes the received task processing requests, evaluates the rationality and security of multiple task processing requests, and generates task review authorization records; The task requirement analysis module analyzes the requirements of various tasks for equipment types, quantities and operators based on the task review authorization records, and generates equipment requirement analysis results; The task flow management module analyzes the processing flow of multiple tasks based on the equipment demand analysis result, identifies the dependency relationship between the multiple tasks, and generates a task dependency analysis result; The priority management module calculates the processing priorities of multiple tasks based on the task dependency analysis results, adjusts the execution order of the tasks to match the actual production demand changes, and generates priority adjustment results; Based on the priority adjustment results, the dynamic resource scheduling module monitors the equipment working status and load conditions in real time through the Internet of Things sensors, dynamically adjusts resource allocation and task execution order, optimizes the work efficiency and response speed of the production line, and generates automated task management results.

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