Production progress management method and system based on Internet of Things

Abstract

The invention discloses a production progress management method and system based on the Internet of Things, and relates to the related field of the industrial Internet, and the method comprises the steps: obtaining a historical production data set, and extracting a maximum load, a current load, and an adaptation feature set of a task; a production task is obtained, task adaptation analysis of a production line is carried out, and an adaptation value set is established; task demand analysis is carried out on the production task, and an importance factor is established; carrying out comprehensive adaptation degree calculation to generate a comprehensive adaptation degree calculation result; constructing a production line synergistic effect coefficient, constructing a target optimization function, and establishing a synergistic effect activation condition and a task allocation constraint condition; carrying out production task allocation optimization, and constructing matching mapping; time sequence monitoring is carried out, and production management is carried out. The technical problem of low production task allocation and management control accuracy in existing production progress management is solved, and the technical effect of efficient allocation and accurate management of the production tasks is achieved through accurate production line load and task adaptation degree evaluation.

Description

Technical Field

[0001] This application relates to fields related to the industrial Internet, and in particular to a production progress management method and system based on the Internet of Things. Background Art

[0002] Among existing production schedule management methods, most companies use a production task allocation method based on manual or simple rules. This method relies on the experience and judgment of production managers and cannot accurately assess the load capacity of the production line and the adaptability of the task, which easily leads to waste of production resources and low production efficiency.

[0003] In the current related technologies, production progress management has technical problems such as low accuracy in production task allocation and management control. Summary of the Invention

[0004] This application provides a production progress management method and system based on the Internet of Things, adopts the Internet of Things technology to establish digital communication with multiple production lines, obtains the historical production data and real-time operation status of the production lines in real time, accurately evaluates the maximum load, current load and task adaptability of the production lines, realizes efficient allocation of production tasks and collaborative optimization of production lines, and achieves the technical effect of efficient allocation and precise management of production tasks through accurate production line load and task adaptability evaluation.

[0005] This application provides a production progress management method based on the Internet of Things, including:

[0006] Establish digital communication with n production lines, obtain historical production data sets of the n production lines, and extract the maximum load, current load, and task fitness feature set of the n production lines based on the historical production data sets; obtain m production tasks, perform task fitness analysis of the m production tasks for the n production lines based on the fitness feature set, and establish a set of fitness values for the production lines and tasks; perform task demand analysis on the m production tasks and establish task importance factors; perform comprehensive fitness calculation on the tasks using the fitness value set, maximum load, and current load to generate a comprehensive fitness calculation result; construct a production line synergy effect coefficient under production task allocation, construct a target optimization function using the synergy effect coefficient and comprehensive fitness calculation result, and establish synergy effect activation conditions and task allocation constraints; optimize production task allocation using the target optimization function, synergy effect activation conditions, task allocation constraints, and importance factors, and construct a matching mapping between production tasks and production lines; perform time series monitoring based on the matching mapping, and perform production management of the n production lines based on the time series monitoring results.

[0007] In a possible implementation, the comprehensive fitness calculation of the task is performed using the adaptation value set, the maximum load, and the current load to generate a comprehensive fitness calculation result, and the following processing is performed:

[0008] The adaptation value set is analyzed to construct quality adaptation and quantity adaptation; a comprehensive adaptation calculation formula is established as follows: ij =α1Q ij +α2N ij +α3(1-L i ), where d ij is the comprehensive adaptability of production line i to production task j, Q ij is the quality adaptability of production line i to production task j, N ij is the quantity adaptation of production line i to production task j, L i is the current load rate of production line i, which is obtained by the ratio of the current load to the maximum load. α1, α2, and α3 are the fitness weight coefficients of quality fitness, quantity fitness, and current load rate, respectively.

[0009] In a possible implementation, the target optimization function is constructed by using the synergy effect coefficient and the comprehensive fitness calculation result, and the following processing is performed:

[0010] Construct the target optimization function, the formula is as follows: Co-op ik =β ik γ ik ; Where Z is the target optimization function, n is the total number of production lines, m is the total number of production tasks, x ij is a decision variable. If production task j is assigned to production line i, then x ij =1, otherwise x ij =0, Coop ik Characterizes the synergistic effect of production line i and production line k, β ik is the synergy coefficient between production line i and production line k, γ ik is a binary decision variable, representing whether production line i and production line k work together.

[0011] In a possible implementation, the following processing is performed:

[0012] Establish a record timing zero point, use the record timing zero point to record the usage of n production lines, and calculate the utilization rate based on the usage record results; obtain the utilization rate calculation result, and construct an additional matching coefficient between the production task and the production line based on the utilization rate calculation result; use the additional matching coefficient to perform optimal compensation for production task allocation, and perform production management of the n production lines based on the compensation result.

[0013] In a possible implementation, the following processing is performed:

[0014] An Internet of Things network for n production lines is established, wherein the Internet of Things network includes a central cloud platform and terminal sensors, and the central cloud platform and the terminal sensors communicate digitally; acquisition parameters of the terminal sensors are configured according to the matching mapping, and the terminal sensors are controlled to perform data acquisition based on the acquisition parameters to establish time series monitoring results; the time series monitoring results are fed back to the central cloud platform, and production management of the n production lines is performed based on the Internet of Things network.

[0015] In a possible implementation, the timing monitoring results are fed back to the central cloud platform, and production management of n production lines is performed based on the central cloud platform, and the following processing is performed:

[0016] Based on the time series monitoring results, the task execution status of the production task is analyzed to establish a first abnormal result; an equipment abnormality prediction model for n production lines is established, and after data extraction from the time series monitoring results, the data is input into the equipment abnormality prediction model to generate a second abnormal result; production tasks are adaptively scheduled according to the first abnormal result and the second abnormal result, and production management of the n production lines is performed according to the production task adaptive scheduling results.

[0017] In a possible implementation, the following processing is performed:

[0018] A predictive maintenance plan is configured based on the second abnormal result, and a maintenance penalty factor is established; a scheduling impact analysis of the production task is performed through the time series monitoring results, and a scheduling penalty factor is established; and an adaptive scheduling penalty analysis is performed using the maintenance penalty factor and the scheduling penalty factor to obtain an adaptive scheduling result for the production task.

[0019] This application also provides a production progress management system based on the Internet of Things, including:

[0020] A historical production data set acquisition module, the historical production data set acquisition module is used to establish digital communication with n production lines, obtain the production line historical production data sets of n production lines, and extract the maximum load, current load, and task fitness feature set of n production lines based on the historical production data sets; a task adaptation analysis module, the task adaptation analysis module is used to obtain m production tasks, perform task adaptation analysis of n production lines on the m production tasks based on the fitness feature set, and establish a set of fitness values for production lines and tasks; a task importance factor establishment module, the task importance factor establishment module is used to perform task demand analysis on m production tasks and establish task importance factors; a comprehensive fitness calculation result generation module, the comprehensive fitness calculation result generation module is used to generate fitness values through the fitness value The comprehensive fitness calculation of the task is performed based on the set, maximum load and current load to generate a comprehensive fitness calculation result; a target optimization function construction module, the target optimization function construction module is used to construct the production line synergy coefficient under the production task allocation, and construct the target optimization function through the synergy coefficient and the comprehensive fitness calculation result, and establish the synergy activation condition and the task allocation constraint condition; a production task allocation optimization module, the production task allocation optimization module is used to optimize the production task allocation through the target optimization function, synergy activation condition, task allocation constraint condition and importance factor, and construct a matching mapping between the production task and the production line; a production management module, the production management module is used to perform time series monitoring based on the matching mapping, and perform production management of n production lines according to the time series monitoring results.

[0021] The production schedule management method and system based on the Internet of Things proposed in this application first establish digital communication with n production lines, obtain the historical production data set of the n production lines, extract the maximum load, current load, and task adaptation feature set of the n production lines based on the historical production data set, then obtain m production tasks, perform task adaptation analysis of the n production lines on the m production tasks based on the adaptation feature set, establish an adaptation value set of the production line and the task, then perform task demand analysis on the m production tasks, establish the importance factor of the task, and then calculate the comprehensive adaptation of the task through the adaptation value set, maximum load and current load to generate a comprehensive The results of suitable matching calculation are used to construct the production line synergy coefficient under production task allocation. The target optimization function is constructed through the synergy coefficient and the comprehensive fitness calculation results, and the synergy activation conditions and task allocation constraints are established. Then, the production task allocation is optimized through the target optimization function, synergy activation conditions, task allocation constraints and importance factors, and a matching mapping between production tasks and production lines is constructed. Finally, time series monitoring is performed based on the matching mapping, and production management of n production lines is performed according to the time series monitoring results, achieving the technical effect of efficient allocation and precise management of production tasks through accurate production line load and task fitness evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0023] Figure 1 A flowchart of a production schedule management method based on the Internet of Things provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of the structure of a production schedule management system based on the Internet of Things provided in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: historical production data set acquisition module 10, task adaptation analysis module 20, task importance factor establishment module 30, comprehensive adaptation calculation result generation module 40, target optimization function construction module 50, production task allocation optimization module 60, production management module 70. DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, 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 art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0029] The present application embodiment provides a production schedule management method based on the Internet of Things, such as Figure 1 As shown, the method includes:

[0030] Step S100, establish digital communication with n production lines, obtain the historical production data sets of the n production lines, and extract the maximum load, current load, and task fitness feature sets of the n production lines based on the historical production data sets. Specifically, according to the equipment and system of the production line, select a suitable communication protocol, such as MQTT, CoAP, etc., to ensure compatibility with the production line equipment, configure network equipment such as routers, switches, gateways, etc. at the production site so that data can be transmitted smoothly, deploy sensors and monitoring equipment on the production line to collect production data in real time, and the sensors and monitoring equipment may include temperature sensors, pressure sensors, RFID readers, cameras, etc., connect the sensors and monitoring equipment to the network equipment via wired or wireless means, and establish a communication connection with the central control system. Through digital communication, collect historical production data from the production line equipment, including output, production time, equipment status, etc., clean the collected historical production data, remove abnormal values, duplicate values, etc., to ensure the accuracy and reliability of the data. Use data analysis tools or algorithms to analyze historical production data sets and extract key features. Based on historical production data, calculate the maximum production capacity or load of each production line by analyzing parameters such as the maximum output power and maximum production speed of the equipment (maximum load, that is, the maximum production capacity or load that the production line can achieve under specific conditions); obtain the current production data of the production line in real time, and calculate the current production load (current load, that is, the actual production load of the production line) by analyzing parameters such as the real-time output power and production speed of the equipment; based on the type and requirements of the production task, as well as the equipment capacity, process flow and other information of the production line, extract the adaptability features between the production task and the production line, that is, the features or indicators that measure the degree of matching between the production task and the production line, which may include the complexity of the production task, the degree of automation of the production line, the compatibility of the equipment, etc., and organize the extracted task adaptability features into a adaptability feature set.

[0031] Step S200: Obtain m production tasks, perform task adaptation analysis on the m production tasks for n production lines based on the fitness feature set, and establish a set of fitness values for the production lines and tasks. Specifically, obtain m production tasks to be assigned from a task management system or production plan, wherein the production tasks include specific production requirements, quantity, priority, delivery date, and other information. For each production task, based on its feature value in the task fitness feature set, the complexity, output requirements, delivery date of the task are compared with the production capacity, process flow, equipment configuration, and other factors of the production line, and the feature values of the n production lines are matched. Based on the results of the feature matching, the fitness between each production task and each production line is evaluated by calculating a fitness score, rating, or other quantitative indicator. The fitness evaluation results between each production task and each production line are integrated to form a fitness value set. The fitness value set can be a two-dimensional fitness value matrix or table, where each row of the matrix or table represents a production task and each column represents a production line. The elements in the matrix or table are the corresponding fitness values.

[0032] Step S300 performs a task requirements analysis on m production tasks and establishes task importance factors. Specifically, detailed information on the m production tasks, including task type, quantity, delivery date, quality standards, and special requirements, is collected from the task management system. This collected task information is then analyzed in detail to determine the specific requirements and constraints for each production task, including task urgency, importance, and production difficulty. Key elements are identified from the task requirements. Based on the results of the task requirements analysis, indicators for measuring task importance are determined, including delivery urgency, special product quality requirements, and scarcity of production resources. Each importance indicator is quantified and assigned a weight, which can be determined based on the actual production task situation and historical data. Based on the actual production task situation and the weight of each importance indicator, an importance score for each production task is calculated using methods such as weighted summation and weighted product. The calculated importance score is used as the task's importance factor. The importance factor can be a specific numerical value or level that represents the importance and priority of the task.

[0033] Step S400, the comprehensive fitness calculation of the task is performed through the adaptation value set, the maximum load and the current load to generate a comprehensive fitness calculation result. Specifically, the comprehensive fitness is a quantitative indicator used to measure the overall fitness between the production task and the production line, and comprehensively considers the fitness value (the degree of matching between the task and the production line) and the residual capacity (the residual production capacity of the production line). Specifically, from the adaptation value set, the fitness values between m production tasks and n production lines are extracted, and the fitness value, maximum load and current load data are organized into a table or data structure for easy calculation. A comprehensive fitness model is configured to calculate the comprehensive fitness, wherein the fitness value weight is defined according to the importance of the fitness value in task allocation, and the residual capacity weight is defined according to the importance of the residual capacity (maximum load minus current load) in task allocation. The comprehensive fitness calculation result is obtained based on the weighted calculation of the fitness value and the residual capacity for each combination of each production task and each production line.

[0034] In a possible implementation, step S400 further includes step S410, parsing the adaptation value set to construct quality adaptation and quantity adaptation. Specifically, the adaptation value set includes the adaptation values of the production line to the production task in multiple dimensions. The adaptation value set is parsed to identify the adaptation values related to quality and the adaptation values related to quantity. Based on the adaptation values related to quality, the quality adaptation of the production line to the production task is constructed through a certain calculation method (such as weighted summation, average value, etc.). The quality adaptation reflects the degree to which the production line meets the production task requirements in terms of quality; based on the adaptation values related to quantity, the quantity adaptation of the production line to the production task is constructed through a similar calculation method. The quantity adaptation reflects the degree to which the production line meets the production task requirements in terms of quantity. Step S420, establish a comprehensive adaptation calculation formula as follows:

[0035] d ij =α1Q ij +α2N ij +α3(1-L i );

[0036] Among them, d ij is the comprehensive adaptability of production line i to production task j, Q ij is the quality adaptability of production line i to production task j, N ij is the quantity adaptation of production line i to production task j, L iis the current load rate of production line i, which is obtained by the ratio of the current load to the maximum load. α1, α2, and α3 are the fitness weight coefficients of quality fitness, quantity fitness, and current load rate, respectively. Specifically, the comprehensive fitness reflects the overall fitness of the production line to the production task in multiple dimensions, including quality, quantity, and current load rate. This implementation method evaluates the adaptability of the production line in terms of quality and quantity by constructing quality fitness and quantity fitness. By introducing the current load rate, the actual load situation of the production line is taken into account, avoiding overload or underload. By combining various fitnesses, the technical effect of comprehensively evaluating the adaptability of the production line to the production task is achieved.

[0037] Step S500, construct the production line synergy coefficient under the production task allocation, construct the target optimization function through the synergy coefficient and the comprehensive fitness calculation results, and establish the synergy activation conditions and task allocation constraint conditions. Specifically, the production line synergy refers to the phenomenon that when multiple production tasks are allocated to different production lines, the efficiency of the entire production system is improved or the cost is reduced due to factors such as complementarity and resource sharing between production lines. Analyze the key factors affecting the production line synergy, such as logistics efficiency, information sharing level, production capacity matching, personnel collaboration, etc. between production lines, establish quantitative indicators for each synergy factor, and obtain the specific values of these indicators through historical data, expert evaluation, etc. Based on the quantitative indicators of the synergy factors, use mathematical models (such as weighted summation, multiple regression, etc.) to construct the production line synergy coefficient. The production line synergy coefficient reflects the strength of the synergy between production lines under different production task allocations. Clarify the optimization objectives for production task allocation, such as maximizing overall production efficiency and minimizing production costs. Identify the decision variables in the target optimization function, including the production line to which each production task is assigned. Utilize the comprehensive fitness calculation results and the production line synergy coefficient as components of the target optimization function. Combined with the optimization objectives, construct the target optimization function. The target function is a mathematical expression that measures the overall effectiveness of different production task allocation schemes. Based on the characteristics and actual conditions of production line synergies, analyze and determine the conditions for activating synergies, including specific task combinations and the sharing of specific resources between production lines. Translate the resulting synergy activation conditions into mathematical expressions or logical conditions for judgment and application during the task allocation process. Analyze the constraints within the production task allocation process, such as capacity limits, delivery requirements, and process limitations. Translate the resulting task allocation constraints into mathematical expressions or logical conditions, using them as constraints in the optimization process to ensure the feasibility and effectiveness of the task allocation scheme.

[0038] In a possible implementation, the target optimization function is constructed by using the synergy effect coefficient and the comprehensive fitness calculation result. Step S500 further includes step S510 of constructing the target optimization function. The formula is as follows:

[0039]

[0040] Co-op ik =β ik γ ik ;

[0041] Among them, Z is the target optimization function, n is the total number of production lines, m is the total number of production tasks, x ij is a decision variable. If production task j is assigned to production line i, then x ij =1, otherwise x ij =0, Coop ik Characterizes the synergistic effect of production line i and production line k, β ik is the synergy coefficient between production line i and production line k, γ ik is a binary decision variable representing whether production lines i and k work together. This implementation constructs a target optimization function, ensuring that the allocation results not only take into account the comprehensive compatibility between production lines and production tasks, but also the synergy between production lines. By maximizing synergy, the efficiency and performance of the entire production system can be improved, while resource waste and costs can be reduced. By ensuring that production tasks are assigned to the production line with the highest compatibility, production quality and efficiency can be improved, and problems and risks in the production process can be reduced, achieving a comprehensive and effective technical solution to the production task allocation problem.

[0042] Step S600 optimizes production task allocation using the target optimization function, synergy activation conditions, task allocation constraints, and importance factors, establishing a matching mapping between production tasks and production lines. Specifically, the constructed target optimization function is loaded into the optimization algorithm, the established synergy activation conditions and task allocation constraints are set as constraints in the optimization algorithm, and the importance factor is used to adjust the priority of different production tasks during the optimization process. According to the scale of the production task allocation problem, the parameters and variables of the optimization algorithm are initialized. In each iteration, the effectiveness of the current task allocation scheme is evaluated based on the objective optimization function, synergy activation conditions, task allocation constraints, and importance factors. Based on the evaluation results, the task allocation scheme is adjusted through the optimization algorithm (such as genetic algorithm, simulated annealing, particle swarm optimization, etc.) to find a better solution. The above steps are repeated until the algorithm's stopping conditions are met (such as reaching the maximum number of iterations, finding a solution that meets the requirements, etc.). During the iterative process, the optimal solution obtained in each iteration (i.e., the matching mapping between production tasks and production lines) is recorded, and the optimal task allocation scheme is extracted from the output of the optimization algorithm. Based on the optimal solution, a matching mapping between production tasks and production lines is constructed, that is, one or more suitable production lines are assigned to each production task.

[0043] Step S700, based on the matching mapping, timing monitoring is performed, and production management of n production lines is performed according to the timing monitoring results. Specifically, relevant information is extracted from the matching mapping between production tasks and production lines, including the production line corresponding to each production task, the start and end time of production, the required resources, etc., and the parameters of timing monitoring are set according to production requirements and matching mapping, such as the monitoring time interval, the key monitoring indicators (such as production progress, equipment status, material consumption, etc.), and timing monitoring is performed on each production line according to the set time interval and monitoring parameters through the factory's production management system, Internet of Things devices (such as PLC, sensors, etc.) or manual inspections. The data obtained during the timing monitoring process is collected, organized and analyzed to form a production status report. Based on the production status report, the key indicators such as the production progress, equipment status, material consumption, etc. of each production line are analyzed to determine whether there are abnormal conditions or potential risks. Based on the analysis results, corresponding production management measures are formulated. For example, for production lines with lagging production progress, the production plan can be adjusted, resource input can be increased, or the production process can be optimized; for problems such as equipment failure or material shortages, timely repairs, replacements, or purchases can be carried out. Through the production management process, timely understanding of production status, identification of potential problems and corresponding adjustments are made to ensure the smooth progress of production activities and the achievement of goals. The embodiment of the present application uses the Internet of Things technology to establish digital communication with multiple production lines, obtain the historical production data and real-time operation status of the production lines in real time, accurately evaluate the maximum load, current load and task adaptability of the production lines, and achieve efficient allocation of production tasks and collaborative optimization of production lines. The technical effect of achieving efficient allocation and precise management of production tasks through accurate production line load and task adaptability evaluation is achieved.

[0044] In one possible implementation, step S700 further includes step S710, establishing a recording time sequence zero point, recording the usage of n production lines through the recording time sequence zero point, and calculating the utilization rate based on the usage record results. Specifically, a fixed time point (such as the beginning of each day, Monday of each week, etc.) is selected as the recording time sequence zero point, and the recording time sequence zero point is used as the benchmark for starting to record the usage of the production lines. Starting from the recording time sequence zero point, the usage of each production line is continuously recorded, including operating time, idle time, failure time, etc. Based on the collected usage record data, the utilization rate of each production line is calculated, that is, the proportion of the actual operating time of the production line to the total time, reflecting the actual usage of the production line. Step S720, obtaining the utilization rate calculation result, and constructing an additional matching coefficient between the production task and the production line based on the utilization rate calculation result. Specifically, the utilization rate calculation result of each production line is obtained, and based on the utilization rate calculation result, an additional matching coefficient is constructed for each production line. The additional matching coefficient reflects the actual usage of the production line and the degree of matching between the production task. For example, production lines with high utilization rates are more suitable for handling urgent or high-priority production tasks. Step S730, the production task allocation is optimized and compensated by the additional matching coefficient, and the production management of the n production lines is performed based on the compensation results. Specifically, on the basis of the original production task allocation plan, optimization compensation is performed according to the additional matching coefficient. For example, some production tasks can be adjusted from production lines with low utilization rates to production lines with high utilization rates and high additional matching coefficients to improve the efficiency and performance of the entire production system, and actual production management operations are performed according to the compensated production task allocation plan, such as adjusting production plans, allocating production resources, etc. This implementation method further optimizes the production task allocation plan by performing optimization compensation for production task allocation based on the additional matching coefficient, realizes more refined and dynamic management of production lines and production tasks, and achieves the technical effect of improving the efficiency and performance of the entire production system.

[0045] In one possible implementation, step S700 further includes step S740, establishing an IoT network for n production lines, wherein the IoT network includes a central cloud platform and terminal sensors, and the central cloud platform and the terminal sensors communicate digitally. Specifically, IoT devices are deployed for each production line, including a central cloud platform and terminal sensors, and the terminal sensors are connected to the central cloud platform via wired or wireless means, wherein the central cloud platform is the core part of the IoT network, and is used to receive, process, store and forward data from the terminal sensors; the terminal sensors are IoT devices deployed on the production lines, and are used to collect real-time data from the production lines. Step S750, configure the acquisition parameters of the terminal sensors according to the matching mapping, control the terminal sensors to perform data acquisition based on the acquisition parameters, and establish time series monitoring results. Specifically, the matching mapping between production tasks and production lines is combined with the actual needs of the production lines to configure the acquisition parameters of the terminal sensors, such as acquisition frequency, data type, data format, etc. According to the configured acquisition parameters, the terminal sensors are controlled to perform data acquisition tasks, and the real-time data of the production lines are collected. The collected data are sorted and analyzed in chronological order to establish time series monitoring results. The time series monitoring results reflect the operating status and performance of the production lines. Step S760, the time series monitoring results are fed back to the central cloud platform, and production management of n production lines is performed based on the Internet of Things network. Specifically, the time series monitoring results collected by the terminal sensors are transmitted to the central cloud platform in real time. The central cloud platform processes and analyzes the received data, extracts useful information, and makes production management decisions based on the processed data, such as adjusting the production plan, optimizing the production process, etc. According to the production management decisions, corresponding production management operations are performed, such as starting or stopping the production line, adjusting production parameters, etc. This implementation method realizes real-time, accurate and intelligent monitoring and management of production lines by introducing Internet of Things technology for production management, improves the technical effect of providing timely information support for production management decisions, and improves the accuracy and efficiency of decision-making.

[0046] In one possible implementation, step S760 further includes step S761, performing task execution status analysis of the production task based on the timing monitoring results, and establishing a first abnormal result. Specifically, real-time monitoring data is obtained from the timing monitoring system, and the task execution status is analyzed using statistical methods, machine learning algorithms, or expert systems to identify whether there are abnormalities in progress, efficiency, quality, etc., such as decreased production efficiency, unstable product quality, etc. The analyzed abnormal results are integrated into a first abnormal result, including the type, degree, and possible causes of the abnormality. Step S762, establishes an equipment abnormality prediction model for n production lines, extracts data from the timing monitoring results, and inputs them into the equipment abnormality prediction model to generate a second abnormal result. Specifically, for each production line's equipment, historical data is used to establish an anomaly prediction model based on machine learning, deep learning, or traditional statistical methods. Data related to the equipment's status, such as temperature, vibration, and current, is extracted from the time-series monitoring results. This extracted data is then input into the corresponding equipment anomaly prediction model. The equipment anomaly prediction model predicts the equipment's future state based on the input data, identifies possible anomalies, and integrates the predicted anomaly results into a second anomaly result, including the anomaly type, occurrence time, and impact range. In step S763, adaptive production task scheduling is performed based on the first and second anomaly results, and production management of the n production lines is performed based on the adaptive scheduling results. Specifically, the first and second anomaly results are integrated, and their correlation and priority are analyzed. Based on the integrated anomaly results, adaptive production task scheduling is performed, including resource reallocation, production plan adjustment, and production process optimization. Based on the adaptive scheduling results, specific production management operations are performed on the n production lines to ensure the smooth progress of production tasks. This implementation method, through real-time monitoring and predictive analysis, promptly detects and resolves anomalies in the production process, avoiding production interruptions and product quality issues, thereby achieving the technical effect of optimizing production processes and reducing production costs.

[0047] In one possible implementation, step S763 further includes step S7631, configuring a predictive maintenance plan based on the second abnormal result and establishing a maintenance penalty factor. Specifically, the second abnormal result is analyzed in detail to determine which equipment or production links have potential failure risks, as well as the time and severity of the possible failure. Based on the analysis results, a predictive maintenance plan is formulated for each equipment or production link with potential risks, including regular inspections, replacement of wearing parts, adjustment of working parameters and other measures to reduce equipment failure rates and improve equipment availability and production stability. Establish a maintenance penalty factor. The maintenance penalty factor is a quantitative indicator calculated based on historical data, expert experience or risk assessment models. It is used to measure the losses or impacts that may result from not implementing the predictive maintenance plan, including factors such as the probability of failure, the impact of the failure on production, and maintenance costs. Step S7632, perform scheduling impact analysis of production tasks through time series monitoring results and establish a scheduling penalty factor. Specifically, the time-series monitoring data is analyzed to determine the real-time status of production tasks, including task progress and resource usage. Based on the analysis results, the impact of different production task scheduling schemes on the production system is analyzed, including production efficiency, delivery time, and resource utilization. A scheduling penalty factor is established. The scheduling penalty factor is a quantitative indicator used to assess the potential losses or impacts of different scheduling schemes, including increased costs due to decreased production efficiency and customer dissatisfaction caused by delayed delivery. In step S7633, an adaptive scheduling penalty analysis is performed using the maintenance penalty factor and the scheduling penalty factor to obtain an adaptive scheduling result for production tasks. Specifically, the maintenance penalty factor and the scheduling penalty factor are integrated to form a comprehensive penalty factor system. Based on this integrated penalty factor system, a penalty analysis is performed on different production task scheduling schemes, assessing the total potential losses or impacts of each scheme. The scheduling scheme with the lowest losses or impacts from the penalty analysis results is selected as the adaptive scheduling result. This implementation method, by establishing a penalty factor, comprehensively considers the risk of equipment failure and the impact of production task scheduling, and quantitatively analyzes adaptive scheduling, thereby achieving the technical effect of improving the accuracy and reliability of adaptive scheduling for production tasks.

[0048] In the above, refer to Figure 1 The production schedule management method based on the Internet of Things according to an embodiment of the present invention is described in detail. Figure 2 The present invention is described in detail with reference to an embodiment of the present invention.

[0049] The IoT-based production schedule management system according to an embodiment of the present invention is designed to address the technical issues of low precision in production task allocation and management control in existing production schedule management systems, achieving the technical effect of efficient allocation and precise management of production tasks through accurate production line load and task adaptability assessment. The IoT-based production schedule management system includes: a historical production data set acquisition module 10, a task adaptability analysis module 20, a task importance factor establishment module 30, a comprehensive adaptability calculation result generation module 40, a target optimization function construction module 50, a production task allocation optimization module 60, and a production management module 70.

[0050] The historical production data set acquisition module 10 is used to establish digital communication with n production lines, obtain the historical production data sets of the n production lines, and extract the maximum load, current load, and task fitness feature set of the n production lines based on the historical production data sets; the task adaptation analysis module 20 is used to obtain m production tasks, perform task adaptation analysis of n production lines on the m production tasks based on the fitness feature set, and establish a set of adaptation values for the production lines and tasks; the task importance factor establishment module 30 is used to perform task demand analysis on the m production tasks and establish the importance factor of the task; the comprehensive fitness calculation result generation module 40 is used to calculate the fitness value set, the maximum load and the current load of the n production lines based on the fitness feature set. The front load performs a comprehensive fitness calculation on the task to generate a comprehensive fitness calculation result; the target optimization function construction module 50 is used to construct the production line synergy coefficient under the production task allocation, and construct the target optimization function through the synergy coefficient and the comprehensive fitness calculation result, and establish the synergy activation condition and the task allocation constraint condition; the production task allocation optimization module 60 is used to optimize the production task allocation through the target optimization function, synergy activation condition, task allocation constraint condition and importance factor, and construct a matching mapping between the production task and the production line; the production management module 70 is used to perform time series monitoring based on the matching mapping, and perform production management of n production lines according to the time series monitoring results.

[0051] The specific configuration of the comprehensive fitness calculation result generation module 40 will be described in detail below. As described above, the comprehensive fitness calculation result is generated by performing a comprehensive fitness calculation based on the adaptation value set, the maximum load, and the current load. The comprehensive fitness calculation result generation module 40 may further include: an adaptation value set parsing unit for parsing the adaptation value set to construct quality fitness and quantity fitness; and a comprehensive fitness calculation formula establishment unit for establishing a comprehensive fitness calculation formula, as follows:

[0052] d ij =α1Q ij +α2N ij +α3(1-L i );

[0053] Among them, d ij is the comprehensive adaptability of production line i to production task j, Q ij is the quality adaptability of production line i to production task j, N ij is the quantity adaptation of production line i to production task j, L i is the current load rate of production line i, which is obtained by the ratio of the current load to the maximum load. α1, α2, and α3 are the fitness weight coefficients of quality fitness, quantity fitness, and current load rate, respectively.

[0054] The specific configuration of the target optimization function construction module 50 will be described in detail below. As described above, the target optimization function is constructed by the synergy effect coefficient and the comprehensive fitness calculation result. The target optimization function construction module 50 may further include: a target optimization function construction unit for constructing the target optimization function, the formula is as follows:

[0055]

[0056] Co-op ik =β ik γ ik ;

[0057] Among them, Z is the target optimization function, n is the total number of production lines, m is the total number of production tasks, x ij is a decision variable. If production task j is assigned to production line i, then x ij =1, otherwise x ij =0, Coop ik Characterizes the synergistic effect of production line i and production line k, β ik is the synergy coefficient between production line i and production line k, γ ik is a binary decision variable, representing whether production line i and production line k work together.

[0058] The specific configuration of production management module 70 will be described in detail below. As described above, production management module 70 may further include: a utilization calculation unit for establishing a record time zero point, recording the usage of n production lines using said record time zero point, and performing utilization calculation based on the usage record results; an additional matching coefficient construction unit for obtaining utilization calculation results and constructing additional matching coefficients between production tasks and production lines based on said utilization calculation results; and a production task allocation optimization and compensation unit for performing production task allocation optimization and compensation using said additional matching coefficients and performing production management of the n production lines based on the compensation results.

[0059] Among them, the production management module 70 can further include: an Internet of Things network establishment unit for establishing an Internet of Things network for n production lines, wherein the Internet of Things network includes a central cloud platform and terminal sensors, and the central cloud platform and the terminal sensors communicate digitally; a timing monitoring result establishment unit for configuring the acquisition parameters of the terminal sensors according to the matching mapping, controlling the terminal sensors to perform data acquisition based on the acquisition parameters, and establishing timing monitoring results; a production management unit for feeding back the timing monitoring results to the central cloud platform, and performing production management of n production lines based on the Internet of Things network.

[0060] Among them, the timing monitoring results are fed back to the central cloud platform, and production management of n production lines is performed based on the central cloud platform. The production management unit may further include: a first abnormal result establishment subunit is used to perform task execution status analysis of the production task based on the timing monitoring results, and establish a first abnormal result; a second abnormal result generation subunit is used to establish an equipment abnormality prediction model for n production lines, extract data from the timing monitoring results, and input them into the equipment abnormality prediction model to generate a second abnormal result; a production task adaptive scheduling subunit is used to perform production task adaptive scheduling according to the first abnormal result and the second abnormal result, and perform production management of n production lines according to the production task adaptive scheduling results.

[0061] Among them, the production task adaptive scheduling sub-unit may further include: a predictive maintenance plan configuration micro-unit for configuring a predictive maintenance plan based on the second abnormal result and establishing a maintenance penalty factor; a scheduling impact analysis micro-unit for performing scheduling impact analysis of production tasks through time series monitoring results and establishing a scheduling penalty factor; an adaptive scheduling penalty analysis micro-unit for performing adaptive scheduling penalty analysis through the maintenance penalty factor and the scheduling penalty factor to obtain a production task adaptive scheduling result.

[0062] The Internet of Things-based production progress management system provided by the embodiment of the present invention can execute the Internet of Things-based production progress management method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0063] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0064] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. The production progress management method based on the Internet of Things is characterized by: The method comprises: Establish digital communication with n production lines, obtain historical production data sets of the n production lines, and extract maximum load, current load, and task fitness feature sets of the n production lines based on the historical production data sets; Obtain m production tasks, perform task adaptation analysis on n production lines for the m production tasks based on the adaptation feature set, and establish an adaptation value set between the production lines and the tasks; Perform task demand analysis on m production tasks and establish task importance factors; Performing a comprehensive fitness calculation on the task using the adaptation value set, the maximum load, and the current load to generate a comprehensive fitness calculation result; Constructing a production line synergy effect coefficient under production task allocation, constructing a target optimization function based on the synergy effect coefficient and comprehensive fitness calculation results, and establishing synergy effect activation conditions and task allocation constraint conditions; Optimize the production task allocation through the target optimization function, synergy effect activation conditions, task allocation constraints and importance factors, and build a matching mapping between production tasks and production lines; Timing monitoring is performed based on the matching mapping, and production management of n production lines is performed according to the timing monitoring results.

2. The production progress management method based on the Internet of Things according to claim 1, characterized in that: The step of calculating the comprehensive fitness of the task using the adaptation value set, the maximum load, and the current load to generate a comprehensive fitness calculation result further includes: Parsing the adaptation value set to construct quality adaptation and quantity adaptation; The comprehensive fitness calculation formula is established as follows: d ij =α1Q ij +α2N ij +α3(1-L i ); Among them, d ij is the comprehensive adaptability of production line i to production task j, Q ij is the quality adaptability of production line i to production task j, N ij is the quantity adaptation of production line i to production task j, L i is the current load rate of production line i, which is obtained by the ratio of the current load to the maximum load. α1, α2, and α3 are the fitness weight coefficients of quality fitness, quantity fitness, and current load rate, respectively.

3. The production progress management method based on the Internet of Things according to claim 1, characterized in that: The target optimization function is constructed by calculating the synergistic effect coefficient and the comprehensive fitness degree, and further includes: Construct the target optimization function, the formula is as follows: Coop ik =b ik ·c ik ; Among them, Z is the target optimization function, n is the total number of production lines, m is the total number of production tasks, x ij is a decision variable. If production task j is assigned to production line i, then x ij =1, otherwise x ij =0, Coop ik Characterizes the synergistic effect of production line i and production line k, β ik is the synergy coefficient between production line i and production line k, γ ik is a binary decision variable, representing whether production line i and production line k work together.

4. The production progress management method based on the Internet of Things according to claim 1, characterized in that: The method further comprises: Establishing a recording time sequence zero point, recording the usage of n production lines through the recording time sequence zero point, and calculating the utilization rate based on the usage record results; Obtaining utilization calculation results, and constructing additional matching coefficients between production tasks and production lines based on the utilization calculation results; The additional matching coefficient is used to perform optimization compensation for production task allocation, and production management of n production lines is performed based on the compensation results.

5. The production progress management method based on the Internet of Things according to claim 1, characterized in that: The method further comprises: Establishing an Internet of Things network for n production lines, wherein the Internet of Things network includes a central cloud platform and terminal sensors, and the central cloud platform and terminal sensors communicate digitally; configuring acquisition parameters of the terminal sensor according to the matching mapping, controlling the terminal sensor to perform data acquisition based on the acquisition parameters, and establishing a time series monitoring result; The timing monitoring results are fed back to the central cloud platform, and production management of n production lines is performed based on the Internet of Things network.

6. The method for production progress management based on the Internet of Things according to claim 5, characterized in that: Feeding back the time series monitoring results to the central cloud platform, and performing production management of n production lines based on the central cloud platform, further includes: Performing task execution status analysis on the production task based on the time series monitoring result to establish a first abnormal result; Establishing an equipment anomaly prediction model for n production lines, extracting data from the time series monitoring results, and inputting the extracted data into the equipment anomaly prediction model to generate a second anomaly result; Adaptive scheduling of production tasks is performed according to the first abnormal result and the second abnormal result, and production management of n production lines is performed according to the adaptive scheduling results of production tasks.

7. The method for production progress management based on the Internet of Things according to claim 6, characterized in that: The method further comprises: configuring a predictive maintenance plan based on the second abnormal result and establishing a maintenance penalty factor; Conduct scheduling impact analysis on production tasks based on time series monitoring results and establish scheduling penalty factors; An adaptive scheduling penalty analysis is performed using the maintenance penalty factor and the scheduling penalty factor to obtain an adaptive scheduling result of a production task.

8. The production progress management system based on the Internet of Things is characterized by: The system is used to implement the production progress management method based on the Internet of Things according to any one of claims 1 to 7, and the system includes: A historical production data set acquisition module, the historical production data set acquisition module is used to establish digital communication with n production lines, obtain the production line historical production data sets of the n production lines, and extract the maximum load, current load, and task fitness feature sets of the n production lines based on the historical production data sets; A task adaptation analysis module, which is used to obtain m production tasks, perform task adaptation analysis on n production lines for the m production tasks based on the adaptation feature set, and establish an adaptation value set between the production lines and the tasks; A task importance factor establishment module, wherein the task importance factor establishment module is used to perform task demand analysis on m production tasks and establish task importance factors; A comprehensive fitness calculation result generation module, the comprehensive fitness calculation result generation module is used to perform comprehensive fitness calculation of the task based on the adaptation value set, maximum load and current load to generate a comprehensive fitness calculation result; A target optimization function construction module is used to construct a production line synergy effect coefficient under production task allocation, construct a target optimization function based on the synergy effect coefficient and the comprehensive fitness calculation result, and establish synergy effect activation conditions and task allocation constraint conditions; A production task allocation optimization module, which is used to optimize production task allocation based on the objective optimization function, synergy effect activation conditions, task allocation constraints and importance factors, and to construct a matching mapping between production tasks and production lines; A production management module is used to perform time sequence monitoring based on the matching mapping, and to perform production management of n production lines according to the time sequence monitoring results.

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