Cable production management method and system based on Internet of Things

Abstract

The invention provides a cable production management method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: building a multi-process correlation topological structure through obtaining the sensing data of the Internet of Things of a production line, and building an inter-process beat balance constraint condition based on the multi-process correlation topological structure. And an initial scheduling sequence is generated according to the production order and the constraint condition, scheduling conflicts are identified and adjusted through dynamic conflict detection, and finally an optimized scheduling sequence is generated to control production. According to the invention, dynamic collaboration and conflict resolution among multiple processes are realized, and the overall efficiency and resource utilization rate of cable production are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things, and in particular to a cable production management method and system based on Internet of Things. BACKGROUND

[0002] In the current cable manufacturing industry, with the popularization of industrial Internet of Things technology, production management is gradually transforming towards digitization and intelligentization. The existing conventional approach is usually to deploy sensors at each key process node of the production line, to collect data such as equipment status, material flow and production progress in real time, and upload them to the central management system. The manager arranges production tasks and schedules resources for each process independently according to these discrete data points and experience. The common approach is to use scheduling methods based on fixed beat or empirical formula to set relatively independent production plans for each process, and to coordinate the connection between processes through manual or simple system alarms. This management mode aims to improve the visibility and execution efficiency of individual processes to meet the diverse production order requirements.

[0003] However, the above conventional approach has obvious defects. Due to the lack of systematic modeling and analysis of the dynamic correlation between processes, the planning of each process is often carried out in isolation, which easily leads to imbalance between upstream and downstream processes. The output rate of the upstream process does not match the receiving rate of the downstream process, often causing problems such as work-in-process backlog or downstream equipment downtime due to material waiting, resulting in production process interruption and resource waste. At the same time, when faced with multi-variety and small-batch order changes, static or experience-driven scheduling methods are difficult to quickly identify potential conflicts caused by material transfer and resource competition, such as overlapping of multiple order tasks in the same process resource occupation period, or long material waiting time disrupting the overall production rhythm. These conflicts are often exposed during production execution and rely on manual intervention for adjustment, resulting in scheduling response lag, reduced production efficiency, and difficulty in ensuring on-time delivery of orders. SUMMARY

[0004] The embodiments of the present application provide a cable production management method and system based on Internet of Things, which can solve the problems in the prior art.

[0005] In a first aspect, the embodiments of the present application provide a cable production management method based on Internet of Things, comprising: obtaining Internet of Things sensing data of each process node in a cable manufacturing production line, and constructing a multi-process correlation topology structure according to the Internet of Things sensing data, the multi-process correlation topology structure representing the material transfer relationship and production capacity coupling constraints between each process node; establishing a beat balance constraint condition between processes based on the multi-process correlation topology, the beat balance constraint condition determining a target production beat range of each process node by calculating a production capacity matching degree between adjacent process nodes, so that an output rate of an upstream process node and a receiving rate of a downstream process node are maintained within a preset cooperation threshold; receiving production order demand information, and generating an initial scheduling sequence according to the production order demand information and the beat balance constraint condition, the initial scheduling sequence including task allocation timing and resource occupation period of each process node; performing dynamic conflict detection on the initial scheduling sequence, the dynamic conflict detection identifying scheduling conflicts in the task allocation timing due to overlapping of resource occupation periods or exceeding of material waiting time beyond the target production beat range by traversing pairs of process nodes having a material transfer relationship in the multi-process correlation topology, and adjusting task timing of process nodes having scheduling conflicts according to the production capacity coupling constraint to generate an optimized scheduling sequence, and controlling each production device to perform production work according to the optimized scheduling sequence.

[0006] establishing a beat balance constraint condition between processes based on the multi-process correlation topology, including: extracting historical processing cycle data sequences of each process node from the multi-process correlation topology, and obtaining a processing cycle fluctuation interval of each process node by statistical analysis on the historical processing cycle data sequences; calculating a beat elasticity coefficient of each process node according to the processing cycle fluctuation interval, the beat elasticity coefficient representing an adjustable amplitude of adjusting the production beat within the fluctuation interval; for each pair of adjacent process nodes having a material transfer relationship, constructing a joint elasticity space based on the beat elasticity coefficients of the upstream process node and the downstream process node, and searching for a beat combination point in the joint elasticity space that minimizes the deviation between the output rate of the upstream process node and the receiving rate of the downstream process node; expanding the preset cooperation threshold outward to form a cooperative beat domain of the pair of adjacent process nodes with the beat combination point as the center, and constraining the target production beat range of each process node to the intersection region of all cooperative beat domains in which it participates; encapsulating the constraint relationship that the target production beat range of all process nodes and the production capacity matching degree of adjacent process node pairs need to fall within the preset cooperation threshold as the beat balance constraint condition between processes.

[0007] searching for a beat combination point in the joint elasticity space that minimizes the deviation between the output rate of the upstream process node and the receiving rate of the downstream process node, including: The beat adjustable range defined by the beat flexibility coefficient of the upstream process node is the first coordinate axis, and the beat adjustable range defined by the beat flexibility coefficient of the downstream process node is the second coordinate axis, to construct a two-dimensional joint flexibility space; A rate deviation metric index is defined in the two-dimensional joint flexibility space, and the rate deviation metric index is the absolute value of the difference between the reciprocal of the beat value corresponding to the first coordinate axis and the reciprocal of the beat value corresponding to the second coordinate axis; Discrete sampling is performed along the feasible region boundary and the interior of the two-dimensional joint flexibility space, and the rate deviation metric index value corresponding to each sampling point is calculated; The sampling point with the minimum rate deviation metric index value is selected as a candidate beat combination point, and the neighborhood of the candidate beat combination point is searched to determine the coordinate position that minimizes the rate deviation metric index value as the beat combination point; The projection values of the beat combination point on the first coordinate axis and the second coordinate axis are recorded as the recommended production beat of the upstream process node and the downstream process node in the adjacent process node pair, respectively.

[0008] An initial scheduling sequence is generated according to the production order demand information and the beat balance constraint condition, including: The product type identifier, demand quantity, and delivery time limit of each production order are extracted by analyzing the production order demand information, and the process path information corresponding to each production order is obtained according to the product type identifier; Based on the process path information, each production order is decomposed into multiple process-level sub-tasks, and a mapping relationship between each process-level sub-task and the corresponding process node in the multi-process correlation topology structure is established; According to the demand quantity and the target production beat range of each process node under the beat balance constraint condition, the estimated execution time interval of each process-level sub-task is calculated; Taking the delivery time limit as the reverse calculation reference, the planned completion time of each process-level sub-task is assigned in the reverse order direction of the process path information, and the planned completion time of each process-level sub-task and the planned start time of the successor process-level sub-task are constrained to satisfy the material flow connection condition; According to the estimated execution time interval of each process-level sub-task, the planned start time is deduced from the planned completion time to form the task allocation time sequence and resource occupation period of each process-level sub-task, and the allocation results of all process-level sub-tasks are summarized to generate the initial scheduling sequence.

[0009] Dynamic conflict detection is performed on the initial scheduling sequence, including: A scheduling time axis is constructed, the resource occupation period of all process-level sub-tasks in the initial scheduling sequence is projected onto the scheduling time axis, and a time period occupation distribution map of each process node is formed; Traverse each adjacent process node pair in the multi-process correlation topology along the material transfer direction, and extract, for each adjacent process node pair, a sequence of occupied time periods of the upstream process node and the downstream process node in the time period occupation distribution atlas; Perform timing alignment analysis on the sequence of occupied time periods, identify whether there is a negative time difference between the end time of any occupied time period of the upstream process node and the start time of the corresponding occupied time period of the downstream process node, and mark the case where there is a negative time difference as a resource occupation time period overlap conflict; Calculate the positive time difference between the end time of the occupied time period of the upstream process node and the start time of the corresponding occupied time period of the downstream process node as the actual material waiting time, compare the actual material waiting time with the allowed waiting time derived from the target production tempo range, and mark the case where the allowed waiting time is exceeded as a tempo overrun conflict; Summarize the process node pair information of all resource occupation time period overlap conflicts and tempo overrun conflicts to form the detection result set of the scheduling conflicts.

[0010] Perform timing alignment analysis on the sequence of occupied time periods, including: For each occupied time period in the sequence of occupied time periods of the upstream process node, determine the downstream process node occupied time period to which the processed material batch will flow according to the material batch flow mapping relationship recorded in the multi-process correlation topology, and establish an upstream and downstream occupied time period pairing relationship; For each upstream and downstream occupied time period pairing relationship, extract the end time of the upstream occupied time period and the start time of the downstream occupied time period, and calculate the time difference value as a timing interval; Classify and determine according to the positive and negative attributes of the timing interval, classify the pairing relationship with a negative timing interval into an overlap conflict candidate set, and classify the pairing relationship with a positive timing interval into a waiting time evaluation set; Compare the timing interval of each pairing relationship in the waiting time evaluation set with the allowed waiting time of the adjacent process node pair under the tempo balance constraint condition one by one, and transfer the pairing relationship with a timing interval exceeding the allowed waiting time into a tempo overrun conflict candidate set; Merge the overlap conflict candidate set and the tempo overrun conflict candidate set, and output as the timing alignment analysis result of the adjacent process node pair.

[0011] In a second aspect of the embodiment of the application, a cable production management system based on the Internet of Things is provided, which comprises: A data acquisition unit is configured to acquire Internet of Things sensing data of each process node in a cable manufacturing line, construct a multi-process correlation topology according to the Internet of Things sensing data, and represent the material transfer relationship and the production capacity coupling constraint between the process nodes in the multi-process correlation topology. a beat balancing unit configured to establish an inter-process beat balancing constraint condition based on the multi-process correlation topology, the beat balancing constraint condition determining a target production beat range of each process node by calculating a capacity matching degree between adjacent process nodes, so as to maintain an output rate of an upstream process node and a receiving rate of a downstream process node within a preset collaborative threshold; an initial scheduling unit configured to receive production order demand information, and generate an initial scheduling sequence according to the production order demand information and the beat balancing constraint condition, the initial scheduling sequence including task allocation timing and resource occupation period of each process node; a dynamic conflict unit configured to perform dynamic conflict detection on the initial scheduling sequence, the dynamic conflict detection identifying scheduling conflicts in the task allocation timing due to overlapping of resource occupation periods or material waiting time exceeding the target production beat range by traversing pairs of process nodes having a material transfer relationship in the multi-process correlation topology, and adjusting task timing of process nodes having scheduling conflicts according to the capacity coupling constraint to generate an optimized scheduling sequence, so as to control each production device to perform production work according to the optimized scheduling sequence.

[0012] In a third aspect, the embodiment of the present application provides an electronic device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0013] In a fourth aspect, the embodiment of the present application provides a computer readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the method described above.

[0014] The beneficial effects are as follows: The multi-process correlation topology constructed based on Internet of Things sensing data can accurately represent the material transfer relationship and capacity coupling constraint between each process node in the cable manufacturing production line, thereby realizing global visual modeling of the production process. The topology provides an accurate data basis for subsequent beat balancing and scheduling optimization, and helps to eliminate the production bottleneck identification lag problem caused by isolated process analysis in traditional management. The inter-process beat balancing constraint condition established based on the multi-process correlation topology determines the target production beat range of each process node by calculating the capacity matching degree between adjacent process nodes. This mechanism ensures that the output rate of the upstream process and the receiving rate of the downstream process can be maintained within the preset collaborative threshold, effectively reducing the work-in-process backlog or equipment idle waiting phenomenon caused by beat imbalance between processes, and improving the overall flow and resource utilization of the production line.

[0015] The initial scheduling sequence generated according to the production order demand and the beat balance constraint condition clearly shows the task allocation time sequence and resource occupation period of each process node, and provides clear guidance for production execution. The dynamic conflict detection performed on the initial scheduling sequence can identify scheduling conflicts caused by overlapping of resource occupation periods or exceeding of material waiting time through traversing process node pairs having material transfer relationship in the topology structure. The task time sequence of the conflict nodes is adjusted according to the production capacity coupling constraint, and the finally generated optimized scheduling sequence significantly enhances the executability and robustness of the production scheduling scheme. The production equipment is controlled to perform work according to the optimized scheduling sequence, realizes closed-loop precise control from order to production, and improves the on-time rate of order delivery and the adaptability of the production system. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A flowchart of the cable production management method based on the Internet of Things according to an embodiment of the present application is shown in DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0018] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and some embodiments may not be described again for the same or similar concepts or processes.

[0019] Figure 1 A flowchart of the cable production management method based on the Internet of Things according to an embodiment of the present application is shown in Figure 1 The method comprises: obtaining Internet of Things sensing data of each process node in a cable manufacturing production line, constructing a multi-process correlation topology structure according to the Internet of Things sensing data, and the multi-process correlation topology structure representing material transfer relationships and production capacity coupling constraints between the process nodes; establishing inter-process beat balance constraint conditions based on the multi-process correlation topology structure, determining target production beat ranges of the process nodes by calculating production capacity matching degrees between adjacent process nodes through the beat balance constraint conditions, and maintaining output rates of upstream process nodes and receiving rates of downstream process nodes within a preset cooperation threshold; receiving production order demand information, and generating an initial scheduling sequence based on the production order demand information and the beat balance constraint condition, the initial scheduling sequence including task allocation timing and resource occupation period of each process node; performing dynamic conflict detection on the initial scheduling sequence, the dynamic conflict detection identifying scheduling conflicts in task allocation timing caused by resource occupation period overlap or material waiting time exceeding the target production beat range by traversing pairs of process nodes with material transfer relationship in the multi-process correlation topology, and adjusting task timing of process nodes with scheduling conflicts based on the production capacity coupling constraint to generate an optimized scheduling sequence, and controlling each production device to perform production work according to the optimized scheduling sequence.

[0020] Exemplarily, in a cable manufacturing production line, Internet of Things (IoT) sensing data of each process node is obtained by deploying various sensors. These sensing data include but are not limited to production device operating state data, material flow data, processing quality data, and energy consumption data. Based on these collected data, a multi-process correlation topology is constructed, which represents the material transfer relationship and production capacity coupling constraint between each process node in the form of a directed graph. In the topology, nodes represent process nodes, edges represent material flow direction, and edge weights can represent material transfer capacity or time delay.

[0021] The production capacity coupling constraint reflects the production capacity limitation between adjacent processes, including factors such as device production capacity upper limit, process time requirement, and material buffer capacity. Through this topology, the material flow path and production capacity bottleneck of the entire production line can be intuitively understood.

[0022] Based on the constructed multi-process correlation topology, beat balance constraint conditions between processes are established. Specifically, for each pair of adjacent process nodes in the topology, the production capacity matching degree is calculated, which is the ratio of the theoretical production capacity of the upstream process to the theoretical production capacity of the downstream process. When this ratio is close to 1, it indicates that the production capacity matching degree of the two processes is high; otherwise, it indicates that the production capacity is unbalanced.

[0023] Based on the production capacity matching degree, the target production beat range is determined for each process node, so that the output rate and receiving rate of adjacent process nodes are maintained within a preset coordination threshold. This means that the production capacity difference between adjacent processes should not exceed the pre-set acceptable range, thereby ensuring the balanced operation of the entire production line.

[0024] When receiving specific production order demand information, including product specifications, quantity, delivery deadline, etc., an initial scheduling sequence is generated based on these information combined with the beat balance constraint condition. The initial scheduling uses an improved critical path method to determine the task allocation timing and resource occupation period of each process node.

[0025] The dynamic conflict detection is performed on the initial scheduling sequence to check whether there is a conflict, such as resource occupation period overlap, i.e., the same device is assigned multiple tasks in the same time period, or material waiting time exceeds the target production tempo range, i.e., the difference between the start time of the downstream process and the completion time of the upstream process exceeds the allowed range.

[0026] For the detected conflict, a heuristic adjustment algorithm is adopted to adjust the task timing of the process node according to the capacity coupling constraint. The adjustment strategy includes task reordering, resource reallocation, or tempo rate fine-tuning, etc., to generate an optimized scheduling sequence.

[0027] Finally, the optimized scheduling sequence is issued to the control system of each production device through the Internet of Things communication network, and the control device executes the production operation according to the optimized scheduling sequence. In the production process, the actual execution is continuously monitored, and when a deviation occurs, a dynamic rearrangement mechanism is triggered to ensure smooth and efficient operation of the production process.

[0028] Based on the multi-process correlation topology, a process-to-process tempo balance constraint condition is established, including: The historical processing cycle data sequence of each process node is extracted from the multi-process correlation topology, and statistical analysis is performed on the historical processing cycle data sequence to obtain the processing cycle fluctuation interval of each process node; According to the processing cycle fluctuation interval, the tempo elasticity coefficient of each process node is calculated, which represents the adjustable range of adjusting the production tempo within the fluctuation interval; For each adjacent process node pair with a material transfer relationship, a joint elasticity space is constructed based on the tempo elasticity coefficients of the upstream and downstream process nodes, and a tempo combination point is searched in the joint elasticity space that minimizes the deviation between the output rate of the upstream process node and the receiving rate of the downstream process node; The tempo combination point is taken as the center, and the preset coordination threshold is expanded outward to form the coordinated tempo domain of the adjacent process node pair, and the target production tempo range of each process node is constrained to the intersection region of all coordinated tempo domains it participates in; The target production tempo range of all process nodes and the constraint relationship that the capacity matching degree of adjacent process node pairs needs to fall within the preset coordination threshold are encapsulated as the process-to-process tempo balance constraint condition.

[0029] In a multi-process collaborative production system, the actual processing cycle of each process node shows dynamic fluctuation characteristics due to factors such as equipment performance differences, operator proficiency fluctuations, and material property changes. To achieve beat balance between processes, historical processing cycle data sequences of each process node in the past period are extracted from the multi-process correlation topology structure that has been constructed. This data sequence records the actual time consumed by each process node to complete a unit product or unit operation under different batches and different time periods. Statistical methods are used to analyze these historical data, and statistical quantities such as the mean, standard deviation, and upper and lower quartiles of the processing cycle of each process node are calculated. After removing abnormal outliers, the processing cycle fluctuation interval of each process node is determined. The lower bound of the fluctuation interval represents the shortest processing cycle that the process can achieve under ideal conditions, and the upper bound reflects the longest processing cycle under adverse conditions. In this way, the cycle fluctuation range of each process node under normal production conditions can be quantitatively described.

[0030] Based on the obtained processing cycle fluctuation interval, the beat elasticity coefficient of each process node is calculated. This coefficient is represented by the ratio of the amplitude to the mean of the fluctuation interval. In the calculation, the difference between the upper and lower bounds of the fluctuation interval is divided by the mean of the processing cycle. The larger the value of the beat elasticity coefficient, the wider the processing cycle fluctuation range relative to the average level of the process node, and the greater the adjustable beat amplitude in production scheduling. For example, the processing cycle fluctuation interval of a certain welding process is 85 seconds to 115 seconds, with a mean of 100 seconds, and its beat elasticity coefficient is 0.3. In comparison, if the fluctuation interval of the assembly process is 48 seconds to 52 seconds, with a mean of 50 seconds, its beat elasticity coefficient is only 0.08, indicating that the beat adjustment space of this process is relatively limited. The introduction of the beat elasticity coefficient provides a quantitative basis for subsequent construction of the collaborative adjustment space between processes.

[0031] For each pair of adjacent process nodes connected by a material transfer edge in the topology structure, the beat coordination relationship between them needs to be established. The beat elasticity coefficient of the upstream process node is taken as the horizontal coordinate dimension, and the beat elasticity coefficient of the downstream process node is taken as the vertical coordinate dimension to construct a two-dimensional joint elasticity space. Each coordinate point in this space corresponds to a beat adjustment combination of the upstream and downstream processes. In this space, the output rate of the upstream process is defined as the number of workpieces completed per unit time, and the receiving rate of the downstream process is defined as the number of workpieces that can start processing per unit time. Ideally, the two should be equal to avoid material accumulation or waiting. By traversing and searching in the joint elasticity space, the absolute deviation between the upstream output rate and the downstream receiving rate under different beat combinations is calculated. When a beat combination makes the deviation reach a minimum value, the point is determined as the beat combination point. This combination point represents the optimal capacity matching state that the upstream and downstream processes can achieve under the current process performance constraints.

[0032] After determining the beat combination points, considering the random disturbance and fluctuation in actual production, the process beat cannot be strictly fixed at a single value. Taking the beat combination point as the center, the region is expanded combined with the pre-set collaborative threshold. The collaborative threshold reflects the tolerance of the system to the beat deviation, which is usually set according to the buffer capacity of the production line, work-in-process inventory strategy and other factors. In the joint elastic space, a two-dimensional region is formed by expanding outward from the beat combination point. The upstream and downstream capacity deviations of all beat combination points in this region are within the allowable range of the collaborative threshold. This region is defined as the collaborative beat domain of the adjacent process node pair. Within the collaborative beat domain, the upstream and downstream processes can be fine-tuned within their respective beat elastic range, while ensuring that the capacity matching relationship between them does not deteriorate beyond the threshold limit.

[0033] In a multi-process production system, a process node may have material transfer relationships with multiple upstream or downstream process nodes at the same time, so the process node will participate in the collaborative beat domain of multiple adjacent process node pairs. To ensure that the production beat of the process node can be coordinated with all related processes, the target production beat range of the process node needs to be constrained. In specific operation, all collaborative beat domains participated by the process node are projected in the beat parameter space, and the intersection part of these projected regions is taken as the target production beat range of the process node. The intersection region ensures that the process node can select any beat value within this range, and all its upstream and downstream processes can be kept within the matching relationship allowed by the collaborative threshold. If the intersection of multiple collaborative beat domains participated by a process node is empty, it means that under the current process performance parameters and collaborative threshold setting, it is impossible to find a beat value that satisfies all collaborative relationships at the same time. At this time, the process capacity needs to be adjusted or the collaborative threshold requirement needs to be relaxed.

[0034] After determining the target production beat range of all process nodes, these range constraints and the capacity matching degree constraints between adjacent process node pairs are formalized. For each process node, its actual running beat must fall within its target production beat range. For each pair of adjacent process nodes, the capacity matching degree is measured by the ratio of the upstream output rate to the downstream receiving rate, and the deviation of this ratio from the unit value must be less than the pre-set collaborative threshold. These constraint conditions are mathematically described by a system of inequalities, constituting a complete expression of the inter-process beat balancing constraints. In the subsequent production scheduling optimization process, these constraints are included in the optimization model as hard constraints, ensuring that the generated scheduling scheme not only meets the order demand and resource constraints, but also ensures the beat coordination between processes, avoiding the loss of production efficiency caused by local process overload or idling. Through this constraint construction method based on beat elasticity coefficient and collaborative beat domain, the smooth operation and maximum utilization of the production system can be realized under the condition of dynamic fluctuation of process performance.

[0035] searching for a beat combination point in the joint elasticity space that minimizes the deviation between the output rate of the upstream process node and the receiving rate of the downstream process node, comprising: The beat adjustable range defined by the beat elasticity coefficient of the upstream process node is the first coordinate axis, and the beat adjustable range defined by the beat elasticity coefficient of the downstream process node is the second coordinate axis, to construct a two-dimensional joint elasticity space; Defining a rate deviation metric index in the two-dimensional joint elasticity space, the rate deviation metric index being the absolute value of the difference between the reciprocal of the beat value corresponding to the first coordinate axis and the reciprocal of the beat value corresponding to the second coordinate axis; Discrete sampling is performed along the boundary and interior of the feasible region of the two-dimensional joint elasticity space, and the rate deviation metric index value corresponding to each sampling point is calculated; Selecting the sampling point with the minimum rate deviation metric index value as the candidate beat combination point, searching the neighborhood of the candidate beat combination point, and determining the coordinate position that minimizes the rate deviation metric index value as the beat combination point; Recording the projection values of the beat combination point on the first coordinate axis and the second coordinate axis as the recommended production beat of the upstream process node and the downstream process node in the adjacent process node pair, respectively.

[0036] In the joint elasticity space of the adjacent process node pair, the search for the beat combination point needs to establish a clear spatial coordinate system and an optimization target. This search process maps the beat adjustable range of the upstream and downstream processes into the coordinate axes by constructing a two-dimensional space representation method, thereby converting the originally abstract beat matching problem into an optimization problem in the geometric space.

[0037] When constructing the two-dimensional joint elasticity space, the beat adjustable range defined by the beat elasticity coefficient of the upstream process node is taken as the first coordinate axis. Specifically, if the standard beat of the upstream process node is , and the elasticity coefficient is , then the value range of the first coordinate axis is . Any point on this axis represents the actual running beat that the upstream process can choose. Similarly, the adjustable range defined by the beat elasticity coefficient of the downstream process node constitutes the second coordinate axis. If the standard beat of the downstream process is , and the elasticity coefficient is , then the range of the second coordinate axis is . Through this coordinate mapping, any beat combination can be represented as a point on a two-dimensional plane, with the horizontal coordinate corresponding to the actual beat of the upstream process and the vertical coordinate corresponding to the actual beat of the downstream process. The entire two-dimensional joint elasticity space forms a rectangular region, the boundaries of which are defined by the beat elasticity ranges of the two processes.

[0038] A rate deviation metric is defined in the two-dimensional space to quantify the rate matching degree between upstream and downstream processes. Since the production pace and production rate are reciprocal, the shorter the pace, the more output per unit time. Therefore, the output rate of the upstream process can be represented as the inverse of its pace, and the receiving rate of the downstream process is also the inverse of its pace. The rate deviation metric is defined as the absolute value of the difference between the inverse of the pace value on the first coordinate axis and the inverse of the pace value on the second coordinate axis. Let the coordinates of a point in the two-dimensional space be , then the rate deviation metric corresponding to the point is calculated as . The physical meaning of the index is the difference between the output rate of the upstream process and the receiving rate of the downstream process. The smaller the index value, the closer the rates of the two processes, and the smoother the material transfer between processes. When the index value is zero, it means that the upstream and downstream processes are completely synchronized, and there is no work-in-process accumulation or waiting phenomenon.

[0039] To find the pace combination that minimizes the rate deviation metric in the two-dimensional joint elasticity space, a hierarchical search strategy is adopted. First, discrete sampling is performed along the feasible region of the two-dimensional space, and the sampling strategy takes into account both the boundary and the internal region. For a rectangular feasible region, m equally spaced sampling points are selected in the first coordinate axis direction, and n equally spaced sampling points are selected in the second coordinate axis direction, forming m x n grid points. The sampling point density needs to be determined according to the adjustable range of the pace and the accuracy requirements, usually making the distance between adjacent sampling points less than half of the desired accuracy. For the boundary region, since the process tends to run near the boundary of the elasticity range in actual production, dense sampling points are added on the four sides of the rectangle to ensure that the boundary solution is not missed. For each sampling point, its coordinate value is extracted and substituted into the calculation formula of the rate deviation metric to obtain the index value corresponding to the point and record it.

[0040] After obtaining the rate deviation metric values of all sampling points, a global comparison is performed to select several sampling points with the smallest index values as candidate pace combination points. Considering that discrete sampling may not accurately locate the global optimal solution, local fine search needs to be performed on the candidate points. For the candidate point with the smallest index value, its neighborhood range is defined, which is usually set to half of the distance between adjacent sampling points. In this neighborhood, more intensive sampling or gradient descent method is used for search. Since the rate deviation metric function is continuous and derivable in the feasible region, the partial derivatives of the function with respect to the two coordinate axes can be calculated. According to the gradient direction, iterative search is performed along the negative gradient direction from the candidate point, and the iteration step size is adjusted adaptively according to the current gradient modulus and the preset convergence threshold. The iteration continues until the index value changes less than the set threshold or the maximum number of iterations is reached.

[0041] The coordinate position determined by the neighborhood search to make the rate deviation metric indicator value minimum is the required beat combination point. This point represents the best rate matching state that can be achieved within the respective elastic range of the two processes in the geometric sense. The complete coordinate information of the beat combination point is recorded, and the projection value on the first coordinate axis corresponds to the recommended production beat of the upstream process node, and the projection value on the second coordinate axis corresponds to the recommended production beat of the downstream process node. The two projection values not only satisfy the beat elasticity constraints of the respective processes, but also make the rate deviation between the processes reach the minimum value within the feasible range.

[0042] In practical applications, if the upstream process standard beat is 45 seconds, the elasticity coefficient is 0.15, the downstream process standard beat is 50 seconds, and the elasticity coefficient is 0.1, then the upstream beat adjustable range is 38.25 seconds to 51.75 seconds, and the downstream beat adjustable range is 45 seconds to 55 seconds. The span of the two-dimensional space is 13.5 seconds in the horizontal axis and 10 seconds in the vertical axis. In this space, sampling is performed with a step size of 0.5 seconds, resulting in a total of 540 sampling points. After calculating the rate deviation indicator of each point, it is found that when the upstream beat is 47.5 seconds and the downstream beat is 47 seconds, the corresponding rate deviation is minimum, about 0.02 pieces per minute.

[0043] Generating an initial scheduling sequence according to the production order demand information and the beat balance constraint condition, comprising: Analyzing the production order demand information to extract the product type identifier, demand quantity and delivery time limit of each production order, and obtaining the process path information corresponding to each production order according to the product type identifier; Based on the process path information, each production order is decomposed into multiple process level sub-tasks, and a mapping relationship between each process level sub-task and the corresponding process node in the multi-process associated topological structure is established; According to the demand quantity and the target production beat range of each process node under the beat balance constraint condition, the estimated execution time interval of each process level sub-task is calculated; Taking the delivery time limit as the reverse calculation reference, the planned completion time of each process level sub-task is assigned in the reverse order direction of the process path information, and the planned completion time of each process level sub-task and the planned start time of the successor process level sub-task are constrained to satisfy the material flow connection condition; According to the estimated execution time interval of each process level sub-task, the planned start time is deduced from the planned completion time to form the task assignment time sequence and resource occupation period of each process level sub-task, and the assignment results of all process level sub-tasks are summarized to generate the initial scheduling sequence.

[0044] After obtaining the production order demand information and the constraint conditions of the beat balance, the order demand needs to be converted into an executable initial scheduling sequence. When analyzing the production order demand information, three core parameters are extracted for each production order: product type identifier, demand quantity, and delivery deadline. The product type identifier usually uses a unified coding system, such as the string in the form of "A01-MV250-X", which contains the product series, model, and variant code. The demand quantity is recorded as a positive integer, representing the number of products required by the order. The delivery deadline is represented by a timestamp or standard date and time format, indicating the deadline for completing the delivery of the order.

[0045] According to the extracted product type identifier, the corresponding process path information is retrieved from the process database. The process path information records all the processes and their sequence that the product needs to go through from raw materials to finished products. Each process is accompanied by standard process time quota, required equipment type, skill requirement, and other process parameters. For products with multiple alternative process paths, the main process path that is feasible and has the highest priority under current production conditions is selected as the reference.

[0046] Based on the process path information, each production order is decomposed into multiple process-level sub-tasks, and a mapping relationship between the process-level sub-tasks and the corresponding process nodes in the multi-process association topology structure is established. The specific method is as follows: match the process identifier of the process-level sub-task with the process identifier of each process node in the topology structure to determine which resource pool the sub-task should be executed in. For example, the "welding" process-level sub-task generated by order decomposition is mapped to the node identified as "welding process node" in the topology structure, which manages all welding equipment resources and welding worker teams.

[0047] When calculating the estimated execution time interval of each process-level sub-task, the demand quantity and the constraint conditions of the beat balance are considered. A reverse scheduling strategy is used to allocate the planned completion time for each process-level sub-task. The delivery deadline is used as the reverse reference benchmark, and each process-level sub-task is processed in reverse order along the process path. For the process-level sub-task at the end of the process path, its planned completion time is directly set to the delivery deadline to ensure that the product can be delivered on time after the process is completed. For process-level sub-tasks at intermediate positions in the process path, their planned completion times need to meet the material flow transfer connection conditions with the successor process-level sub-tasks.

[0048] The material flow coordination requirements stipulate that sufficient material flow time must be reserved between the planned completion time of the current process-level sub-task and the planned start time of its immediately following process-level sub-task. Material flow time includes the total time consumed for materials to be unloaded from the current process equipment, transported to the next process, and loaded onto the next process equipment. For processes that are close together and connected by conveyor belts or conveyor lines, material flow time may only be a few minutes; for processes located in different workshops and requiring forklifts or logistics carts for transfer, material flow time may reach tens of minutes or even several hours.

[0049] The planned start time is calculated by working backward from the planned completion time based on the estimated execution time range of each sub-task at each process level. In actual scheduling, to cope with production fluctuations and uncertainties, the start time can be planned appropriately in advance according to the buffer strategy, so as to reserve more time margin for subsequent execution.

[0050] Perform dynamic conflict detection on the initial sorting sequence, including: Construct a scheduling time axis and project the resource occupation time periods of all process-level subtasks in the initial scheduling sequence onto the scheduling time axis to form a time period occupation distribution map of each process node; In the multi-process associated topology, each pair of adjacent process nodes is traversed along the material transfer direction, and for each pair of adjacent process nodes, the occupancy time sequence of the upstream and downstream process nodes in the time occupancy distribution map is extracted. Perform time-series alignment analysis on the occupied time period sequence to identify whether there is a negative time difference between the end time of any occupied time period of the upstream process node and the start time of the corresponding occupied time period of the downstream process node. Mark the cases with negative time differences as resource occupied time period overlap conflicts. The positive time difference between the end time of the occupied period of the upstream process node and the start time of the corresponding occupied period of the downstream process node is calculated as the actual material waiting time. The actual material waiting time is compared with the allowable waiting time derived from the target production cycle range. Cases exceeding the allowable waiting time are marked as cycle limit exceedance conflict. The information on all process node pairs with overlapping resource usage periods and exceeding cycle time limits is aggregated to form the set of detection results for the scheduling conflict.

[0051] To realize the dynamic conflict detection of the initial scheduling sequence, a scheduling time axis structure is established first. The time axis takes the starting time of the current scheduling period as the origin and scales the time in minutes or seconds as the minimum time unit, covering the entire production planning period. The resource occupation information of all process-level sub-tasks in the initial scheduling sequence is mapped to the time axis one by one, and each process-level sub-task corresponds to a time interval segment. The start point of the interval is the time when the task starts to occupy resources, and the end point is the time when the resources are released. The distribution of these time interval segments on the time axis forms the time period occupation distribution atlas of each process node, which intuitively shows the resource usage state of different process nodes in the time dimension.

[0052] In the multi-process correlation topology structure, traversal operation is performed according to the actual flow direction of materials in the production process. Starting from the raw material input process, each pair of adjacent process nodes is visited in turn along the process route. For any adjacent process node pair, the roles of the upstream process node and the downstream process node are clearly defined. All occupation time periods of the upstream process node on the time axis are extracted from the time period occupation distribution atlas. These time periods record the time points when the process completes processing and outputs semi-finished products. At the same time, the occupation time period sequence of the downstream process node is extracted, which identifies the time points when the downstream process starts to receive materials and start processing. The extraction of the two occupation time period sequences should maintain the time sequence correspondence, that is, the pth batch output of the upstream process should correspond to the pth batch input of the downstream process.

[0053] For the extracted occupation time period sequence, time sequence alignment analysis is performed to detect resource occupation period overlap conflicts. Take the end time of an occupation time period of the upstream process node, which represents the time point when the upstream process completes the current batch processing and releases the equipment. Get the start time of the corresponding batch occupation time period of the downstream process node, which represents the time point when the downstream process starts to occupy the equipment to process the batch of materials. Calculate the time difference between the two time points. When the time difference is negative, it means that the start time of the downstream process is earlier than the completion time of the upstream process, that is, the downstream process has started running before the upstream process has output materials, which violates the physical constraints of material transfer. Mark all process node pairs that meet the constraint conditions as resource occupation period overlap conflicts, and record the specific time of conflict occurrence, the involved process node identifier, and the absolute value of the time difference as the quantitative indicator of conflict severity.

[0054] For the process node pairs without overlapping conflicts, further evaluate the beat matching. Calculate the positive time difference between the end time of the upstream process occupation period and the start time of the downstream process occupation period, which is the actual material waiting time, representing the length of time that the semi-finished product is stranded after the upstream process is completed before the downstream process begins. According to the target production beat range, derive the upper limit value of the allowed waiting time. If the target production beat is set to produce N products per unit time, theoretically the material transfer interval between adjacent processes should not exceed 1 / N time units. Considering the buffer requirement and equipment switching time in actual production, add a certain tolerance coefficient α to the theoretical beat to get the allowed waiting time , where α usually takes a value range of 0.1 to 0.3. Compare the actual material waiting time with the allowed waiting time, when the actual material waiting time is greater than the allowed waiting time, it indicates that the connection of this process node pair has a long waiting interval, which will lead to the decrease of overall production beat and the increase of work-in-process. Mark this situation as a beat overrun conflict, and record the overrun time, that is, the time difference between the actual material waiting time and the allowed waiting time as the measure of conflict intensity.

[0055] After completing the traversal and conflict detection of all adjacent process node pairs, summarize the analysis results. Establish a conflict record data structure, each record contains conflict type identification, upstream process node number, downstream process node number, time position of conflict occurrence, conflict quantification parameters, etc. For resource occupation period overlapping conflict, record the absolute value of negative time difference; for beat overrun conflict, record the overrun time value. Sort the records according to the severity of the conflict, the priority determination rule is that the resource occupation period overlapping conflict is higher than the beat overrun conflict, and the priority of the same type of conflict with larger time difference absolute value or overrun time is higher. Integrate all conflict records to form the detection result set of scheduling conflicts, which provides decision basis for subsequent conflict resolution strategy. The detection result set also contains statistical information, such as the total number of conflicts, the distribution proportion of each type of conflict, the process link where the conflict occurs, etc., which helps to analyze the weaknesses of the scheduling scheme. Through this systematic and dynamic conflict detection mechanism, potential resource allocation contradictions and beat mismatch problems can be identified before the execution of the scheduling scheme, providing accurate target guidance for optimization and adjustment, ensuring that the final scheduling scheme meets the process constraints and production efficiency requirements.

[0056] Perform timing alignment analysis on the occupation period sequence, including: For each occupation period in the occupation period sequence of the upstream process node, according to the material batch flow mapping relationship recorded in the multi-process correlation topology structure, determine the downstream process node occupation period that the processed material batch will flow to, establish the upstream and downstream occupation period pairing relationship; For each upstream-downstream occupation period pair, the end time of the upstream occupation period and the start time of the downstream occupation period are extracted, and the time difference between the two times is calculated as the time interval; According to the positive and negative properties of the time interval, the classification decision is made, and the pair relationship with negative time interval is classified into the overlap conflict candidate set, and the pair relationship with positive time interval is classified into the waiting time evaluation set; The time interval of each pair relationship in the waiting time evaluation set and the allowed waiting time of the adjacent process node pair under the beat balance constraint condition are compared one by one, and the pair relationship whose time interval exceeds the allowed waiting time is transferred into the beat overrun conflict candidate set; The overlap conflict candidate set and the beat overrun conflict candidate set are merged, and the time alignment analysis result of the adjacent process node pair is output.

[0057] After completing the time windowing decomposition of the occupation period sequence, the obtained occupation period sequence needs to be subjected to time alignment analysis to identify the time conflicts and beat imbalance problems existing in the production process. Based on the material batch flow mapping relationship recorded in the multi-process correlation topology structure, the processing time relationship of the same material batch between adjacent process nodes is tracked to realize quantitative evaluation of the production time sequence rationality.

[0058] For the occupation period sequence of the upstream process node, each occupation period in it is traversed one by one, and the downstream flow information of the material batch is queried by using the material batch flow mapping relationship maintained in the multi-process correlation topology structure. The material batch flow mapping relationship usually takes batch identification as the key value, and records the next process node to which the batch flows from the current process node and the corresponding occupation period identification. Through the mapping relationship, the processing occupation period of the material batch in the downstream process node can be accurately located. In this way, for the upstream occupation period and the corresponding downstream occupation period, a one-to-one upstream-downstream occupation period pair relationship is established. The pair relationship not only records the time boundaries of the two periods, but also contains context information such as material batch identification and process node identification, providing data basis for subsequent time sequence consistency inspection.

[0059] After establishing the pairing relationship, for each pair of upstream and downstream occupancy time period pairing relationship, the key time nodes are extracted for time interval calculation. Specifically, the end time is extracted from the upstream occupancy time period, and the start time is extracted from the downstream occupancy time period. The time difference between the two times is calculated to obtain the time interval. This time interval reflects the time span of the material batch after the upstream process is completed to before it starts processing in the downstream process. When the time interval is positive, it means that there is a waiting time between the two processes. When the time interval is negative, it means that the start time of the downstream process is earlier than the end time of the upstream process, and there is a time overlap conflict.

[0060] Based on the positive and negative properties of the time interval, all pairing relationships are classified and determined. All pairing relationships are traversed to check the sign of the time interval. If the time interval is less than 0, it means that there is a time overlap in the pairing relationship, which is classified into the overlap conflict candidate set. The pairing relationship in the overlap conflict candidate set violates the basic time causal logic, i.e. the downstream process cannot start processing the same material batch before the upstream process is completed. Such conflicts are usually caused by scheduling errors, timestamp recording deviations or abnormal process flow data, and need to be focused on and corrected. If the time interval is equal to 0, the pairing relationship does not have a time overlap problem, and it is classified into the waiting time evaluation set. Although the pairing relationship in the waiting time evaluation set is logically correct in time, it still needs to be further tested whether the waiting time meets the beat balance constraint condition.

[0061] For the pairing relationships in the waiting time evaluation set, they need to be deeply evaluated in combination with the beat balance constraint condition. The beat balance constraint condition usually specifies the maximum allowed waiting time for a specific pair of adjacent process nodes. The allowed waiting time is determined comprehensively according to process requirements, material characteristics, equipment layout, etc., aiming to ensure the continuity and efficiency of the production process. The time interval of each pairing relationship in the waiting time evaluation set is extracted one by one and compared with the allowed waiting time corresponding to the pair of adjacent process nodes. If it is greater than the allowed waiting time, it is determined that the waiting time of the pairing relationship exceeds the beat balance constraint condition, and it is transferred from the waiting time evaluation set to the beat overrun conflict candidate set. Although the pairing relationship in the beat overrun conflict candidate set does not violate the time causal logic, its long waiting time will cause the imbalance of the production beat, affect the overall production efficiency, and may cause problems such as work-in-process backlog and equipment utilization decline.

[0062] After completing the above classification and screening, the overlapping conflict candidate set and the cycle time limit exceeding conflict candidate set are merged. The merging process is implemented through set union operation, generating a comprehensive conflict candidate set containing all timing conflicts and cycle time limit exceeding issues. Each pair in this comprehensive conflict candidate set is labeled with a conflict type, facilitating differentiated processing strategies for different conflict types. For example, overlapping conflicts need to be resolved by adjusting the time boundaries of the occupied time period or correcting the material batch flow mapping relationship, while cycle time limit exceeding conflicts need to be improved by optimizing the material transfer timing between processes or adjusting the equipment processing rate. The merged comprehensive conflict candidate set is output as the timing alignment analysis result of the adjacent process node pair for use by the subsequent conflict resolution and scheduling optimization modules.

[0063] Through the aforementioned time-series alignment analysis process, time-related logical conflicts and cycle time balance issues in the production process can be systematically identified, providing a quantitative basis for optimizing multi-process collaborative scheduling. This analysis method not only considers the rationality of temporal causal relationships but also incorporates the process constraints of production cycle time balance, ensuring that the generated time-series alignment analysis results conform to both physical laws and production efficiency requirements. Furthermore, by refining conflict types into overlapping conflicts and cycle time limit exceedance conflicts, clear classification guidance is provided for subsequent conflict resolution strategies, enhancing the intelligence level and practical application value of the production scheduling system.

[0064] A second aspect of this invention provides an Internet of Things (IoT) based cable production management system, comprising: The data acquisition unit is used to acquire IoT sensor data of each process node in the cable manufacturing production line, and construct a multi-process association topology based on the IoT sensor data. The multi-process association topology represents the material transfer relationship and capacity coupling constraint between each process node. The cycle time balancing unit is used to establish cycle time balancing constraints between processes based on the multi-process associated topology. The cycle time balancing constraints determine the target production cycle time range of each process node by calculating the capacity matching degree between adjacent process nodes, so that the output rate of the upstream process node and the receiving rate of the downstream process node are maintained within a preset coordination threshold. The initial scheduling unit is used to receive production order demand information and generate an initial scheduling sequence based on the production order demand information and the cycle time balance constraint. The initial scheduling sequence includes the task allocation sequence and resource occupation period of each process node. A dynamic conflict unit is configured to perform dynamic conflict detection on the initial scheduling sequence, identify scheduling conflicts in task assignment timing caused by overlapping resource occupation periods or material waiting time exceeding the target production rhythm range by traversing process node pairs with material transfer relationships in the multi-process association topology, and adjust task timing of process nodes with scheduling conflicts according to the production capacity coupling constraint to generate an optimized scheduling sequence, so that each production device performs production work according to the optimized scheduling sequence.

[0065] In a third aspect, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the method described above.

[0066] In a fourth aspect, a computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0067] The present application can be a method, apparatus, system and / or computer program product. The computer program product can include a computer-readable storage medium having stored thereon computer-readable program instructions that, when executed by a computer, cause the computer to carry out various aspects of the present application.

[0068] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A cable production management method based on the Internet of Things, characterized in that, include: Acquire IoT sensor data of each process node in the cable manufacturing production line, and construct a multi-process association topology based on the IoT sensor data. The multi-process association topology represents the material transfer relationship and capacity coupling constraint between each process node. Based on the multi-process associated topology, inter-process cycle time balance constraints are established. The cycle time balance constraints determine the target production cycle time range of each process node by calculating the capacity matching degree between adjacent process nodes, so that the output rate of the upstream process node and the receiving rate of the downstream process node are maintained within a preset coordination threshold. Receive production order demand information, and generate an initial schedule based on the production order demand information and the cycle time balance constraint. The initial schedule includes the task allocation sequence and resource occupation period of each process node. Dynamic conflict detection is performed on the initial scheduling sequence. The dynamic conflict detection identifies scheduling conflicts caused by overlapping resource occupation periods or material waiting time exceeding the target production cycle range by traversing the process node pairs with material transfer relationships in the multi-process associated topology. Based on the capacity coupling constraints, the task sequence of the process nodes with scheduling conflicts is adjusted to generate an optimized scheduling sequence, and each production equipment is controlled to perform production operations according to the optimized scheduling sequence.

2. The method according to claim 1, characterized in that, Based on the aforementioned multi-process associated topology, inter-process cycle time balance constraints are established, including: The historical processing cycle data sequence of each process node is extracted from the multi-process associated topology, and the processing cycle fluctuation range of each process node is obtained by statistical analysis of the historical processing cycle data sequence. The cycle elasticity coefficient of each process node is calculated based on the processing cycle fluctuation range. The cycle elasticity coefficient represents the adjustable range of the production cycle of the process node within the fluctuation range. For each pair of adjacent process nodes with material transfer relationship, a joint elasticity space is constructed based on the respective cycle elasticity coefficients of the upstream and downstream process nodes. Within the joint elasticity space, a cycle combination point that minimizes the deviation between the output rate of the upstream process node and the receiving rate of the downstream process node is searched. Centered on the beat combination point, and combined with the preset coordination threshold, the coordinated beat domain of the adjacent process node pair is formed by expanding outward. The target production beat range of each process node is constrained to the intersection area of ​​all the coordinated beat domains it participates in. The constraint relationship that the target production cycle range of all process nodes and the capacity matching degree of adjacent process node pairs must fall within the preset coordination threshold is encapsulated as the inter-process cycle balance constraint condition.

3. The method according to claim 2, characterized in that, Searching within the joint elastic space for a clock combination that minimizes the deviation between the output rate of the upstream process node and the receiving rate of the downstream process node includes: The adjustable range of the cycle time defined by the cycle time elasticity coefficient of the upstream process node is used as the first coordinate axis, and the adjustable range of the cycle time defined by the cycle time elasticity coefficient of the downstream process node is used as the second coordinate axis to construct a two-dimensional joint elastic space. A rate deviation metric is defined within the two-dimensional joint elastic space. The rate deviation metric is the absolute value of the difference between the reciprocal of the beat value corresponding to the first coordinate axis and the reciprocal of the beat value corresponding to the second coordinate axis. Discrete sampling is performed along the feasible region boundary and interior of the two-dimensional joint elastic space, and the rate deviation metric value corresponding to each sampling point is calculated. The sampling point with the smallest rate deviation metric value is selected as the candidate beat combination point. The neighborhood of the candidate beat combination point is searched to determine the coordinate position that minimizes the rate deviation metric value as the beat combination point. The projection values ​​of the cycle combination points on the first and second coordinate axes are recorded and used as the recommended production cycles for the upstream and downstream process nodes in the adjacent process node pair, respectively.

4. The method according to claim 1, characterized in that, An initial scheduling sequence is generated based on the production order demand information and the cycle time balance constraints, including: The production order demand information is parsed to extract the product type identifier, required quantity and delivery time limit for each production order, and the process path information corresponding to each production order is obtained based on the product type identifier; Based on the process path information, each production order is decomposed into multiple process-level sub-tasks, and a mapping relationship is established between each process-level sub-task and the corresponding process node in the multi-process associated topology. Based on the required quantity and the target production cycle time range of each process node under the cycle time balance constraint, calculate the estimated execution time range of each process-level sub-task; Using the delivery time limit as the reverse calculation benchmark, the planned completion time is assigned to each process-level sub-task in reverse order along the process path information, and the planned completion time of each process-level sub-task and the planned start time of its subsequent process-level sub-task are constrained to meet the material flow connection conditions. Based on the estimated execution time range of each process-level subtask, the planned start time is deduced from the planned completion time to form the task allocation sequence and resource occupation period of each process-level subtask. The allocation results of all process-level subtasks are summarized to generate the initial scheduling sequence.

5. The method according to claim 1, characterized in that, Perform dynamic conflict detection on the initial sorting sequence, including: Construct a scheduling time axis and project the resource occupation time periods of all process-level subtasks in the initial scheduling sequence onto the scheduling time axis to form a time period occupation distribution map of each process node; In the multi-process associated topology, each pair of adjacent process nodes is traversed along the material transfer direction, and for each pair of adjacent process nodes, the occupancy time sequence of the upstream and downstream process nodes in the time occupancy distribution map is extracted. Perform time-series alignment analysis on the occupied time period sequence to identify whether there is a negative time difference between the end time of any occupied time period of the upstream process node and the start time of the corresponding occupied time period of the downstream process node. Mark the cases with negative time differences as resource occupied time period overlap conflicts. The positive time difference between the end time of the occupied period of the upstream process node and the start time of the corresponding occupied period of the downstream process node is calculated as the actual material waiting time. The actual material waiting time is compared with the allowable waiting time derived from the target production cycle range. Cases exceeding the allowable waiting time are marked as cycle limit exceedance conflict. The information on all process node pairs with overlapping resource usage periods and exceeding cycle time limits is aggregated to form the set of detection results for the scheduling conflict.

6. The method according to claim 5, characterized in that, Perform time-series alignment analysis on the occupied time period sequence, including: For each occupied time period in the occupied time period sequence of the upstream process node, the downstream process node occupied time period to which the processed material batch will flow is determined according to the material batch flow mapping relationship recorded in the multi-process association topology, and an upstream and downstream occupied time period pairing relationship is established. For each upstream and downstream occupation time period pairing, the end time of the upstream occupation time period and the start time of the downstream occupation time period are extracted, and the time difference between the two is calculated as the time interval. The pairings with negative time intervals are classified and determined according to their positive or negative attributes. Pairings with negative time intervals are assigned to the overlapping conflict candidate set, while pairings with positive time intervals are assigned to the waiting time evaluation set. The timing interval of each pairing relationship in the waiting time evaluation set is compared with the allowable waiting time of the adjacent process node pair under the cycle balance constraint. Pairing relationships with timing intervals exceeding the allowable waiting time are transferred to the cycle over-limit conflict candidate set. The overlapping conflict candidate set and the beat over-limit conflict candidate set are merged, and the result is output as the timing alignment analysis result of the adjacent process node pair.

7. An Internet of Things-based cable production management system, used to implement the method as described in any one of claims 1-6, characterized in that, include: The data acquisition unit is used to acquire IoT sensor data of each process node in the cable manufacturing production line, and construct a multi-process association topology based on the IoT sensor data. The multi-process association topology represents the material transfer relationship and capacity coupling constraint between each process node. The cycle time balancing unit is used to establish cycle time balancing constraints between processes based on the multi-process associated topology. The cycle time balancing constraints determine the target production cycle time range of each process node by calculating the capacity matching degree between adjacent process nodes, so that the output rate of the upstream process node and the receiving rate of the downstream process node are maintained within a preset coordination threshold. The initial scheduling unit is used to receive production order demand information and generate an initial scheduling sequence based on the production order demand information and the cycle time balance constraint. The initial scheduling sequence includes the task allocation sequence and resource occupation period of each process node. The dynamic conflict unit is used to perform dynamic conflict detection on the initial scheduling sequence. The dynamic conflict detection identifies scheduling conflicts caused by overlapping resource occupation periods or material waiting time exceeding the target production cycle range by traversing the process node pairs with material transfer relationships in the multi-process associated topology. Based on the capacity coupling constraints, the task sequence of the process nodes with scheduling conflicts is adjusted to generate an optimized scheduling sequence, and each production equipment is controlled to perform production operations according to the optimized scheduling sequence.

8. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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