A method for automatically generating transport paths based on MCS system data modeling

Abstract

This invention discloses a method for automatically generating transport paths based on MCS system data modeling, belonging to the field of path generation technology. It involves acquiring the current transport path map generated by the material control system and obtaining its global structural information; calculating the corresponding path map structural complexity index based on multiple topological dimension features in the path map, including path redundancy and local region map density; comparing the path map structural complexity index with a preset structural complexity threshold; if the complexity index is not less than the preset structural complexity threshold, triggering path map structural compression processing to generate a new transport path map. This method can measure and address the global topological complexity problem of the current transport path map generated by the material control system, enabling automated path construction to achieve high initial efficiency while also supporting long-term stability and maintainability under large-scale system operation.

Description

Technical Field

[0001] This invention relates to the field of path generation technology, and more specifically to a method for automatically generating transport paths based on MCS system data modeling. Background Technology

[0002] Against the backdrop of the accelerating evolution of intelligent manufacturing and industrial logistics automation, Material Control Systems (MCS), as a key platform connecting upper-level production planning and lower-level logistics execution, undertake core functions such as plant-wide material handling path planning, equipment scheduling, and material flow optimization. Their operational efficiency and flexibility directly affect the rhythm and capacity of the entire production system. Currently, mainstream MCS systems generally adopt a manual preset mode for path map construction. Operators, based on experience, set equipment nodes and their path relationships one by one in the system interface, and then manually configure and generate the entire material handling topology map to support subsequent path planning and scheduling decisions. However, with the continuous growth in the number of factory equipment, the increasing complexity of production line layouts, and the growing demand for flexible production, the traditional manual path map generation method faces serious adaptability and efficiency bottlenecks. Not only is the workflow cumbersome and the deployment cycle long, but it is also prone to human errors such as incorrect path connections and missing nodes, leading to frequent problems such as material handling task failures and system scheduling chaos. Furthermore, due to the lack of adaptability to equipment changes in manual methods, the system often requires a complete shutdown and reconfiguration of the path graph once equipment is added, removed, or relocated, severely restricting the dynamic response capability and overall operational flexibility of the manufacturing system. To address these issues, some solutions in recent years have attempted to introduce an automatic path graph generation mechanism driven by equipment data. This mechanism automatically constructs transport nodes using AMHS equipment and port information and automatically generates edge sets based on the logical relationships between nodes, thereby constructing a weighted path graph that can be used for algorithms such as Dijkstra's algorithm or A*. This method has significant advantages in reducing labor costs and improving deployment efficiency, but it has also exposed some unaddressed systemic defects during the evolution of the path graph structure.

[0003] Although such automatic graph building mechanisms have achieved a high degree of intelligence in path generation and dynamic updates, without global control of the path graph structure, they may still lead to uncontrolled complexity phenomena such as redundant expansion of the graph structure and abnormal local density as the scale of equipment expands. This can result in hidden risks such as a sharp decline in the efficiency of the path algorithm, increased fluctuations in scheduling results, and a significant increase in the difficulty of system maintenance. Existing solutions generally neglect the measurement and response mechanisms for such global topological complexity problems and lack necessary graph structure compression, path aggregation, or redundancy suppression methods. As a result, although automated path construction has high efficiency in the early stages, it is difficult to support the stability and maintainability of large-scale system operation in the long term. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned above and provide a method for automatically generating transport paths based on MCS system data modeling.

[0005] In a first aspect of this invention, a method for automatically generating transport paths based on MCS system data modeling is first proposed, the method comprising:

[0006] S1: Obtain the current conveying path diagram generated based on the material control system, and obtain the global structure information of the path diagram;

[0007] S2: Calculate the corresponding path graph structure complexity index based on multiple topological dimension features in the path graph. The topological dimension features include path redundancy and local region graph density.

[0008] S3: Compare the path graph structure complexity index with the preset structure complexity threshold for judgment;

[0009] S4: If the complexity index is not less than the preset structural complexity threshold, then trigger the path graph structure compression process to generate a new transport path graph.

[0010] Optionally, the steps for calculating the corresponding path graph structural complexity index based on multiple topological dimension features in the path graph are as follows:

[0011] The path redundancy index and the local graph density index are included in the path redundancy index and the local graph density index. The path redundancy index and the local graph density index are normalized and then added together to obtain the path graph structure complexity index.

[0012] The steps for calculating the path redundancy index are as follows:

[0013] Extract all unique start-end pairs from the path graph, and denote them as a set. , Indicates the first For the starting point of the path, Indicates the corresponding endpoint, for each pair List all feasible paths and record the total cost of each path, denoted as . in From arrive The number of paths, For the first The total cost of the path;

[0014] For each set of costs Perform the following operations: Find the maximum and minimum path costs. And determine the cost is close to the range. Count the number of paths that fall within this interval. Calculate the redundancy overlap rate : .

[0015] Optionally, the calculation steps of the path redundancy index further include:

[0016] Cost set Sort by size from smallest to largest to obtain the sorted set.

[0017] Calculate sorted sets Normalized difference sequence of adjacent path costs : Statistically normalized difference series The cost convergence rate is obtained by dividing the proportion of differences less than a preset threshold by the total number of differences in the normalized difference sequence. ;

[0018] Combining redundancy overlap rate With cost convergence rate Calculate the redundancy index of a single path pair. , Redundancy index for all path pairs The path redundancy index of the entire path graph is obtained by performing a normalized geometric mean.

[0019] Optionally, the calculation steps for the local graph density index are as follows:

[0020] Let all nodes in the path graph form a set. ,in Represent the total number of nodes in the graph, and construct the first-order adjacency moments of the nodes. Defined as: ;

[0021] Set the maximum number of jumps Constructing a multi-order reachability matrix based on the adjacency matrix Used to indicate that a node is in Connectivity under step jumps;

[0022] For each node Calculate its value in all 1 to The sum of reachable strengths within a step jump is denoted as: In the formula, Indicates the number of jumps. Below, from arrive The number of connected paths; express exist Total connection strength within a step jump.

[0023] Optionally, the calculation step of the local graph density index further includes:

[0024] Take all The maximum value in is And calculate the normalized connection density score for each node. : ;

[0025] Calculate the density score difference between adjacent nodes for all nodes (starting from the second one). , , Construct the jump rate for each node. , ;in It is a very small constant used to avoid division by zero errors;

[0026] Construct second-order perturbation values ​​for all jump rates (starting from the 3rd one). , , ; All Normalization, the maximum value is denoted as And calculate the disturbance score. , ;

[0027] All perturbation scores The mean value is used as the local graph density index.

[0028] Optionally, the comparison between the path graph structure complexity index and a preset structure complexity threshold includes:

[0029] If the complexity index of the path graph structure is less than the preset complexity threshold, there is no need to regenerate the path graph; the current path graph can be used for transfer.

[0030] If the complexity index of the path graph structure is not less than the preset structure complexity threshold, then the path graph structure compression process is triggered to generate a new transport path graph.

[0031] Optionally, if the complexity index is not less than a preset structural complexity threshold, then the path graph structure compression process is triggered, and the steps to generate a new transport path graph are as follows:

[0032] In the path graph, find multiple path segments with the same start and end points and whose path costs differ within a set tolerance range, and merge them into a main path. The main path is retained as a scheduling priority path, and the remaining paths are marked as alternative paths and deleted to reduce the complexity of the graph structure.

[0033] The beneficial effects of this invention are:

[0034] This invention proposes a method for automatically generating transport paths based on MCS system data modeling. It acquires the current transport path map generated by the material control system and obtains its global structural information. Based on multiple topological dimension features in the path map, it calculates the corresponding path map structural complexity index, including path redundancy and local region map density. The path map structural complexity index is compared with a preset structural complexity threshold. If the complexity index is not less than the preset threshold, path map structural compression is triggered, generating a new transport path map. This method can measure and address the global topological complexity issue of the current transport path map generated by the material control system, enabling automated path construction to achieve high initial efficiency while maintaining stability and maintainability under long-term large-scale system operation. Attached Figure Description

[0035] The invention will now be further described with reference to the accompanying drawings.

[0036] Figure 1 This is a flowchart of a method for automatically generating transport paths based on data modeling of an MCS system. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] This invention provides a method for automatically generating transport paths based on MCS system data modeling. See also... Figure 1 , Figure 1 This is a flowchart illustrating a method for automatically generating transport paths based on MCS system data modeling, provided as an embodiment of the present invention. The method includes the following steps:

[0039] S1: Obtain the current conveying path diagram generated based on the material control system, and obtain the global structure information of the path diagram;

[0040] S2: Calculate the corresponding path graph structure complexity index based on multiple topological dimension features in the path graph. The topological dimension features include path redundancy and local region graph density.

[0041] S3: Compare the path graph structure complexity index with the preset structure complexity threshold for judgment;

[0042] S4: If the complexity index is not less than the preset structural complexity threshold, then trigger the path graph structure compression process to generate a new transport path graph.

[0043] Based on the embodiments of the present invention, an automatic generation method for conveying paths based on MCS system data modeling is provided. In this way, the complexity of the global topology of the current conveying path diagram generated by the material control system can be measured and addressed, so that the automated path construction can not only have efficient initial performance, but also support the stability and maintainability of large-scale system operation in the long term.

[0044] In one embodiment, a method for automatically generating transport paths based on MCS system data modeling includes the following steps:

[0045] S1: Obtain the current conveying path diagram generated based on the material control system, and obtain the global structure information of the path diagram;

[0046] In step S1, obtaining the current transport path diagram generated by the material control system refers to the directed graph structure automatically generated by the MCS system after modeling the equipment, transport units, and their connections within the plant. This graph contains all equipment nodes and their path connections. This path diagram is typically represented in graph data structure form, where nodes represent various types of equipment or port interfaces, and edges represent the actual transport paths between devices. Edges may also include initial weight information such as transport distance, time, or transmission cost. Obtaining the global structural information of the path diagram involves structurally extracting and summarizing the topological characteristics of the entire graph without altering its structure. This includes, but is not limited to, extracting the distribution of start and end points of all path segments, the number and connection density of edges, the reachability relationships between nodes, the maximum and minimum depths of paths, and the partitioning of strongly connected subgraphs, to reflect the overall complexity and structural characteristics of the path diagram. For example, if the system detects a large number of different path segments in the graph that move devices from device A to device B, and their costs (such as transport time) are extremely similar, it may indicate structural redundancy in the graph. Similarly, if devices in one area are densely interconnected while another part of the graph is sparsely structured, it indicates uneven density distribution in the local graph. Furthermore, if some devices require 10 hops to reach each other, while others only require one hop, it indicates a highly uneven path depth. This information forms the basis for calculating the "path graph structural complexity index" and is a prerequisite for determining whether the path graph is too complex and requires structural compression.

[0047] S2: Based on multiple topological dimension features in the path graph, calculate the corresponding path graph structure complexity index. Topological dimension features include path redundancy, local region graph density, path depth distribution, and graph structure connectivity.

[0048] In one implementation, the steps for calculating the corresponding path graph structural complexity index based on multiple topological dimension features in the path graph are as follows:

[0049] The path redundancy index and the local graph density index are included in the path redundancy index and the local graph density index. The path redundancy index and the local graph density index are normalized and then added together to obtain the path graph structure complexity index.

[0050] It should be noted that the above-mentioned normalization methods for removing dimensions include Min-Max normalization, Z-Score standardization, etc., which will not be elaborated here.

[0051] The steps for calculating the path redundancy index are as follows:

[0052] Extract all unique start-end pairs from the path graph, and denote them as a set. , Indicates the first For the starting point of the path, Indicates the corresponding endpoint, for each pair List all feasible paths and record the total cost of each path, denoted as . in From arrive The number of paths, For the first The total cost of the path;

[0053] For each set of costs Perform the following operations: Find the maximum and minimum path costs. And determine the cost is close to the range. Count the number of paths that fall within this interval. Calculate the redundancy overlap rate : .

[0054] Cost set Sort by size from smallest to largest to obtain the sorted set.

[0055] Calculate sorted sets Normalized difference sequence of adjacent path costs : Statistically normalized difference series The cost convergence rate is obtained by dividing the proportion of differences less than a preset threshold by the total number of differences in the normalized difference sequence. ;

[0056] Combining redundancy overlap rate With cost convergence rate Calculate the redundancy index of a single path pair. , Redundancy index for all path pairs The path redundancy index of the entire path graph is obtained by performing a normalized geometric mean.

[0057] It should be noted that all data involved in the above path redundancy index calculation comes from the material control system's real-time recording and management of transport task data and system topology data during operation. Specifically, the starting point, ending point, and all feasible paths in between in the path diagram are automatically generated by the MCS system based on the on-site equipment layout and transport logic. Each node corresponds to an actual material handling device or its port, and each path corresponds to an executable transport channel. When the system executes task scheduling, it records the transport cost information of each path in real time. This cost can be determined by path length, transmission time, and transport... Parameters such as the number of times, equipment switching delay, and energy consumption are calculated or directly obtained from statistics by the system monitoring module, thus forming the total cost set of all feasible paths between each pair of origin and destination. The maximum and minimum values ​​can be obtained directly by retrieving these total cost values. The number of paths falling into close intervals is obtained by counting the number of paths whose cost differences are within a set tolerance range. The sorted set and the sequence of adjacent path differences are automatically calculated and generated by the system in the data analysis module. The cost convergence rate is obtained from the proportional statistical results of the path cost differences. Finally, the normalized geometric mean process of all redundancy indices is completed by the data aggregation module of the MCS system. In other words, all data comes from the system's internal operation records, topology, and historical data of transport tasks. The calculation process requires no manual intervention or external data import, ensuring the real-time and objectivity of the indicator calculation.

[0058] It's important to note that the path redundancy index measures whether there are numerous feasible paths with similar functions but similar costs between the same starting and ending points in a transport path graph. Its core purpose is to reflect whether structural redundancy has arisen within the path graph under the premise that the task objective remains unchanged; that is, whether the diversity of paths has exceeded the effective utilization range of the scheduling strategy. A higher path redundancy index indicates that many path segments in the system have no significant difference in actual cost, belonging to a "low-difference, high-overlap" redundant structure. If this redundancy is not controlled, it will grow exponentially with the continuous expansion of the number of devices, eventually leading to an abnormally complex and bloated path graph structure, severely weakening the operating efficiency of path algorithms. For example, there should only be 1-2 optimal paths from device A to device B, but due to the lack of constraints in the system's automatic graph building mechanism, more than 10 slightly different path segments may be generated. The cost difference between these paths may be within 3%, causing path algorithms (such as Dijkstra's or A*) to repeatedly judge and sort among multiple approximate paths when performing shortest path calculations, greatly increasing algorithm complexity and consequently increasing the random volatility of scheduling decisions. Furthermore, when the differences between paths are not significant, the system struggles to establish stable strategy preferences during load balancing or path selection, leading to path oscillations, task congestion, or frequent scheduling switches, thus impacting overall transport efficiency and system stability. Moreover, when troubleshooting abnormal paths or maintaining and optimizing the graph structure, the large number and high similarity of paths between the same origin and destination make it difficult for engineers to quickly locate critical paths, significantly increasing system maintenance costs. Therefore, the path redundancy index not only reveals the degree of redundancy in the path graph but is also a key indicator for judging whether the graph structure has "structural uncontrollable risks." Especially in large-scale equipment environments, a higher value warrants greater attention to system structural complexity management.

[0059] It should be noted that the reason for using the above method to calculate the path redundancy index, instead of the conventional methods such as path number statistics, average cost difference calculation, or weighted summation, is mainly because this method constructs a more discriminative and structurally sensitive redundancy assessment logic from two dimensions: the overlap density of path costs and the convergence trend of the cost sequence. This can effectively capture the implicit redundancy feature of "functional duplication but lack of cost differentiation" in the path graph. Its greatest advantages are as follows: First, this method does not rely on subjectively set weights or manual classification standards, but analyzes the cost distribution objectively formed within the path graph, ensuring the results are consistent and transferable. Second, it jointly evaluates the substantial differences between paths by using the "local concentration rate" (i.e., redundancy overlap rate) of cost approximation paths and the "small jump frequency" (i.e., cost convergence rate) of global path differences. This can identify highly redundant areas that, although the paths are different in form, tend to be consistent in cost, thus avoiding misjudging "useful diversity" as redundancy and avoiding missing "seemingly different but actually repetitive" paths. Third, this method does not involve weighted summation, avoiding distortion problems caused by improper weight selection. The formula structure used is symmetrical, the variable meanings are clear, and it has strong mathematical interpretability and algorithmic feasibility. Finally, this method designs operations such as "normalization, sorting, difference, and interval projection" in each layer of calculation, giving it good scale invariance and graph structure adaptability. That is, regardless of the size of the equipment or the total number of paths, the result always falls within the 0~1 range, making it easy to use in conjunction with other structural indices. In summary, this method not only improves the accuracy and robustness of path redundancy detection, but also effectively supports subsequent structure compression, path aggregation, and scheduling strategy optimization. It is a high-value computation method for managing system structural complexity.

[0060] The calculation steps for the local graph density index are as follows:

[0061] Let all nodes in the path graph form a set. ,in Represent the total number of nodes in the graph, and construct the first-order adjacency moments of the nodes. Defined as: ;

[0062] Set the maximum number of jumps Constructing a multi-order reachability matrix based on the adjacency matrix Used to indicate that a node is in Connectivity under step jumps;

[0063] For each node Calculate its value in all 1 to The sum of reachable strengths within a step jump is denoted as: In the formula, Indicates the number of jumps. Below, from arrive The number of connected paths; express exist Total connection strength within a step jump.

[0064] Take all The maximum value in is And calculate the normalized connection density score for each node. : ;

[0065] Calculate the density score difference between adjacent nodes for all nodes (starting from the second one). , , Construct the jump rate for each node. , ;in It is a very small constant used to avoid division by zero errors;

[0066] Construct second-order perturbation values ​​for all jump rates (starting from the 3rd one). , , ; All Normalization, the maximum value is denoted as And calculate the disturbance score. , ;

[0067] All perturbation scores The mean value is used as the local graph density index.

[0068] It should be noted that all data involved in the calculation of the local graph density index above comes from the graph structure information and node connection relationship information maintained by the Material Control System (MCS) during the automatic path graph construction and task scheduling process. Specifically, the set of nodes in the path graph can be automatically extracted from entity units such as equipment, workstations, and conveying ports registered by the system. Each node represents an actual transportable location. The connection relationship between nodes is determined by the first-order adjacency relationship generated by the system during the graph construction phase based on the physical layout and conveying rules, forming an adjacency matrix. This matrix is ​​maintained in the system in the form of a sparse matrix or edge set for a long time. Based on this, the system can construct hop reachability matrices of arbitrary order through matrix exponentiation. The hop order is automatically controlled by set parameters and is used to describe the connectivity of nodes within several hop paths. The reachability strength of each node is the result of row-wise summation of these hop reachability matrices, which is directly calculated by the graph processing module of the system. Intermediate computational data such as density scores, jump rates, and second-order perturbations can all be generated in real time based on the original graph structure using the graph algorithm engine. This process does not rely on any historical task data or external information; it is entirely derived from the structure of the current path graph, ensuring the repeatability and self-consistency of the computation process. This data source method guarantees that the generation of local graph density indices is both efficient and possesses the objectivity and universality of the graph structure itself.

[0069] It's important to note that the local graph density index is a quantitative indicator used to measure whether there is excessive structural connectivity and extremely uneven density distribution in different node regions within a multi-hop connectivity range in a path graph. Its core function is to identify "graph structure stacking" phenomena in local regions of the graph structure caused by overly tight connections between nodes and excessive redundancy of hop paths, thereby determining whether the system has experienced topological imbalance even with good surface connectivity. A higher local graph density index indicates the presence of more and more "high-density local clusters" in the path graph—that is, there are a large number of short hop paths, highly overlapping nearest neighbor paths, or redundant interconnections between nodes within certain regions. If these structures are not addressed, they will rapidly amplify and accumulate during the scaling up of the equipment, causing the overall complexity of the graph structure to spiral out of control "from point to surface," resulting in a significant decrease in the efficiency of the path algorithm. For example, if 10 devices in a certain area form a dense network that is reachable within order 3, while other areas remain sparsely connected, the path planning algorithm will need to handle a large number of comparisons, sorting, and path weight evaluations of skip paths when performing calculations in high-density areas, resulting in an exponential increase in computational overhead. Simultaneously, due to the numerous available paths in such high-density areas, scheduling is prone to "random looping" or "selective oscillation," causing the system to choose different paths each time it faces the same transport task. This leads to unstable scheduling results, unpredictable scheduling logs, and failure of resource scheduling priorities, severely impacting production rhythm and transport reliability. Furthermore, when multiple such high-density areas appear in the graph structure, engineers will find it difficult to quickly identify abnormal areas and optimal paths during debugging and maintenance, significantly reducing the controllability of system evolution and the interpretability of maintenance. Therefore, the local graph density index not only reflects the spatial "pressure" of the graph structure but also warns of the systemic risk of structural complexity spreading across regions, making it one of the core evaluation indicators supporting path graph compression control and load distribution in scheduling.

[0070] It should be noted that the reason for using the above method to calculate the local graph density index, instead of conventional methods such as edge count, degree centrality averaging, subgraph density scoring, or simple path counting, is mainly because this method can comprehensively capture the local connectivity evolution trend and structural perturbation characteristics of the path graph within a multi-order hop range without relying on weighting or setting unknown factors, thereby achieving a deep identification of the complex problem of "over-connectivity + uneven distribution". Its greatest advantages are: First, it not only examines the cumulative reachability strength of each node within an L-order hop range, but also constructs a structural spatial scale index through normalized density scoring, making dense and sparse regions mathematically comparable; Second, compared to only looking at the number of edges or degree, this method successfully captures the "abrupt change zone at the edge of dense region" in the graph structure by calculating the first-order hop of the density score (i.e., the difference between adjacent nodes) and the second-order perturbation (i.e., the changing trend of the hop rate itself). This structure is often the real cause of scheduling oscillations and path selection instability; Third, this method does not rely on graph material... The embedded or external coordinates are constructed solely based on adjacency matrices and factorial jump reachability, exhibiting high universality and transferability. Fourth, the calculation process employs a hierarchical processing logic of nested local scoring—difference—perturbation—normalization—geometric mean, avoiding the problems of weight distortion or artificially set proportions in weighted averaging, resulting in a stable, controllable, and easily preset structural complexity threshold for the final index value. Fifth, all intermediate variables have clear physical or topological meanings, facilitating interpretation and system optimization linkage, making it particularly suitable for subsequent modules such as embedded graph compression, adaptive aggregation, or task scheduling load balancing. Therefore, this calculation method outperforms conventional methods in terms of mathematical stability, structural sensitivity, engineering adaptability, and system interpretability, making it an ideal method for constructing basic indices for high-dimensional graph structure control.

[0071] S3: The path graph structure complexity index is compared with a preset structure complexity threshold for judgment, including:

[0072] If the complexity index of the path graph structure is less than the preset complexity threshold, there is no need to regenerate the path graph; the current path graph can be used for transfer.

[0073] If the complexity index of the path graph structure is not less than the preset structure complexity threshold, then the path graph structure compression process is triggered to generate a new transport path graph.

[0074] S4: If the complexity index is not less than the preset structural complexity threshold, then the path graph structure compression process is triggered, and the steps to generate a new transport path graph are as follows:

[0075] In the path graph, find multiple path segments with the same start and end points and whose path costs differ within a set tolerance range, and merge them into a main path. The main path is retained as a scheduling priority path, and the remaining paths are marked as alternative paths and deleted to reduce the complexity of the graph structure.

[0076] It should be noted that the control mechanism dynamically determines whether the current path graph structure needs optimization. When the calculated path graph structure complexity index is lower than the set complexity threshold, it indicates that the overall structure of the current path graph remains within an acceptable complexity range. The system does not need to perform path compression or adjustment operations and can directly perform material transport scheduling based on the existing graph structure, avoiding additional computational burden and scheduling strategy interference caused by changes in the graph structure. Conversely, if the path graph structure complexity index is greater than or equal to the set structural complexity threshold, it indicates that there may be redundant connections, abrupt density changes, uneven path depths, or unbalanced connectivity in the graph structure, which may pose hidden risks to scheduling performance and system maintenance. Therefore, the system needs to immediately trigger path graph structure compression processing and proceed to the next optimization step to generate a new path graph with greater controllability.

[0077] In step S4, the core objective of path graph structure compression is to reduce structural redundancy in the graph and improve the clarity and stability of the scheduling graph. The specific implementation is as follows: The system first traverses all existing start-end point path pairs in the current path graph. For each pair of nodes, it extracts all corresponding feasible path segments and the total path cost, and performs cost clustering analysis based on this. The system then filters out a set of paths with similar costs based on a preset tolerance range (e.g., total cost difference within 5%), considering paths with high structural redundancy and large overlap of skip nodes as equivalent transport paths. Among the path sets that meet the conditions, the system prioritizes retaining the path segment with the simplest structure and lowest total cost as the main path. This main path is marked as the scheduling priority path and used as the first choice for subsequent transport task scheduling. The remaining paths are marked as redundant paths or alternative paths, and are compressed and hidden, logically deleted, or have their weight reduced or excluded from scheduling according to the system configuration strategy, effectively reducing the overall connection complexity and path interference of the graph structure. During path merging, node relationships and original topology information are preserved as compression history, allowing for path recovery or manual intervention in special scenarios or abnormal tasks. Furthermore, the compressed new path graph reconstructs the reachability matrix, updates the scheduling algorithm weights, and replaces the path graph version after the structure stabilizes. This enables the entire MCS system to achieve triple optimization—structure slimming, efficient scheduling, and controllable graph—while maintaining transport integrity.

[0078] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for automatically generating transport paths based on MCS system data modeling, characterized in that, Includes the following steps: S1: Obtain the current conveying path diagram generated based on the material control system, and obtain the global structure information of the path diagram; S2: Calculate the corresponding path graph structure complexity index based on multiple topological dimension features in the path graph. The topological dimension features include path redundancy and local region graph density. The topological dimension features include path redundancy index and local graph density index. The path redundancy index and local graph density index are normalized, and the normalized path redundancy index and local graph density index are added together to obtain the path graph structure complexity index. The steps for calculating the path redundancy index are as follows: Extract all unique start-end pairs from the path graph, and denote them as a set. , Indicates the first For the starting point of the path, Indicates the corresponding endpoint, for each pair List all feasible paths and record the total cost of each path, denoted as . in From arrive The number of paths, For the first The total cost of the path; For each set of costs Perform the following operations: Find the maximum and minimum path costs. And determine the cost is close to the range. Count the number of paths that fall within this interval. Calculate the redundancy overlap rate : ; Also includes: Cost set Sort by size from smallest to largest to obtain the sorted set. Calculate sorted sets Normalized difference sequence of adjacent path costs : Statistically normalized difference series The cost convergence rate is obtained by dividing the number of differences less than a preset threshold by the total number of differences in the normalized difference sequence. ; Combining redundancy overlap rate With cost convergence rate Calculate the redundancy index of a single path pair. , Redundancy index for all path pairs The normalized geometric mean is used to obtain the overall path redundancy index of the path graph. The calculation steps for the local graph density index are as follows: Let all nodes in the path graph form a set. ,in To represent the total number of nodes in the graph, construct a first-order adjacency matrix. Defined as: ; Set the maximum number of jumps Constructing a multi-order reachability matrix based on the adjacency matrix Used to indicate that a node is in Connectivity under step jumps; For each node Calculate its value in all 1 to The sum of reachable strengths within a step jump is denoted as: In the formula, Indicates the number of jumps. Below, from arrive The number of connected paths; express exist Total connectivity strength within a step jump; Also includes: Take all The maximum value in is And calculate the normalized connection density score for each node. : ; Calculate the density score difference between adjacent nodes for all nodes. , , Construct the jump rate for each node. , ;in It is a very small constant used to avoid division by zero errors; Construct second-order perturbation values ​​for all jump rates , , ; All Normalization, the maximum value is denoted as And calculate the disturbance score. , ; All perturbation scores The mean value is used as the local graph density index; S3: Compare the path graph structure complexity index with the preset structure complexity threshold for judgment; S4: If the complexity index is not less than the preset structural complexity threshold, then trigger the path graph structure compression process to generate a new transport path graph.

2. The method for automatically generating transport paths based on MCS system data modeling according to claim 1, characterized in that, The comparison between the path graph structure complexity index and a preset structure complexity threshold includes: If the complexity index of the path graph structure is less than the preset complexity threshold, there is no need to regenerate the path graph; the current path graph can be used for transfer. If the complexity index of the path graph structure is not less than the preset structure complexity threshold, then the path graph structure compression process is triggered to generate a new transport path graph.

3. The method for automatically generating transport paths based on MCS system data modeling according to claim 1, characterized in that, If the complexity index is not less than the preset structural complexity threshold, then the path graph structure compression process is triggered, and the steps to generate a new transport path graph are as follows: In the path graph, find multiple path segments with the same start and end points and whose path costs differ within a set tolerance range, and merge them into a main path. The main path is retained as a scheduling priority path, and the remaining paths are marked as alternative paths and deleted to reduce the complexity of the graph structure.

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