Method for realizing automatic generation of carrying path based on MCS system data modeling

Abstract

The invention discloses a method for realizing automatic generation of a carrying path based on MCS system data modeling, and relates to the technical field of path generation. The method comprises the following steps: obtaining a current carrying path diagram generated based on a material control system, and obtaining global structure information of the path diagram; according to a plurality of topological dimension features in the path diagram, calculating a corresponding path diagram structure complexity index, the topological dimension features including a path redundancy condition and a local area diagram density; comparing and judging the structural complexity index of the path diagram with a preset structural complexity threshold value; and if the complexity index is not less than a preset structure complexity threshold, triggering path diagram structure compression processing, and generating a new conveying path diagram. Through the mode, the global topological structure complexity problem of the current carrying path diagram generated by the material control system can be measured and handled, so that the automatic path construction has efficient initial performance, and meanwhile, the stability and maintainability under large-scale system operation can be supported for a long time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of path generation, in particular to a method for automatically generating a conveying path based on MCS system data modeling. BACKGROUND

[0002] Under the background of continuous acceleration of intelligent manufacturing and industrial logistics automation, as a key platform connecting upper production planning and lower logistics execution, the material control system (MCS) undertakes core functions such as whole factory conveying path planning, equipment scheduling and material flow optimization, and its running efficiency and flexibility are directly related to the rhythm and capacity of the whole production system. The current mainstream MCS system generally adopts an artificial preset mode in path graph construction, that is, an operator sets device nodes and their path relationships one by one in the system interface according to experience, and then generates a whole conveying topology graph by manual configuration to support subsequent path planning and scheduling decisions. However, with the continuous growth of the number of factory equipment, the increasing complexity of production line layout and the increasing demand for flexible production, the traditional path graph generation method based on manual operation faces serious adaptability and efficiency bottlenecks. Not only is the work process tedious and the deployment period long, but also human errors such as path connection errors and node omissions are prone to occur, leading to frequent problems such as conveying task failure and system scheduling chaos. In addition, due to the lack of self-adaptive ability to equipment changes, once equipment is added, removed or adjusted, the system often needs to be shut down for overall reconfiguration of the path graph, which seriously restricts the dynamic response ability and overall operation flexibility of the manufacturing system. In order to solve the above problems, in recent years, some schemes have tried to introduce an automatic path graph generation mechanism based on equipment data driving, that is, to automatically build conveying nodes using AMHS equipment and Port information, and to automatically generate edge sets based on the logical relationships between nodes, so as to build a weighted path graph that can be used for Dijkstra or A* algorithm. This method has obvious advantages in reducing labor costs and improving deployment efficiency, but it also exposes some systematic defects that have not been paid attention to in the evolution process of path graph structure.

[0003] Although this kind of automatic mapping mechanism has achieved a high degree of intelligence in path generation and dynamic updating, without global structure control of the path graph, it may still cause graph structure redundancy expansion, local density anomaly and other complexity out-of-control phenomena after the scale of equipment is expanded, thereby leading to sharp decline in path algorithm running efficiency, intensified fluctuations in scheduling results and significantly increased difficulty in system maintenance, and other implicit risks. The existing schemes generally ignore the measurement and response mechanism for such global topology structure complexity problems, lack necessary graph structure compression, path aggregation or redundancy suppression means, so that the automatic path construction has high initial efficiency, but it is difficult to support stability and maintainability in the long run under the running of large-scale systems. SUMMARY

[0004] The present application aims to solve the above-mentioned problems, and provides a method for automatically generating a conveying path based on MCS system data modeling.

[0005] In the first aspect of the present application, a method for automatically generating a conveying path based on MCS system data modeling is provided, which comprises: S1: obtaining a current conveying path map generated based on a material control system, and obtaining global structure information of the path map; S2: calculating a corresponding path map structure complexity index according to a plurality of topological dimension characteristics in the path map, the topological dimension characteristics including path redundancy and local area map density; S3: comparing the path map structure complexity index with a preset structure complexity threshold value for judgment; S4: if the complexity index is not less than the preset structure complexity threshold value, triggering path map structure compression processing to generate a new conveying path map.

[0006] Optionally, the step of calculating the corresponding path map structure complexity index according to the plurality of topological dimension characteristics in the path map comprises: The topological dimension characteristics include a path redundancy index and a local map density index, the path redundancy index and the local map density index are normalized, the normalized path redundancy index and the local map density index are added, and a path map structure complexity index is obtained.

[0007] The calculation step of the path redundancy index comprises: extracting all unique start-end point pairs from the path map, denoted as a set , denoting the start point of the i-th pair of paths, denoting the corresponding end point, listing all feasible paths for each pair , and recording the total cost of each path, denoted as wherein is the number of paths from to , is the total cost of the i-th path; for each set of cost , the following operations are performed: taking the maximum and minimum path cost , and determining the cost proximity interval counting the number of paths falling within the interval , and calculating the redundancy overlap rate : . .

[0008] ​Optionally, the calculation steps of the path redundancy index further include: Cost set Sort by size from smallest to largest to obtain the sorted set.

[0009] 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. ; 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.

[0010] Optionally, the calculation steps for the local graph density index are as follows: 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: ; Set the maximum number of jumps Constructing a multi-order reachability matrix based on the adjacency matrix , used to indicate that the node is 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 connection strength within a step jump.

[0011] Optionally, the calculation step of the local graph density index further includes: Take all The maximum value in is And calculate the normalized connection density score for each node. : ; Calculate the adjacent node density score difference for all nodes (from the 2nd) , , ; construct the jump rate of each node , ; wherein is a very small constant, used to avoid division by zero error; Construct the second-order perturbation value for all jump rates (from the 3rd) , , ; normalize all , the maximum value is , and calculate the perturbation score , ; The average of all perturbation scores is taken as the local graph density index.

[0012] Optionally, the path graph structure complexity index is compared with the preset structure complexity threshold to determine whether the path graph needs to be regenerated, which includes: If the path graph structure complexity index is less than the preset structure complexity threshold, the path graph does not need to be regenerated, and the current path graph can be delivered; If the path graph structure complexity index is not less than the preset structure complexity threshold, the path graph structure compression processing is triggered to generate a new delivery path graph.

[0013] Optionally, if the complexity index is not less than the preset structure complexity threshold, the step of triggering the path graph structure compression processing to generate a new delivery path graph is: Find multiple path segments with the same starting point and ending point and a path cost difference less than a set tolerance range in the path graph, and merge them into a main path, which is reserved as a scheduling priority path, and the remaining paths are marked as alternative paths and deleted to reduce the graph structure complexity.

[0014] The beneficial effects of the present application are: The application provides a kind of based on MCS system data modeling realizes the flow path automatic generation method, by obtaining the current flow path graph generated based on material control system, and obtain the global structure information of path graph;According to the multiple topological dimension characteristics in path graph, the corresponding path graph structure complexity index is calculated, and the topological dimension characteristics include path redundancy and local area graph density;Path graph structure complexity index is compared with preset structure complexity threshold value and judged;If the complexity index is not less than the preset structure complexity threshold value, then trigger path graph structure compression processing, generate new flow path graph.By the above-mentioned mode, the global topological structure complexity problem of the current flow path graph generated by material control system can be measured and responded, so that the automatic path construction has efficient initial performance, and also can support stability and maintainability under large-scale system operation for a long time. BRIEF DESCRIPTION OF DRAWINGS

[0015] The application will be further described below with reference to the drawings.

[0016] Figure 1 It is a flow chart of a kind of based on MCS system data modeling realizes the flow path automatic generation method. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0018] The application provides a kind of based on MCS system data modeling realizes the flow path automatic generation method. Referring to Figure 1 , Figure 1 It is a flow chart of a kind of based on MCS system data modeling realizes the flow path automatic generation method provided in the embodiments of the application. The method comprises the following steps: S1: obtain the current flow path graph generated based on material control system, and obtain the global structure information of path graph; S2: according to the multiple topological dimension characteristics in path graph, the corresponding path graph structure complexity index is calculated, and the topological dimension characteristics include path redundancy and local area graph density; S3: path graph structure complexity index is compared with preset structure complexity threshold value and judged; S4: if the complexity index is not less than the preset structure complexity threshold value, then trigger path graph structure compression processing, generate new flow path graph.

[0019] Based on the embodiment of the application, the method for automatically generating a conveying path based on MCS system data modeling is provided, by the above-mentioned mode, the complexity of the global topology structure of the current conveying path graph generated by the material control system can be measured and responded, so that the automatic path construction has high initial performance and can also support the stability and maintainability under large-scale system operation in the long term.

[0020] In one embodiment, a method for automatically generating a conveying path based on MCS system data modeling comprises the following steps: S1: obtaining a current conveying path graph generated based on a material control system, and obtaining global structure information of the path graph; In the S1 step, obtaining a current conveying path graph generated based on a material control system refers to a directed graph structure containing all device nodes and path connection relationships between them generated by the system after the MCS system models the in-plant equipment, the conveying unit and the connection relationship thereof. The path graph is usually represented in the form of a graph data structure, wherein the node represents various devices or Port interfaces, and the edge represents the actual conveying path between the devices, and the edge may be attached with initial weight information such as conveying distance, time or transmission cost. Obtaining the global structure information of the path graph refers to structurally extracting and summarizing the topological characteristics of the whole graph without changing the structure of the path graph, including but not limited to extracting the start and end point distribution of all path segments, the number and connection density of edges, the reachability relationship between nodes, the maximum and minimum depth of the path, the strong connected subgraph division, etc., to reflect the overall complexity and structural characteristics of the path graph. For example, if the system detects that there are a large number of different path segments from device A to device B in the graph, and their costs (such as conveying time) are extremely close, it may mean that there is structural redundancy in the graph. For another example, the interconnection between devices in a certain area is extremely dense, while the structure of another part of the graph is sparse, which indicates that the local graph density distribution is uneven. For another example, some devices in the graph must pass through 10 jump nodes to reach, while some devices only need 1 hop to reach, which indicates that the path depth presents a high degree of unevenness. These information constitute the basis data for subsequent calculation of the path graph structure complexity index, and are the prerequisite for judging whether the path graph is too complex and needs to be structurally compressed.

[0021] S2: calculating a corresponding path graph structure complexity index according to a plurality of topological dimension characteristics in the path graph, the topological dimension characteristics including path redundancy, local area graph density, path depth distribution and graph structure connectivity; In one implementation, the step of calculating a corresponding path graph structure complexity index according to a plurality of topological dimension characteristics in the path graph is: The path redundancy index and the local graph density index are normalized, the normalized path redundancy index and the normalized local graph density index are added, and a path graph structure complexity index is obtained.

[0022] It should be noted that the normalization processing dimension removal method includes Min-Max normalization, Z-Score standardization, etc., which will not be repeated here. The calculation steps of the path redundancy index are as follows: All unique start-end pairs are extracted from the path graph, denoted as set , denotes the start point of the path, denotes the corresponding end point, and for each pair all feasible paths are listed, and the total cost of each path is recorded, denoted as wherein is the number of paths from to , is the total cost of the path; For each cost set , the following operations are performed: taking the maximum and minimum path cost : and determining the cost proximity interval The number of paths falling within the interval is counted , and the redundancy overlap rate is calculated: . .

[0023] The cost set is sorted in ascending order to obtain a sorted set

[0024] The normalized difference sequence of adjacent path costs in the sorted set is calculated: ; the proportion of differences less than a predetermined threshold in the normalized difference sequence is counted, and the proportion of differences less than the predetermined threshold is divided by the total number of differences in the normalized difference sequence to obtain a cost convergence rate . ; The redundancy index of a single pair of paths is calculated by combining the redundancy overlap rate and the cost convergence rate , ; the redundancy indexes of all path pairs are normalized and geometrically averaged to obtain the path redundancy index of the entire path graph.

[0025] ​​It should be noted that the data involved in the above path redundancy index calculation process are all derived from the material transfer task data and system topology data recorded and managed by the material control system in real time during operation. Specifically, the starting point, end point and all feasible paths therebetween in the path graph are automatically generated by the MCS system according to the field device layout and transfer logic relationship, each node corresponds to an actual material handling device or its port (Port), and each path corresponds to an executable transfer channel; when the system performs task scheduling, the transfer cost information of each path will be recorded in real time, which can be calculated from the path length, transmission time, transfer times, device switching delay and energy consumption, or directly counted by the system monitoring module, thereby forming the total cost set of all feasible paths between each pair of starting and ending points; the maximum value and the minimum value can be obtained by directly searching the total cost value, and the number of paths falling into the adjacent interval is obtained by counting the number of paths whose cost difference is within the set tolerance; the sorting set and the adjacent path difference sequence are automatically calculated and generated by the system in the data analysis module; the cost convergence rate is obtained from the proportion of the path cost difference; finally, the normalization geometric mean process of all redundancy indexes is completed by relying on the data aggregation module of the MCS system. In other words, all data are derived from the internal operation records, topology structure and transfer task history data of the system, the calculation process does not require manual intervention, nor does it require external data import, ensuring the real-time and objectivity of the index calculation.

[0026] It should be noted that the path redundancy index is an index for measuring whether there are a large number of "similar functions but similar costs" multiple feasible paths between the same starting point and ending point in the conveying path graph. The core purpose is to reflect whether structural redundancy is generated in the path graph under the premise that the task target is unchanged, that is, whether the diversity of the path has exceeded the range that can be effectively utilized by the scheduling strategy. The larger the path redundancy index, the more path segments in the system have no significant difference in actual cost, which belongs to the "low difference high overlap" redundant structure. This redundancy will grow exponentially if not controlled in the context of continuous expansion of the number of devices, eventually leading to an extremely complex and inflated path graph structure, which seriously weakens the running efficiency of the path algorithm. For example, there should be only 1-2 optimal paths from device A to device B, but due to the automatic mapping mechanism of the system without constraints, more than 10 slightly different path segments may be generated, and the cost difference of these paths may be within 3%, which leads to repeated judgment and sorting among multiple similar paths when the path algorithm (such as Dijkstra or A*) performs shortest path calculation, greatly increasing the algorithm complexity, and further increasing the random volatility of scheduling decisions. At the same time, when the difference between paths is not obvious, it is also difficult for the system to form a stable strategy tendency when balancing loads or selecting paths, which may lead to path oscillation, task congestion or frequent switching of scheduling, thereby affecting the overall conveying efficiency and system running stability. Furthermore, when it is necessary to investigate abnormal paths or maintain and optimize the graph structure, it is difficult for engineers to quickly locate the key path due to the large number of similar paths between the same starting and ending points, resulting in a significant increase in system maintenance costs. Therefore, the path redundancy index not only reveals the redundancy degree of the path graph, but also is one of the key indicators for judging whether the graph structure has "structural out-of-control hidden dangers", especially in a large-scale device environment. The larger the value, the more attention should be paid to the complexity management of the system structure.

[0027] It should be noted that the above method is used to calculate the path redundancy index, instead of the traditional path number statistics, average cost difference calculation or weighted sum, mainly because this method constructs a more discriminant and structure sensitive redundancy evaluation logic from the two dimensions of path cost overlap density and cost sequence convergence trend, which can effectively capture the implicit redundancy characteristics of the path graph that "function is repeated but lacks cost differentiation". The greatest advantage is that: first, this method does not depend on subjective weight setting or artificial classification standard, but is completely based on the objective cost distribution formed inside the path graph for analysis, ensuring the uniformity and portability of the results; second, it jointly evaluates the substantial difference between paths through the "local concentration rate" (i.e. redundancy overlap rate) of cost approximation path and the "small jump frequency" (i.e. cost convergence rate) of global path difference, which can identify high redundancy areas that are consistent in cost although different in form, thereby avoiding misjudging "useful diversity" as redundancy and avoiding missing "apparently different but actually repeated" paths; third, this method does not involve weighted sum, avoiding distortion caused by improper weight selection, and the formula structure is symmetrical and the variable meaning is clear, with strong mathematical explanatory power and algorithm implementation; finally, this method designs "normalization, sorting, difference, interval projection" operations in each layer calculation, making it have good scale invariance and graph structure adaptability, that is, regardless of the size of the device and the number of paths, the result always falls within the 0-1 interval, which is convenient for collaborative use with other structure 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, which is a high numerical calculation method for system structure complexity management.

[0028] The calculation steps of the local graph density index are: Let all nodes in the path graph constitute a set , where represents the total number of nodes in the graph, and a first-order adjacency matrix of the nodes is constructed , which is defined as: ; Set the maximum jump order , construct a multi-order reachable matrix based on the adjacency matrix, which is used to represent the connectivity of nodes under order jump; For each node , calculate the sum of reachable strength within all 1 to order jumps, denoted as: , where represents the number of connected paths from to under jump order ; denotes in the total connection strength within the hop.

[0029] take the maximum value of all as and calculate the normalized connection density score : ; calculate the difference of adjacent node density score , , for all nodes (from the 2nd) to construct the hop rate of each node , ; where is a very small constant to avoid division by zero error; construct the second-order perturbation value , , for all hop rates (from the 3rd); normalize all , the maximum value is , and calculate the perturbation score , ; take the average of all perturbation scores as the local graph density index.

[0030] It should be noted that all the data involved in the calculation process of the above local graph density index come from the graph structure information and node connection relationship information maintained by the material control system (MCS) in the automatic path graph construction and task scheduling process. Specifically, the node set in the path graph can be automatically extracted by the system registered equipment, station, conveying port and other entity units, and each node represents an actual conveyable position point; the connection relationship between nodes is determined by the first-order adjacency relationship generated by the system in the mapping stage according to the physical layout and conveying rules, forming an adjacency matrix, which is maintained in the system in the form of sparse matrix or edge set for a long time; on this basis, the system can construct the jump reachable matrix of any order through matrix power operation, and the jump order is automatically controlled by the set parameter, which is used to describe the connectivity of nodes within several jump paths. The reachable strength of each node is the result of summing by row in these jump reachable matrices, which is directly calculated by the graph processing module in the system. The intermediate calculation data such as density score, hop rate, second-order perturbation can be generated by the graph algorithm engine on the basis of the original graph structure in real time, without relying on any historical task data or external information, and completely derived from the structure of the current path graph, ensuring the repeatability and system self-consistency of the calculation process. This data source mode ensures that the generation of local graph density index is not only efficient but also objective and universal.

[0031] It should be noted that the local graph density index is a quantitative index for measuring whether the different node areas in the path graph are structurally connected excessively and the density distribution is extremely uneven within the multi-hop connection range. The core role is to identify the "graph structure accumulation" phenomenon in the local area of the graph structure due to the excessive closeness of the connection between nodes and the excessive redundancy of the hop path, so as to judge whether the system has appeared topological level imbalance under the condition of good surface connectivity. The larger the local graph density index, the more "high-density local clusters" exist in the path graph, that is, there are a large number of short-hop paths, near-neighbor high-coincidence paths or redundant interconnections between nodes in some internal regions, and these structures will be rapidly amplified and superimposed in the process of device scale expansion without intervention, thereby causing the complexity of the graph structure to lose control from point to surface, resulting in a significant decline in the running efficiency of the path algorithm. For example, if 10 devices in a certain area form a dense network that can be reached within 3 steps, while other areas remain sparsely connected, it will make the path planning algorithm need to process a large number of comparison, sorting and path weight evaluation of hop paths when executing calculation in the high-density area, and the calculation overhead increases exponentially. At the same time, due to the large number of optional paths in such high-density areas, "random looping" or "selection oscillation" behaviors are easily formed during scheduling, which makes the system choose different paths for the same transfer task each time, resulting in unstable scheduling results, unpredictable scheduling logs, invalid resource scheduling priorities, and other phenomena, which seriously affect the production rhythm and transfer reliability. In addition, when multiple such high-density areas appear in the graph structure, it will be difficult for engineering personnel to quickly identify abnormal areas and optimal paths during debugging and maintenance, and the controllability of system evolution and the explainability of maintenance are greatly reduced. Therefore, the local graph density index not only reflects the "pressure" of the graph structure in space, but also warns of the systemic risk of the possible spread of structural complexity across regions, and is one of the core evaluation indexes supporting path graph compression control and scheduling load dispersion.

[0032] It should be noted that the above method is used to calculate the local graph density index, instead of using the conventional method of edge number statistics, degree centrality average, subgraph density score or simple path count, mainly because this method can fully capture the local connectivity evolution trend and structure disturbance characteristics of the path graph in the multi-order jump range without relying on weighting and without setting unknown factors, thereby realizing the deep identification of the "overconnection + uneven distribution" complex problem. The greatest advantage is that: first, it not only investigates the accessibility accumulation strength of each node in the L-order jump range, but also constructs a structural spatial scale index through the normalized density score, so that the dense area and the sparse area can be compared mathematically; second, compared with only looking at the number of edges or degrees, this method successfully captures the "mutation zone of the edge of the dense area" in the graph structure by calculating the first-order jump (i.e. the difference between adjacent nodes) and the second-order disturbance (i.e. the change trend of the jump rate itself) of the density score, which is often the real cause of scheduling shock and unstable path selection; third, this method does not rely on physical embedding or external coordinates of the graph, but is based on the adjacency matrix and order jump accessibility, and has high universality and migration; fourth, the calculation process adopts a hierarchical processing logic of local score-difference-disturbance-normalization-geometric average, avoiding the problem of weight distortion or artificial setting of proportion in weighted average, so that the final index value is stable, controllable and easy to preset the structure complexity threshold; fifth, all intermediate variables have clear physical or topological meanings, which is convenient for explanation and system optimization linkage, especially suitable for embedding in subsequent modules such as graph compression, adaptive aggregation or task scheduling load balancing. Therefore, this calculation method is superior to the conventional method in terms of mathematical stability, structure sensitivity, engineering adaptability and system interpretability, and is an ideal basis for constructing high-dimensional graph structure control index.

[0033] S3: Compare the path graph structure complexity index with the preset structure complexity threshold value. If the path graph structure complexity index is less than the preset structure complexity threshold value, the path graph does not need to be regenerated, and the current path graph can be delivered. If the path graph structure complexity index is not less than the preset structure complexity threshold value, trigger the path graph structure compression processing to generate a new delivery path graph.

[0034] S4: If the complexity index is not less than the preset structure complexity threshold value, trigger the path graph structure compression processing to generate a new delivery path graph. Find multiple path segments with the same starting point and ending point in the path graph, and the path cost difference is less than the set tolerance range, and merge them into a main path, the main path is reserved as the scheduling priority path, and the remaining paths are marked as alternative paths and deleted to reduce the graph structure complexity.

[0035] It should be noted that the control mechanism is used to dynamically determine whether the current path graph structure needs to be optimized. When the calculated path graph structure complexity index is lower than the set complexity threshold, it means that the overall structure of the current path graph is kept within an acceptable complexity range, and the system does not need to perform path compression or adjustment operation, and can directly perform material carrying scheduling based on the existing graph structure, avoiding additional calculation burden and scheduling strategy interference caused by graph structure changes; on the contrary, if the path graph structure complexity index is greater than or equal to the set structure complexity threshold, it means that there may be redundant connections, density mutations, uneven path depth or imbalance of connected structure in the graph structure, which may bring implicit risks to the scheduling performance and system maintenance, therefore the system needs to immediately trigger the path graph structure compression processing and enter the next optimization process to generate a new path graph with stronger controllability.

[0036] In step S4, the core goal of the path graph structure compression processing is to reduce structural redundancy in the graph and improve the clarity and stability of the scheduling graph. The specific implementation of the processing is as follows: the system first traverses all existing start-end path pairs in the current path graph, extracts all feasible path segments and total path costs corresponding to each pair of nodes, and performs cost clustering analysis on this basis; the system filters out path sets with similar costs according to a preset tolerance range (for example, a total cost difference within 5%), and regards paths with high structural repetition and large overlapping of jump nodes as equivalent carrying paths. In the path set that meets the conditions, the system preferentially retains the path segment with the simplest structure and the smallest total cost as the main path, which will be marked as the scheduling priority path and used as the preferred path for subsequent carrying task scheduling; the remaining paths are marked as redundant paths or alternative paths, which are compressed, hidden, logically deleted or have their weights reduced or excluded from participating in scheduling according to system configuration strategies, so as to effectively reduce the overall connection complexity and path interference degree of the graph structure. The node relationship and original topology information are retained as compression history records during the path merging process, so that path recovery or manual intervention can be performed in special scenarios or abnormal tasks. In addition, the new path graph after compression will reconstruct the reachability matrix, update the scheduling algorithm weight input, and replace the path graph version after the structure is stable, so that the entire MCS system realizes the three optimizations of structure slimming, scheduling efficiency and graph controllability under the premise of maintaining carrying integrity.

[0037] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application and cannot be considered as limiting the scope of the implementation of the present application. Any equivalent changes and improvements made within the scope of the present application should still be attributed to the patent coverage of the present application.

Claims

1. A method for automatically generating a transfer path based on MCS system data modeling, characterized in that, The method comprises the following steps: S1: obtaining a current conveying path map generated based on a material control system, and obtaining global structure information of the path map; S2: calculating a corresponding path map structure complexity index according to a plurality of topological dimension features in the path map, the topological dimension features including path redundancy and local area graph density; S3: comparing the path map structure complexity index with a preset structure complexity threshold value for judgment; S4: if the complexity index is not less than the preset structure complexity threshold value, triggering path map structure compression processing to generate a new conveying path map.

2. The method of claim 1, wherein the method is based on a data modeling of a MCS system to automatically generate a transfer path. The step of calculating the corresponding path map structure complexity index according to a plurality of topological dimension features in the path map is: The topological dimension features include a path redundancy index and a local graph density index, the path redundancy index and the local graph density index are normalized, and the normalized path redundancy index and the local graph density index are added to obtain the path map structure complexity index.

3. The method of claim 2, wherein the method is based on a data modeling of a MCS system to automatically generate a transfer path. The calculation step of the path redundancy index is: All unique start-end pairs are extracted from the path map, denoted as set , denotes the starting point of the path, denotes the corresponding end point, for each pair all feasible paths are listed and the total cost of each path is recorded, denoted by where is the number of paths from to , is the total cost of the path; for each set of costs perform the following operations: take the maximum and minimum path cost : and determine the cost proximity interval count the number of paths falling within this interval , compute the redundancy overlap rate : .

4. The method of claim 3, wherein the method is based on a data modeling of a MCS system to automatically generate a transfer path. The calculation step of the path redundancy index further includes: The cost set is obtained The cost set is sorted in ascending order to obtain a sorted cost set The sorted cost set is calculated The normalized difference sequence of adjacent path costs in the sorted cost set is calculated : The normalized difference sequence is counted The proportion of the number of differences less than the preset threshold value in the normalized difference sequence is calculated, and the proportion of the number of differences less than the preset threshold value is divided by the total number of differences in the normalized difference sequence to obtain a cost convergence rate ; 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.

5. The method of claim 2, wherein the method is based on MCS system data modeling to automatically generate a transfer path. The calculation step of the local graph density index is: Let S be the set of all nodes in the path graph where denotes the total number of nodes in the graph, and the first order adjacency matrix of the nodes is constructed as , is defined as: ; Setting maximum hop order , constructing a multi-order reachable matrix based on an adjacency matrix , for representing connectivity of nodes at -order hops For each node , the sum of its reachable strength within all 1 to order jumps is calculated, denoted as: , where represents the number of connected paths from to at jump order ; represents the total connection strength of within order jumps.

6. The method of claim 5, wherein the method is based on MCS system data modeling to automatically generate a transfer path. The calculation step of the local graph density index further includes: Take the maximum value in all as and calculate the normalized connection density score of each node : ; Calculate the difference in adjacent node density score for all nodes , , ; construct the hop rate for each node , ; where is a very small constant to avoid division by zero error; Construct second order perturbation values for all jump rates , , ; normalize all , maximum value is , and compute perturbation score , ; The mean of all perturbation scores is taken as the local graph density index.

7. The method of claim 1, wherein the method is based on a data modeling of a MCS system to automatically generate a transfer path. The comparison of the path map structure complexity index with the preset structure complexity threshold value for judgment includes: If the path map structure complexity index is less than the preset structure complexity threshold value, the path map does not need to be regenerated, and the current path map can be conveyed; If the path map structure complexity index is not less than the preset structure complexity threshold value, path map structure compression processing is triggered to generate a new conveying path map.

8. The method of claim 1, wherein the method is based on a data modeling of a MCS system to automatically generate a transfer path. If the complexity index is not less than the preset structure complexity threshold value, triggering path map structure compression processing to generate a new conveying path map includes: In the path map, a plurality of path segments with the same start point and end point and a path cost difference less than a set tolerance range are found and merged into a main path, the main path is reserved as a scheduling priority path, and the remaining paths are marked as alternative paths and deleted to reduce the graph structure complexity.

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