Cable plant multi-process material flow transfer state visual tracking method and system

Abstract

The application provides a cable workshop multi-process material flow transfer state visual tracking method and system, relates to the cable manufacturing technical field, and comprises the following steps: recognizing a subgraph mode through a cable manufacturing field knowledge graph, extracting process entity nodes and classifying the process entity nodes into parallel execution candidate clusters and serial execution forced chains, establishing a process flow transfer space-time optimization framework comprising a time dimension distribution layer and a space dimension scheduling layer, generating a mixed process scheduling result, realizing material flow transfer state visualization tracking, and effectively solving the technical problem that the material flow transfer state is difficult to be grasped in real time under the cable workshop multi-process environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cable manufacturing, and in particular to a cable workshop multi-process material flow state visualization tracking method and system. BACKGROUND

[0002] With the advent of the Industry 4.0 era, cable manufacturing enterprises are facing the demand for customized production of multi-variety and small batch, which challenges the traditional production management mode. The cable manufacturing process usually includes twisting, extrusion, braiding, assembly and other processes, and there is a complex material flow relationship between each process. In the workshop environment of multi-variety cable product parallel production, the material needs to be efficiently transferred between different processes to realize process coordination and optimal allocation of resources. At present, enterprises generally use manufacturing execution system (MES) and enterprise resource planning (ERP) systems to manage the production process, and realize material tracking through bar code or RFID technology. As a semantic network structure, knowledge graph can express and store field knowledge in the form of entities and relationships, providing a new technical means for production process management.

[0003] The existing cable workshop material flow management technology has the following defects and deficiencies:

[0004] The existing technology lacks deep semantic understanding of cable manufacturing process knowledge, and cannot effectively identify and distinguish the process chain that needs to be executed in series and the process cluster that can be arranged in parallel, resulting in insufficient process scheduling optimization and long material waiting time between processes.

[0005] The existing material flow management method usually considers the time dimension and the space dimension separately without establishing a unified space-time optimization framework, making it difficult to simultaneously consider the collaborative optimization of process time allocation and equipment space scheduling, resulting in low resource utilization and unreasonable material flow path. SUMMARY

[0006] The cable workshop multi-process material flow state visualization tracking method and system provided by the embodiments of the present application can solve the problems in the prior art.

[0007] In a first aspect, the cable workshop multi-process material flow state visualization tracking method provided by the embodiments of the present application comprises:

[0008] The pre-constructed cable manufacturing field knowledge graph is subjected to subgraph pattern recognition, first process entity nodes with the same predecessor node and no mutual restriction are extracted, the first process entity nodes are divided into parallel execution candidate clusters, second process entity nodes with sequential transmission of process semantic relationship are extracted, and the second process entity nodes are marked as serial execution mandatory chains, wherein, for the first process entity nodes, a corresponding device entity node set is determined through resource semantic relationship, and for the second process entity nodes, material state transfer requirements are extracted through the process semantic relationship.

[0009] A process flow time-space optimization framework including a time dimension allocation layer and a space dimension scheduling layer is established, the time dimension allocation layer generates a time slice allocation strategy according to the parallel execution candidate clusters and the serial execution mandatory chains, and the space dimension scheduling layer generates a device occupation scheme and a material movement path according to running state data of the device entity node set and position state data of the material entity node;

[0010] A mixed process scheduling result is determined according to the process flow time-space optimization framework, and a material flow state is visualized and tracked according to the mixed process scheduling result, the mixed process scheduling result including a start time, an execution time length and a bound device entity node identifier of each process entity node.

[0011] The method further includes constructing a cable manufacturing field knowledge graph:

[0012] The cable manufacturing field knowledge graph includes process entity nodes, device entity nodes, material entity nodes, process semantic relationships and resource semantic relationships,

[0013] The process entity nodes bear process parameter attributes, the device entity nodes bear process execution unit capability boundary attributes, and the material entity nodes bear work-in-process physical state attributes;

[0014] The process semantic relationships represent logical predecessor and successor relationships between process entity nodes, and the resource semantic relationships represent adaptability relationships between process entity nodes and device entity nodes.

[0015] The pre-constructed cable manufacturing field knowledge graph is subjected to subgraph pattern recognition, first process entity nodes with the same predecessor node and no mutual restriction are extracted, and the first process entity nodes are divided into parallel execution candidate clusters, including:

[0016] The process entity nodes in the cable manufacturing field knowledge graph are traversed, for each process entity node, its predecessor nodes are identified through the in-edge direction of the process semantic relationship, and process entity nodes with the same predecessor nodes are grouped into the same predecessor group.

[0017] For any two process entity nodes in the predecessor group, detect whether there is a process semantic relationship between the two process entity nodes that represents resource competition or state conflict. If neither exists, mark the two process entity nodes as a pair of mutually unconstrained nodes.

[0018] An undirected connected graph is constructed based on the mutually unconstrained node pairs, wherein the nodes of the undirected connected graph are the process entity nodes, and the edges of the undirected connected graph connect the two process entity nodes in the mutually unconstrained node pairs;

[0019] Perform connected component decomposition on the undirected connected graph, and take all process entity nodes contained in each connected component as a parallel execution candidate cluster to obtain multiple parallel execution candidate clusters.

[0020] Extracting the second process entity node with sequential process semantic relationships, and marking the second process entity node as a serial execution forced chain includes:

[0021] Starting from each process entity node in the knowledge graph of the cable manufacturing field, a depth-first traversal is performed along the outgoing edges of the process semantic relationship, and the sequence of process entity nodes traversed on the traversal path is recorded to obtain multiple process entity node paths;

[0022] Detect whether the process semantic relationship between adjacent process entity nodes on each process entity node path carries a forced order attribute, wherein the forced order attribute indicates that the output state of the preceding process entity node is a prerequisite for the input of the subsequent process entity node;

[0023] The process entity node paths are selected by filtering out all process entity node paths where the process semantic relationship between all adjacent process entity nodes carries the mandatory order attribute, thus obtaining candidate mandatory chains;

[0024] Maximal path extraction is performed on the candidate forced chains. If all process entity nodes contained in the first candidate forced chain belong to the second candidate forced chain, the first candidate forced chain is deleted, and the candidate forced chains that are not completely contained by other candidate forced chains are retained as serial execution forced chains.

[0025] The time-dimension allocation layer generates a time-slice allocation strategy based on the parallel execution candidate cluster and the serial execution forced chain, including:

[0026] The time dimension allocation layer extracts the idle time periods of the device entity nodes associated with the resource semantic relationship for the first process entity node, and calculates a set of time slices that allow time overlap based on the idle time periods. Each time slice in the set of time slices corresponds to a first process entity node, and there is partial or complete overlap between the time slices.

[0027] The time dimension allocation layer, for the second process entity node, according to the sequential transmission relationship of the process semantic relationship, uses the end time of the time slice allocated to the preceding process entity node as the earliest start time constraint for the time slice allocated to the subsequent process entity node, generating a non-overlapping sequential time slice sequence;

[0028] The time dimension allocation layer merges the time slice set with the sequential time slice sequence to form a time slice allocation strategy.

[0029] The spatial dimension scheduling layer generates equipment occupancy schemes and material movement paths based on the operating status data of the equipment entity node set and the location status data of the material entity nodes, including:

[0030] The spatial dimension scheduling layer selects available equipment entity nodes based on the start and end times of the time slices for each process entity node in the time slice allocation strategy, combined with the operating status data of the equipment entity node set, and generates an equipment occupancy scheme. The equipment occupancy scheme includes the binding relationship between process entity nodes and equipment entity nodes.

[0031] The spatial dimension scheduling layer calculates the transmission path for a material entity node to move from its current position to the location of a device entity node in the binding relationship based on the binding relationship in the device occupancy scheme and the position status data of the material entity node. It also determines the flow order of the material entity node between device entity nodes based on the process semantic relationship in the serial execution forced chain, and generates a material movement path. The material movement path includes the position transfer sequence and arrival time of the material entity node.

[0032] The process flow time-space optimization framework determines the mixed process scheduling result, and the process flow status is visualized and tracked based on the mixed process scheduling result, including:

[0033] Extract the start and end times of the time slice for each process entity node in the time slice allocation strategy. Use the start time of the time slice start and end times as the start time of the corresponding process entity node, and use the time span of the time slice start and end times as the execution duration of the corresponding process entity node.

[0034] The identifier of the equipment entity node with the binding relationship in the equipment occupancy scheme is used as the identifier of the equipment entity node bound to the corresponding process entity node;

[0035] The device entity node identifiers with the binding relationships in the startup time, execution duration, and equipment occupancy scheme are structured and organized to form a hybrid process scheduling result;

[0036] Based on the start time and the bound equipment entity node identifier in the mixed process scheduling result, and combined with the position transfer sequence and arrival time in the material movement path, the position state change sequence of the material entity node on the time axis is determined.

[0037] The sequence of positional state changes is mapped to a visualization interface. In the visualization interface, the time axis is used as the horizontal axis and the spatial distribution of the equipment entity nodes is used as the vertical axis. The flow trajectory of the material entity nodes is displayed through dynamic graphic elements, thereby realizing the visual tracking of the material flow status.

[0038] A second aspect of the present invention provides a visual tracking system for the material flow status of multiple processes in a cable workshop, comprising:

[0039] The first unit performs subgraph pattern recognition on a pre-constructed knowledge graph in the cable manufacturing field, extracts first process entity nodes that have the same predecessor node and no mutually restrictive process semantic relationships, divides the first process entity nodes into parallel execution candidate clusters, and simultaneously extracts second process entity nodes that have sequential process semantic relationships, marking the second process entity nodes as serial execution forced chains. Specifically, for the first process entity nodes, the corresponding set of equipment entity nodes is determined through resource semantic relationships; for the second process entity nodes, material state transition requirements are extracted through the process semantic relationships.

[0040] The second unit is used to establish a process flow spatiotemporal optimization framework including a time dimension allocation layer and a spatial dimension scheduling layer. The time dimension allocation layer generates a time slice allocation strategy based on the parallel execution candidate cluster and the serial execution forced chain. The spatial dimension scheduling layer generates equipment occupancy schemes and material movement paths based on the running status data of the equipment entity node set and the location status data of the material entity nodes.

[0041] The third unit is used to determine the mixed process scheduling result based on the process flow spatiotemporal optimization framework, and to perform visual tracking of the material flow status based on the mixed process scheduling result. The mixed process scheduling result includes the start time, execution duration and bound equipment entity node identifier of each process entity node.

[0042] A third aspect of the present invention,

[0043] An electronic device is provided, comprising:

[0044] processor;

[0045] Memory used to store processor-executable instructions;

[0046] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0047] Fourth aspect of the present invention,

[0048] A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0049] The beneficial effects of this application are as follows:

[0050] By performing subgraph pattern recognition on the knowledge graph in the cable manufacturing field, it is possible to accurately distinguish between process entity nodes of parallel execution candidate clusters and serial execution forced chains, thereby realizing the intelligent identification of complex dependencies between processes and solving the technical problem that traditional methods cannot effectively handle complex constraints between processes.

[0051] An innovative spatiotemporal optimization framework for process flow, comprising a time-dimension allocation layer and a spatial-dimension scheduling layer, was established. This framework decomposes the process scheduling problem into two levels: time slice allocation and spatial resource scheduling, effectively solving the optimization problem of material flow in complex multi-process scenarios in cable workshops.

[0052] By combining the semantic relationships of knowledge graphs with real-time status data, the visualization and tracking of material flow has been realized, enabling workshop managers to intuitively grasp the status and location information of materials in each process, significantly improving the transparency and controllability of the production process. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating the method for visually tracking the material flow status of multiple processes in a cable workshop, as described in an embodiment of the present invention.

[0054] Figure 2 This is a flowchart illustrating the process of visually tracking the flow of materials. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0056] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0057] refer to Figure 1 and Figure 2The present invention provides a method for visually tracking the material flow status of multiple processes in a cable workshop, comprising:

[0058] Subgraph pattern recognition is performed on a pre-constructed knowledge graph in the cable manufacturing field to extract first-process entity nodes that have the same predecessor node and no mutually restrictive process semantic relationships. The first-process entity nodes are divided into parallel execution candidate clusters. At the same time, second-process entity nodes with sequential process semantic relationships are extracted and marked as serial execution forced chains. Specifically, for the first-process entity nodes, the corresponding set of equipment entity nodes is determined through resource semantic relationships. For the second-process entity nodes, material state transition requirements are extracted through the process semantic relationships.

[0059] A process flow spatiotemporal optimization framework is established, which includes a time dimension allocation layer and a spatial dimension scheduling layer. The time dimension allocation layer generates a time slice allocation strategy based on the parallel execution candidate cluster and the serial execution forced chain. The spatial dimension scheduling layer generates equipment occupancy schemes and material movement paths based on the running status data of the equipment entity node set and the position status data of the material entity nodes.

[0060] The mixed process scheduling result is determined based on the process flow spatiotemporal optimization framework, and the material flow status is visualized and tracked based on the mixed process scheduling result. The mixed process scheduling result includes the start time, execution duration and bound equipment entity node identifier of each process entity node.

[0061] In an alternative implementation, the method further includes constructing a knowledge graph in the field of cable manufacturing:

[0062] The knowledge graph in the cable manufacturing field includes process entity nodes, equipment entity nodes, material entity nodes, process semantic relationships, and resource semantic relationships.

[0063] The process entity node carries the process parameter attributes of the process, the equipment entity node carries the capability boundary attributes of the process execution unit, and the material entity node carries the physical state attributes of the work-in-process.

[0064] The process semantic relationship represents the logical sequential relationship between process entity nodes, and the resource semantic relationship represents the compatibility relationship between process entity nodes and equipment entity nodes.

[0065] In constructing a knowledge graph for the cable manufacturing industry, the first step is to collect process data, equipment data, and material data related to cable manufacturing. This data comes from production management systems, equipment manuals, and process specification documents. The collected data includes information such as process names, process parameters, equipment models, equipment capacity parameters, and material specifications.

[0066] By cleaning and structuring the collected data, three types of core entity nodes are established: process entity nodes, equipment entity nodes, and material entity nodes. Process entity nodes include all technological steps in the entire cable manufacturing process, such as insulation extrusion, cabling, and sheath extrusion; equipment entity nodes include various equipment that perform the processes, such as extruders, stranding machines, and braiding machines; and material entity nodes include raw materials, semi-finished products, and finished products in the cable manufacturing process, such as copper rods, insulation materials, and sheath materials.

[0067] Process parameter attributes are assigned to process entity nodes. These attributes include, but are not limited to, key process parameters such as process execution time, temperature setting, speed requirements, and pressure control. Taking the insulation extrusion process as an example, its process parameter attributes include extrusion temperature (e.g., 180°C to 220°C), extrusion speed (e.g., 30 m / min to 50 m / min), and insulation thickness (e.g., 0.8 mm to 1.2 mm). These parameters directly affect product quality and are important information in the knowledge graph.

[0068] Equipment entity nodes are assigned capability boundary attributes, which define the range and limitations of the processes that the equipment can perform. Taking an extruder as an example, its capability boundary attributes include maximum extrusion temperature (e.g., 250 degrees Celsius), maximum extrusion speed (e.g., 60 meters per minute), applicable material types (e.g., PE, PVC, XLPE, etc.), and screw diameter (e.g., 60 mm to 90 mm). These attributes determine the matching relationship between the equipment and the processes.

[0069] Physical state attributes are assigned to material entities, describing the material's characteristics during manufacturing. Taking copper conductors as an example, these attributes include conductor cross-sectional area (e.g., 2.5 mm² to 400 mm²), conductor structure (e.g., single-strand or multi-strand stranding), and surface treatment (e.g., tin plating or bare copper). These attributes influence parameter settings and equipment selection for subsequent processes.

[0070] While constructing entity nodes, semantic relationships between nodes are established. These technological semantic relationships reflect the logical sequence between processes, represented by directed edges, where the direction of the edges indicates the order in which the processes are executed. For example, in the manufacturing of general power cables, a clear process flow chain is formed, from "conductor stranding" to "insulation extrusion," then to "cabling," and finally to "sheath extrusion." In the knowledge graph, these relationships are connected through semantic relationships between "preceding processes" and "subsequent processes," ensuring the integrity of the process path.

[0071] Resource semantic relationships characterize the compatibility between processes and equipment, defining which equipment can perform specific processes and their degree of compatibility. This relationship not only indicates whether equipment can perform a process but also includes a compatibility score (e.g., a maximum score of 100), reflecting the efficiency and quality level of the equipment in performing the process. For example, a certain type of extruder might have a compatibility score of 85 for insulation extrusion and 95 for sheathing extrusion; this information guides equipment allocation decisions.

[0072] Simultaneously, the consumption and output relationships between materials and processes are established. Consumption relationships define the types and quantities of materials required by a process, while output relationships define the form of materials produced after the process is completed. For example, the insulation extrusion process consumes copper conductors and insulating material, producing insulated conductors. These relationships form the basis of material flow.

[0073] To improve the practicality of knowledge graphs, a rule-based reasoning mechanism is introduced. Based on existing entities and relationships, reasoning rules are designed to discover implicit knowledge. For example, if the next step after process A is process B, and the next step after process B is process C, then it can be deduced that there is a transitive relationship between process A and process C; if equipment X is adapted to process A, and process A needs to process material M, then equipment X must support the ability to process material M.

[0074] In practical applications, when it is necessary to select a suitable production process path for a specific product specification, the necessary set of processes is first determined based on the product specification. Then, the execution order of the processes is determined through process semantic relationships. Next, the available equipment for each process is filtered through resource semantic relationships. Finally, the optimal process path is generated based on the equipment capability boundaries and the current state.

[0075] To verify the effectiveness of the knowledge graph, a certain type of power cable was selected as the test object. This cable requires four main processes: conductor stranding, insulation extrusion, cabling, and sheath extrusion. By querying the knowledge graph, the parameter setting range and a list of available equipment for each process were obtained. The results showed that the system successfully identified the equipment combination that met the requirements and provided recommended values ​​for the process parameters, which were consistent with expert experience values, proving the practical value of the knowledge graph.

[0076] The knowledge graph in the cable manufacturing field constructed through the above steps not only systematically organizes the relationship network of processes, equipment and materials, but also provides a knowledge foundation for process path planning, equipment selection and parameter optimization, thereby improving the level of intelligence in the cable manufacturing process.

[0077] In one optional implementation, subgraph pattern recognition is performed on a pre-constructed knowledge graph of the cable manufacturing domain to extract first process entity nodes that have the same predecessor nodes and do not have mutually restrictive process semantic relationships. The first process entity nodes are then divided into parallel execution candidate clusters, including:

[0078] Traverse the process entity nodes in the knowledge graph of the cable manufacturing field. For each process entity node, identify its predecessor node through the incoming edge direction of the process semantic relationship, and group process entity nodes with the same predecessor node into the same predecessor group.

[0079] For any two process entity nodes in the predecessor group, detect whether there is a process semantic relationship between the two process entity nodes that represents resource competition or state conflict. If neither exists, mark the two process entity nodes as a pair of mutually unconstrained nodes.

[0080] An undirected connected graph is constructed based on the mutually unconstrained node pairs, wherein the nodes of the undirected connected graph are the process entity nodes, and the edges of the undirected connected graph connect the two process entity nodes in the mutually unconstrained node pairs;

[0081] Perform connected component decomposition on the undirected connected graph, and take all process entity nodes contained in each connected component as a parallel execution candidate cluster to obtain multiple parallel execution candidate clusters.

[0082] In cable manufacturing, complex dependencies and opportunities for parallel execution often exist between processes. This implementation provides a knowledge graph-based method for identifying parallel processes, which can effectively discover processes that can be executed simultaneously and optimize production scheduling.

[0083] First, a knowledge graph for the cable manufacturing field is pre-constructed, which includes process entity nodes and their technological semantic relationships. Process entity nodes represent specific manufacturing processes, such as wire stripping, crimping, and welding; technological semantic relationships represent the dependencies, conflicts, or resource competition relationships between processes.

[0084] When traversing the process entity nodes in the knowledge graph, for each process node, its predecessor node is determined by analyzing the direction of its incoming edges. For example, if there is a directed edge from process A to process B representing a "precedence relationship", then process A is the predecessor node of process B. In practice, a depth-first search algorithm can be used to start from each process node, search backwards for all its incoming edges, and record the source nodes connected by these incoming edges as the set of its predecessor nodes.

[0085] Based on the precursor node, the process entity nodes are classified into precursor groups. Suppose that on an automotive wiring harness production line, after the process "wire cutting" is completed, three processes need to be performed: "end stripping", "intermediate stripping" and "end marking". These three processes have the same precursor node "wire cutting", so they are classified into the same precursor group.

[0086] Next, for any two process nodes within the same predecessor group, we check whether there are any constraints between them. Constraints mainly fall into two categories: resource contention and state conflict. Resource contention refers to two processes needing to use the same non-shareable resources, like a single piece of equipment; state conflict refers to the execution of one process affecting the preconditions of another process. In the knowledge graph, these relationships are explicitly represented as process semantic relationship edges.

[0087] Continuing with the previous example, check whether there is a constraint relationship between "end stripping" and "intermediate stripping". If they require different stripping equipment and do not cause a conflict in the wire condition, then they are determined to be independent of each other; if "end stripping" and "end marking" require operating on the same part of the wire, then there is a conflict in the condition and they cannot be performed in parallel.

[0088] For pairs of process nodes that are determined to be unconstrained, an undirected connected graph is constructed. In this graph, each node represents a process entity. If two processes are unconstrained, an undirected edge is added between them. Continuing the example above, suppose "end peeling" and "middle peeling" are unconstrained, "middle peeling" and "end marking" are unconstrained, but "end peeling" and "end marking" are constrained. In this case, a path will be formed between these three process nodes in the undirected graph, rather than a fully connected subgraph.

[0089] After constructing the undirected connected graph, a connected component decomposition algorithm is executed to decompose the graph into multiple connected subgraphs. All nodes in each connected subgraph are independent of each other and can therefore be considered as a candidate cluster for parallel execution. Breadth-first search or depth-first search algorithms can be used for connected component decomposition. Starting with an unvisited node, all nodes connected to it are explored until no further exploration is possible; this constitutes a connected component. Then, the next unvisited node is selected, and the above process is repeated until all nodes have been visited.

[0090] In the example above, after connected component decomposition, two candidate clusters for parallel execution may be obtained: one containing "end peeling" and "middle peeling", and the other containing "end tagging". This means that during production scheduling, "end peeling" and "middle peeling" can be scheduled to be executed simultaneously, while "end tagging" needs to be scheduled separately.

[0091] In practical applications, considering the characteristics of cable manufacturing, the parallel candidate clusters can be further optimized. For example, the optimal parallel combination scheme can be selected based on factors such as process execution time and resource utilization. For complex cable products, there may be multi-level parallel relationships; the above method can be recursively applied to identify parallel opportunities at different levels.

[0092] The parallel execution candidate clusters identified through this method provide a scientific basis for cable manufacturing process planning and production scheduling, which helps to improve production efficiency, shorten production cycles, and reduce manufacturing costs.

[0093] In one optional implementation, extracting the second process entity node with sequential process semantic relationships and marking the second process entity node as a serial execution forced chain includes:

[0094] Starting from each process entity node in the knowledge graph of the cable manufacturing field, a depth-first traversal is performed along the outgoing edges of the process semantic relationship, and the sequence of process entity nodes traversed on the traversal path is recorded to obtain multiple process entity node paths;

[0095] Detect whether the process semantic relationship between adjacent process entity nodes on each process entity node path carries a forced order attribute, wherein the forced order attribute indicates that the output state of the preceding process entity node is a prerequisite for the input of the subsequent process entity node;

[0096] The process entity node paths are selected by filtering out all process entity node paths where the process semantic relationship between all adjacent process entity nodes carries the mandatory order attribute, thus obtaining candidate mandatory chains;

[0097] Maximal path extraction is performed on the candidate forced chains. If all process entity nodes contained in the first candidate forced chain belong to the second candidate forced chain, the first candidate forced chain is deleted, and the candidate forced chains that are not completely contained by other candidate forced chains are retained as serial execution forced chains.

[0098] In the knowledge graph of the cable manufacturing industry, the process semantic relationships between process entity nodes are of great significance for production process planning and optimization. Extracting the second process entity node with sequential process semantic relationships and marking it as a serial execution forced chain can help identify process sequences that must be executed in a strict order, thereby optimizing the production plan.

[0099] Starting with each process entity node in the knowledge graph of the cable manufacturing domain, a depth-first search is performed along the outgoing edges of the process semantic relationships. During the traversal, the sequence of process entity nodes visited is recorded, generating multiple process entity node paths. In specific implementation, a depth-first search algorithm can be used, starting from each process entity node in the knowledge graph and recursively visiting its connected downstream nodes, while maintaining a stack of access paths to record all process entity nodes on the current traversal path.

[0100] For example, in the cable manufacturing process, suppose there are process nodes such as "copper wire drawing", "stranding", "insulation extrusion", "shielding layer coating", and "sheath extrusion". Starting from the "copper wire drawing" node, a depth-first traversal might yield the path: "copper wire drawing → stranding → insulation extrusion → shielding layer coating → sheath extrusion". Similarly, starting from the "stranding" node, multiple paths might be obtained, such as: "stranding → insulation extrusion → shielding layer coating → sheath extrusion".

[0101] The process checks whether the semantic relationship between adjacent process entity nodes along each process entity node path carries a mandatory order attribute. A mandatory order attribute indicates that the output state of a preceding process entity node is a prerequisite for the input of a subsequent process entity node. During the detection, the attribute information of the edges connecting two adjacent process entity nodes in the knowledge graph needs to be queried to determine whether they contain mandatory order markers.

[0102] In practical applications, the forced order attribute can be represented by specific attribute values ​​of edges, such as setting the Boolean attribute "isForced=true" to indicate a forced order relationship. For example, the process semantic relationship between "stranded wire" and "insulation extrusion" has a forced order attribute because only the multi-strand copper wire obtained after the stranding process can undergo the insulation extrusion process. However, the relationship between "color marking" and "insulation extrusion" may not have a forced order attribute because the marking process may be performed simultaneously with the insulation extrusion process.

[0103] After examining the process semantic relationships between adjacent nodes in all paths, paths containing process entity nodes whose process semantic relationships between all adjacent process entity nodes carry a mandatory order attribute are selected, resulting in candidate mandatory chains. This step ensures that all processes in the candidate mandatory chains must be executed sequentially in a strict order, without allowing adjustments to the order or parallel processing.

[0104] For example, the test results may indicate that "copper wire drawing → stranding → insulation extrusion → sheath extrusion" is a candidate forced chain because there is a forced sequence relationship between these adjacent processes; while "insulation extrusion → color marking → packaging" may not be a candidate forced chain because there may not be a forced sequence relationship between "color marking" and "packaging".

[0105] Finally, maximal path extraction is performed on the candidate forced chains. The purpose of maximal path extraction is to remove redundant paths that are completely contained by other paths, retaining only the longest path that is not completely contained by other paths. Specifically, each pair of candidate forced chains is compared: if all process entity nodes contained in the first candidate forced chain belong to the second candidate forced chain, then the first candidate forced chain is deleted, and the second candidate forced chain is retained. In this way, candidate forced chains that are not completely contained by other candidate forced chains are obtained, which serve as the final serial execution forced chains.

[0106] Taking cable manufacturing as an example, suppose we obtain the following candidate forced chains:

[0107] 1. Copper wire drawing → stranding → insulation extrusion;

[0108] 2. Stranded wire → Insulation extrusion → Shielding layer coating → Sheath extrusion;

[0109] 3. Insulation extrusion → Shielding layer coating;

[0110] 4. Copper wire drawing → stranding → insulation extrusion → shielding layer coating → sheath extrusion.

[0111] During the maximal path extraction process, candidate forced chains 1 and 3 are completely contained by candidate forced chains 4 and 2, respectively. Therefore, candidate forced chains 1 and 3 are deleted, and candidate forced chains 2 and 4 are retained as the serial execution forced chains.

[0112] Through the above steps, the second-stage entity nodes with sequentially transmitted process semantic relationships were successfully extracted from the knowledge graph of the cable manufacturing field and marked as serial execution forced chains. These forced chains represent a sequence of processes that must be strictly executed in order, which is of great guiding significance for optimizing production planning, ensuring product quality, and improving production efficiency.

[0113] In one optional implementation, the time dimension allocation layer generates a time slice allocation strategy based on the parallel execution candidate cluster and the serial execution forced chain, including:

[0114] The time dimension allocation layer extracts the idle time periods of the device entity nodes associated with the resource semantic relationship for the first process entity node, and calculates a set of time slices that allow time overlap based on the idle time periods. Each time slice in the set of time slices corresponds to a first process entity node, and there is partial or complete overlap between the time slices.

[0115] The time dimension allocation layer, for the second process entity node, according to the sequential transmission relationship of the process semantic relationship, uses the end time of the time slice allocated to the preceding process entity node as the earliest start time constraint for the time slice allocated to the subsequent process entity node, generating a non-overlapping sequential time slice sequence;

[0116] The time dimension allocation layer merges the time slice set with the sequential time slice sequence to form a time slice allocation strategy.

[0117] In production scheduling systems, in order to achieve efficient allocation of process execution time, the time dimension allocation layer generates a time slice allocation strategy based on the parallel execution candidate cluster and the serial execution forced chain.

[0118] The time-dimension allocation layer first processes the first-stage entity nodes, which can be executed in parallel. For each first-stage entity node, the idle time periods of associated device entity nodes are extracted through resource semantic relationships. Specifically, for each first-stage entity node, the current working status information of its associated device entity nodes is queried to obtain a list of idle time periods for the devices. This list contains multiple time intervals, each defined by a start time and an end time.

[0119] After acquiring idle time slots, the time dimension allocation layer calculates a set of time slices that allow for time overlap based on these idle time slots. The calculation process includes: first, matching the execution duration of each first process entity node with the idle time slot of the corresponding device to determine feasible time slices; then, performing a combination analysis on all feasible time slices to determine whether there is partial or complete overlap between time slices of different processes; finally, retaining those time slice combinations that meet device constraints and allow parallel execution to form a time slice set.

[0120] For example, suppose there are three first-process entity nodes A, B, and C, which are associated with devices D1, D2, and D3, respectively. Analysis shows that A can execute during the time slot [8:00-9:30], B can execute during the time slot [8:30-10:00], and C can execute during the time slot [9:00-10:30]. These three time slots partially overlap but do not violate resource constraints; therefore, they are included in the set of time slots that allow time overlap.

[0121] For the second process entity node, the time dimension allocation layer processes it according to the sequential transmission characteristics of the process semantic relationship. The process semantic relationship defines the dependencies between processes, and the subsequent process can only start after the preceding process is completed. Therefore, the time dimension allocation layer uses the end time of the time slice allocated to the preceding process entity node as the constraint for the earliest start time of the time slice allocated to the subsequent process entity node.

[0122] In practice, the process dependency graph is first constructed to identify all process links. For each link, time slice allocation is progressively advanced from the starting node. Assuming a link contains processes P1, P2, and P3, the time-dimension allocation layer first allocates a time slice [10:00-11:00] to P1. Then, the earliest start time of P2's time slice cannot be earlier than 11:00. If P2's execution duration is 1.5 hours, then P2's time slice is [11:00-12:30]. Similarly, the earliest start time of P3's time slice cannot be earlier than 12:30. In this way, a non-overlapping sequential time slice sequence is allocated to the processes throughout the entire link.

[0123] When dealing with complex process networks, there may be situations where a process has multiple preceding processes. In this case, the maximum value of the completion times of all preceding processes is taken as the earliest start time of the process. For example, if process X has two preceding processes Y and Z, with Y completing at 14:00 and Z completing at 15:30, then the earliest start time of X is 15:30.

[0124] After allocating the time slice set of the first process entity nodes and the sequential time slice sequence of the second process entity nodes, the time dimension allocation layer merges these two parts to form a complete time slice allocation strategy. The merging process must ensure that no resource constraints or process dependencies are violated.

[0125] The specific implementation of the merging strategy includes: first, arranging the two types of time slices in chronological order; then checking for resource conflicts, and if a conflict is found, adjusting the time slices of the first type of process to ensure the priority of the time slices of the second type of process; finally, generating the final time slice allocation strategy, which includes the execution time schedule of all processes.

[0126] Through the processing of the time dimension allocation layer described above, the system can efficiently allocate reasonable execution time to each process, which not only meets the efficiency requirements of parallel execution, but also ensures the process constraints of serial execution, thereby optimizing the overall production scheduling.

[0127] In one optional implementation, the spatial dimension scheduling layer generates equipment occupancy schemes and material movement paths based on the operating status data of the equipment entity node set and the location status data of the material entity nodes, including:

[0128] The spatial dimension scheduling layer selects available equipment entity nodes based on the start and end times of the time slices for each process entity node in the time slice allocation strategy, combined with the operating status data of the equipment entity node set, and generates an equipment occupancy scheme. The equipment occupancy scheme includes the binding relationship between process entity nodes and equipment entity nodes.

[0129] The spatial dimension scheduling layer calculates the transmission path for a material entity node to move from its current position to the location of a device entity node in the binding relationship based on the binding relationship in the device occupancy scheme and the position status data of the material entity node. It also determines the flow order of the material entity node between device entity nodes based on the process semantic relationship in the serial execution forced chain, and generates a material movement path. The material movement path includes the position transfer sequence and arrival time of the material entity node.

[0130] The spatial dimension scheduling layer generates equipment occupancy plans and material movement paths based on the operating status data of the equipment entity node set and the location status data of the material entity node. The specific implementation method is as follows.

[0131] The spatial dimension scheduling layer receives the time slice allocation strategy from the temporal dimension scheduling layer. This strategy includes the start and end times of the time slices for each process entity node. Simultaneously, the spatial dimension scheduling layer also acquires the operational status data of the equipment entity node set and the location status data of the material entity nodes. The operational status data includes the equipment's working status (e.g., idle, running, faulty), processing capacity parameters, and remaining available time; the location status data includes the material's current spatial coordinates, material type, and quantity.

[0132] Based on the start and end times of the time slices for each process entity node in the time slice allocation strategy, and combined with the operational status data of the equipment entity node set, equipment entity nodes in an available state are filtered. An available state means that the equipment is idle and has the capability to execute a specific process. Specifically, during the filtering process, for each process entity node, its corresponding start and end times are queried, such as time slices T1 to T2. Then, the equipment entity node set is traversed, checking whether each device node is available during the time period T1 to T2 and whether it has the function to execute the process. For example, for the cutting process, it is necessary to filter out cutting equipment that has cutting capabilities and is idle during a specified time period.

[0133] After screening, the most suitable equipment entity node is assigned to each process entity node, forming a binding relationship and generating an equipment occupancy plan. The binding process uses an optimization algorithm, considering factors such as the distance between the equipment and the current position of the material, the equipment's processing efficiency, and energy consumption, to select the equipment with the highest comprehensive score. The equipment occupancy plan is represented as a mapping table, recording the equipment entity node identifier, start time, and end time of occupancy for each process entity node.

[0134] After obtaining the equipment occupancy plan, the transfer path for the material to move from its current location to the target equipment location is calculated based on the location status data of the material entity nodes. The path calculation uses the A* algorithm, taking into account constraints such as obstacles and aisle widths in the factory layout. For example, assuming that material A is currently located in the warehouse area (x1, y1) and needs to be moved to equipment B in the processing area (x2, y2), the shortest path is calculated using the A* algorithm, which avoids fixed obstacles and temporary obstacle areas in the factory.

[0135] Simultaneously, based on the process semantic relationships in the serial execution forced chain, the flow order of material entity nodes among equipment entity nodes is determined. Process semantic relationships define the process sequence constraints for material processing; for example, the drilling process must be completed before the welding process. By parsing the process semantic relationships, a directed graph G is obtained, where vertices represent processes and edges represent dependencies. A topological sorting algorithm is then used to process the directed graph G to obtain a process execution sequence that meets the process requirements.

[0136] By combining the binding relationships in the equipment occupancy scheme, the transfer path from the current position of the material to each piece of equipment, and the process execution sequence, a complete material movement path is generated. This path includes the position transfer sequence and arrival time of the material entity nodes. For example, if material M needs to be processed through equipment D1, D2, and D3 in sequence, the material movement path is represented as a series of position transfers: from the starting position P0 to position P1 of equipment D1 (arrival time t1), from P1 to position P2 of equipment D2 (arrival time t2), and from P2 to position P3 of equipment D3 (arrival time t3).

[0137] Material movement paths also need to consider constraints on transportation resources, such as the availability and speed of transportation equipment like forklifts and AGVs. When multiple materials need to be moved simultaneously, scheduling algorithms should be used to rationally allocate transportation resources to avoid congestion and conflicts during transportation.

[0138] The material movement path is then calculated: raw materials move from the warehouse to cutting machine B (estimated arrival time 7:50), after cutting, they move to drilling machine C (estimated arrival time 9:20), and after drilling, they move to assembly table D (estimated arrival time 10:50). The entire material movement path is matched with the time slices of the processes to ensure that materials arrive at each processing equipment on time, thereby improving production efficiency.

[0139] In one optional implementation, determining the hybrid process scheduling result based on the process flow spatiotemporal optimization framework, and visually tracking the material flow status based on the hybrid process scheduling result, includes:

[0140] Extract the start and end times of the time slice for each process entity node in the time slice allocation strategy. Use the start time of the time slice start and end times as the start time of the corresponding process entity node, and use the time span of the time slice start and end times as the execution duration of the corresponding process entity node.

[0141] The identifier of the equipment entity node with the binding relationship in the equipment occupancy scheme is used as the identifier of the equipment entity node bound to the corresponding process entity node;

[0142] The device entity node identifiers with the binding relationships in the startup time, execution duration, and equipment occupancy scheme are structured and organized to form a hybrid process scheduling result;

[0143] Based on the start time and the bound equipment entity node identifier in the mixed process scheduling result, and combined with the position transfer sequence and arrival time in the material movement path, the position state change sequence of the material entity node on the time axis is determined.

[0144] The sequence of positional state changes is mapped to a visualization interface. In the visualization interface, the time axis is used as the horizontal axis and the spatial distribution of the equipment entity nodes is used as the vertical axis. The flow trajectory of the material entity nodes is displayed through dynamic graphic elements, thereby realizing the visual tracking of the material flow status.

[0145] When determining the scheduling results of mixed processes based on the process flow spatiotemporal optimization framework and visually tracking the material flow status, it is first necessary to extract the start and end times of the time slices for each process entity node in the time slice allocation strategy. Specifically, from the time slice allocation strategy data structure generated by the optimization framework, for each process entity node, its corresponding time slice information, including the start and end times, is read. The start time of the time slice is directly used as the start time of the process entity node, and the time span of the time slice (end time minus start time) is calculated as the execution duration of the process entity node.

[0146] For example, if the start and end times of a certain process entity node A in the time slice allocation strategy are from 9:00 AM to 10:30 AM, then the start time of the process is 9:00 AM, and the execution duration is 90 minutes. This information is temporarily stored in the scheduling information table to prepare for the subsequent organization of scheduling results.

[0147] Next, based on the binding relationships in the equipment occupancy scheme, the identifier of the equipment entity node bound to each process entity node is determined. The equipment occupancy scheme typically exists in the form of a mapping table, containing a one-to-one correspondence between process entity nodes and equipment entity nodes. By querying this mapping table, the unique identifier of the equipment entity node bound to the current process entity node is obtained, and this identifier is associated with the scheduling information of that process entity node.

[0148] For example, if process entity node A is assigned to equipment entity node D001 in the equipment occupancy scheme, then D001 will be used as the equipment entity node identifier bound to process A. In this way, each process entity node can determine which specific equipment it executes on, providing spatial dimension information for subsequent material flow tracking.

[0149] Subsequently, the information obtained in the first two steps (start time, execution duration, and bound device entity node identifier) ​​is structured to form a hybrid process scheduling result. This result can be represented by a data structure containing multiple fields, with each record corresponding to the complete scheduling information of a process entity node, including fields such as process ID, start time, execution duration, and bound device ID.

[0150] In the process of structured organization, the dependencies between processes also need to be considered to ensure that subsequent processes can only begin after the preceding processes are completed. This can be verified by checking the start time of a process and the completion time of its preceding processes. The final hybrid process scheduling result can be stored in JSON format or a database table for easy querying and processing later.

[0151] Based on the mixed process scheduling results and combined with material movement path information, the sequence of positional changes of material entity nodes on the time axis is determined. First, the start time of each process and the identifier of the bound equipment entity node are obtained from the mixed process scheduling results; then, the position transfer sequence and arrival time information are extracted from the material movement path data.

[0152] By sorting by time and associating by location, a complete flow trajectory of material entity nodes can be constructed. Specifically, for each material entity node, its spatial location is recorded in chronological order, including its processing status on the equipment and its movement status on the transport path. When the material is on the equipment, its status is "processing"; when the material is on the transport path, its status is "transporting".

[0153] For example, material M001 enters equipment D001 for processing at time T1, lasts for T2-T1, then begins moving along path P1 at time T2, and arrives at equipment D002 at time T3. This sequence of state changes can be represented as: (T1, D001, "Processing") → (T2, P1, "Transporting") → (T3, D002, "Processing"). In this way, the position and state changes of the material throughout the entire production process are fully recorded.

[0154] Finally, the sequence of positional changes is mapped to a visualization interface to achieve visual tracking of material flow. In the visualization interface, a two-dimensional plane is constructed with the time axis as the horizontal axis and the spatial distribution of equipment entity nodes as the vertical axis. On this plane, the flow process of material entity nodes is represented by dynamic graphical elements (such as moving points, lines, or trajectories with different colors).

[0155] The visualization interface can be implemented using technologies such as SVG, Canvas, or WebGL to ensure smooth animation effects. The interface allows for timeline zooming, enabling users to view material flow at different time granularities. Furthermore, color coding can distinguish the flow trajectories of different materials, and labels can display key information such as material ID and current status.

[0156] When users interact with the interface (such as clicking on a material node), a detailed information window pops up, displaying the material's complete processing history, current progress, and subsequent plans. This visual tracking method intuitively shows the flow of materials in the production system, helping production managers to grasp the production situation in real time and promptly identify and resolve potential problems.

[0157] This invention provides a visualization and tracking system for the material flow status of multiple processes in a cable workshop, comprising:

[0158] The first unit performs subgraph pattern recognition on a pre-constructed knowledge graph in the cable manufacturing field, extracts first process entity nodes that have the same predecessor node and no mutually restrictive process semantic relationships, divides the first process entity nodes into parallel execution candidate clusters, and simultaneously extracts second process entity nodes that have sequential process semantic relationships, marking the second process entity nodes as serial execution forced chains. Specifically, for the first process entity nodes, the corresponding set of equipment entity nodes is determined through resource semantic relationships; for the second process entity nodes, material state transition requirements are extracted through the process semantic relationships.

[0159] The second unit is used to establish a process flow spatiotemporal optimization framework including a time dimension allocation layer and a spatial dimension scheduling layer. The time dimension allocation layer generates a time slice allocation strategy based on the parallel execution candidate cluster and the serial execution forced chain. The spatial dimension scheduling layer generates equipment occupancy schemes and material movement paths based on the running status data of the equipment entity node set and the location status data of the material entity nodes.

[0160] The third unit is used to determine the mixed process scheduling result based on the process flow spatiotemporal optimization framework, and to perform visual tracking of the material flow status based on the mixed process scheduling result. The mixed process scheduling result includes the start time, execution duration and bound equipment entity node identifier of each process entity node.

[0161] A third aspect of the present invention,

[0162] An electronic device is provided, comprising:

[0163] processor;

[0164] Memory used to store processor-executable instructions;

[0165] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0166] Fourth aspect of the present invention,

[0167] A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0168] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for visually tracking the material flow status of multiple processes in a cable workshop, characterized in that, include: Subgraph pattern recognition is performed on a pre-constructed knowledge graph in the cable manufacturing domain to extract the first process entity nodes that have the same predecessor nodes and no mutually restrictive process semantic relationships. The first process entity nodes are then divided into parallel execution candidate clusters. Simultaneously, extract the second process entity node with sequential process semantic relationships, and mark the second process entity node as a serial execution forced chain, including: Starting from each process entity node in the knowledge graph of the cable manufacturing field, a depth-first traversal is performed along the outgoing edges of the process semantic relationship, and the sequence of process entity nodes traversed on the traversal path is recorded to obtain multiple process entity node paths; Detect whether the process semantic relationship between adjacent process entity nodes on each process entity node path carries a forced order attribute, wherein the forced order attribute indicates that the output state of the preceding process entity node is a prerequisite for the input of the subsequent process entity node; The process entity node paths are selected by filtering out all process entity node paths where the process semantic relationship between all adjacent process entity nodes carries the mandatory order attribute, thus obtaining candidate mandatory chains; Maximal path extraction is performed on the candidate forced chains. If all process entity nodes contained in the first candidate forced chain belong to the second candidate forced chain, the first candidate forced chain is deleted, and the candidate forced chains that are not completely contained by other candidate forced chains are retained as serial execution forced chains. Specifically, for the first process entity node, the corresponding set of equipment entity nodes is determined through resource semantic relationships; for the second process entity node, the material state transition requirements are extracted through the process semantic relationships. A process flow spatiotemporal optimization framework is established, which includes a time dimension allocation layer and a spatial dimension scheduling layer. The time dimension allocation layer generates a time slice allocation strategy based on the parallel execution candidate cluster and the serial execution forced chain. The spatial dimension scheduling layer generates equipment occupancy schemes and material movement paths based on the running status data of the equipment entity node set and the position status data of the material entity nodes. The mixed process scheduling result is determined based on the process flow spatiotemporal optimization framework, and the material flow status is visualized and tracked based on the mixed process scheduling result. The mixed process scheduling result includes the start time, execution duration and bound equipment entity node identifier of each process entity node.

2. The method according to claim 1, characterized in that, The method also includes constructing a knowledge graph in the field of cable manufacturing: The knowledge graph in the cable manufacturing field includes process entity nodes, equipment entity nodes, material entity nodes, process semantic relationships, and resource semantic relationships. The process entity nodes carry the process parameter attributes of the process, the equipment entity nodes carry the capability boundary attributes of the process execution unit, and the material entity nodes carry the physical state attributes of the work-in-process. The process semantic relationship represents the logical sequential relationship between process entity nodes, and the resource semantic relationship represents the compatibility relationship between process entity nodes and equipment entity nodes.

3. The method according to claim 1, characterized in that, Subgraph pattern recognition is performed on a pre-constructed knowledge graph in the cable manufacturing domain to extract first process entity nodes that have the same predecessor nodes and no mutually restrictive process semantic relationships. The first process entity nodes are then divided into parallel execution candidate clusters, including: Traverse the process entity nodes in the knowledge graph of the cable manufacturing field. For each process entity node, identify its predecessor node through the incoming edge direction of the process semantic relationship, and group process entity nodes with the same predecessor node into the same predecessor group. For any two process entity nodes in the predecessor group, detect whether there is a process semantic relationship between the two process entity nodes that represents resource competition or state conflict. If neither exists, mark the two process entity nodes as a pair of mutually unconstrained nodes. An undirected connected graph is constructed based on the mutually unconstrained node pairs, wherein the nodes of the undirected connected graph are the process entity nodes, and the edges of the undirected connected graph connect the two process entity nodes in the mutually unconstrained node pairs; Perform connected component decomposition on the undirected connected graph, and take all process entity nodes contained in each connected component as a parallel execution candidate cluster to obtain multiple parallel execution candidate clusters.

4. The method according to claim 1, characterized in that, The time-dimension allocation layer generates a time-slice allocation strategy based on the parallel execution candidate cluster and the serial execution forced chain, including: The time dimension allocation layer extracts the idle time periods of the device entity nodes associated with the resource semantic relationship for the first process entity node, and calculates a set of time slices that allow time overlap based on the idle time periods. Each time slice in the set of time slices corresponds to a first process entity node, and there is partial or complete overlap between the time slices. The time dimension allocation layer, for the second process entity node, according to the sequential transmission relationship of the process semantic relationship, uses the end time of the time slice allocated to the preceding process entity node as the earliest start time constraint for the time slice allocated to the subsequent process entity node, generating a non-overlapping sequential time slice sequence; The time dimension allocation layer merges the time slice set with the sequential time slice sequence to form a time slice allocation strategy.

5. The method according to claim 4, characterized in that, The spatial dimension scheduling layer generates equipment occupancy schemes and material movement paths based on the operating status data of the equipment entity node set and the location status data of the material entity nodes, including: The spatial dimension scheduling layer selects available equipment entity nodes based on the start and end times of the time slices for each process entity node in the time slice allocation strategy, combined with the operating status data of the equipment entity node set, and generates an equipment occupancy scheme. The equipment occupancy scheme includes the binding relationship between process entity nodes and equipment entity nodes. The spatial dimension scheduling layer calculates the transmission path for a material entity node to move from its current position to the location of a device entity node in the binding relationship based on the binding relationship in the device occupancy scheme and the position status data of the material entity node. It also determines the flow order of the material entity node between device entity nodes based on the process semantic relationship in the serial execution forced chain, and generates a material movement path. The material movement path includes the position transfer sequence and arrival time of the material entity node.

6. The method according to claim 1, characterized in that, The process flow time-space optimization framework determines the mixed process scheduling result, and the process flow status is visualized and tracked based on the mixed process scheduling result, including: Extract the start and end times of the time slice for each process entity node in the time slice allocation strategy. Use the start time of the time slice start and end times as the start time of the corresponding process entity node, and use the time span of the time slice start and end times as the execution duration of the corresponding process entity node. The identifier of the equipment entity node with the binding relationship in the equipment occupancy scheme is used as the identifier of the equipment entity node bound to the corresponding process entity node; The device entity node identifiers with the binding relationships in the startup time, execution duration, and equipment occupancy scheme are structured and organized to form a hybrid process scheduling result; Based on the start time and the bound equipment entity node identifier in the mixed process scheduling result, and combined with the position transfer sequence and arrival time in the material movement path, the position state change sequence of the material entity node on the time axis is determined. The sequence of positional state changes is mapped to a visualization interface. In the visualization interface, the time axis is used as the horizontal axis and the spatial distribution of the equipment entity nodes is used as the vertical axis. The flow trajectory of the material entity nodes is displayed through dynamic graphic elements, thereby realizing the visual tracking of the material flow status.

7. A visual tracking system for the multi-process material flow status in a cable workshop, used to implement the method as described in any one of claims 1-6, characterized in that, include: The first unit performs subgraph pattern recognition on a pre-constructed knowledge graph in the cable manufacturing field, extracts first process entity nodes that have the same predecessor node and no mutually restrictive process semantic relationships, divides the first process entity nodes into parallel execution candidate clusters, and simultaneously extracts second process entity nodes that have sequential process semantic relationships, marking the second process entity nodes as serial execution forced chains. Specifically, for the first process entity nodes, the corresponding set of equipment entity nodes is determined through resource semantic relationships; for the second process entity nodes, material state transition requirements are extracted through the process semantic relationships. The second unit is used to establish a process flow spatiotemporal optimization framework including a time dimension allocation layer and a spatial dimension scheduling layer. The time dimension allocation layer generates a time slice allocation strategy based on the parallel execution candidate cluster and the serial execution forced chain. The spatial dimension scheduling layer generates equipment occupancy schemes and material movement paths based on the running status data of the equipment entity node set and the location status data of the material entity nodes. The third unit is used to determine the mixed process scheduling result based on the process flow spatiotemporal optimization framework, and to perform visual tracking of the material flow status based on the mixed process scheduling result. The mixed process scheduling result includes the start time, execution duration and bound equipment entity node identifier of each process entity node.

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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