Mine equipment intelligent manufacturing production scheduling optimization system and method

Abstract

The application relates to the technical field of data reasoning, and discloses a mine equipment intelligent manufacturing production scheduling optimization system and method. The system comprises a topological graph generation module, an initial scheduling Gantt chart generation module, a demand intensity coefficient acquisition module, a flexible scheduling Gantt chart acquisition module and a final scheduling instruction set generation module. A topological graph is constructed based on the process connection of mine equipment. An initial scheduling Gantt chart is generated by taking a component task set as a vertical axis and a corresponding final assembly line task sequence as a horizontal axis. When a disturbance event occurs, an affected process dependency tree is extracted, a task delay cost is evaluated, and a demand intensity coefficient for manufacturing process rescheduling is output. A flexible scheduling Gantt chart is generated based on process beat deviation and equipment utilization in synchronous adjustment of a manufacturing process. A final scheduling instruction set is confirmed through process path dynamic correction and process optimization results. The application can improve the efficiency of a mine equipment manufacturing production process scheduling scheme.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a production scheduling optimization system and method for intelligent manufacturing of mining equipment. Background Technology

[0002] In the manufacturing process of mining equipment, existing production scheduling schemes fail to fully integrate equipment status data, material inventory data, and order information to construct an effective production status topology diagram. This makes it difficult to accurately represent the production status of mining equipment. Consequently, in the initial scheduling stage, the Gantt chart generated from the component task set and the assembly line task sequence cannot efficiently match the actual production needs. This makes it difficult to coordinate and optimize process cycle time and equipment utilization, resulting in low efficiency of the production scheduling scheme.

[0003] When disturbances occur during production, existing technologies cannot quickly extract the affected process dependency trees from the topology diagram, nor can they accurately assess the delay costs of task nodes. This leads to inaccurate calculation of the demand intensity coefficient for rescheduling, making it impossible to synchronously adjust process cycle time deviations and equipment utilization based on this coefficient, thus hindering the generation of a flexible scheduling Gantt chart. Furthermore, existing technologies are ineffective in dynamic process path correction and process optimization, failing to confirm the final scheduling instruction set through the synergistic effect of the process path correction topology diagram and the flexible scheduling Gantt chart. Consequently, the scheduling scheme is ineffective during the manufacturing process of mining equipment. Summary of the Invention

[0004] This invention provides a production scheduling optimization system and method for intelligent manufacturing of mining equipment, the main purpose of which is to solve the problem of low efficiency in production scheduling schemes for mining equipment manufacturing.

[0005] To achieve the above objectives, the present invention provides a production scheduling optimization system for intelligent manufacturing of mining equipment, characterized in that the system includes a topology graph generation module, an initial scheduling Gantt chart generation module, a demand intensity coefficient acquisition module, a flexible scheduling Gantt chart acquisition module, and a final scheduling instruction set generation module, wherein: The topology generation module is used to construct a topology map representing the production status of mining equipment based on the process connections of equipment status data, material inventory data, and order information of mining equipment. The initial scheduling Gantt chart generation module is used to generate an initial scheduling Gantt chart of the manufacturing process, with the set of parts tasks in the pre-acquired production order as the vertical axis and the corresponding assembly line task sequence as the horizontal axis. The demand intensity coefficient acquisition module is used to extract the affected process dependency tree in the topology graph when a disturbance event occurs, evaluate the task delay cost of the task nodes in the process dependency tree, and output the demand intensity coefficient for manufacturing process rescheduling. The flexible scheduling Gantt chart acquisition module is used to synchronously adjust the process cycle deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient, so as to generate a flexible scheduling Gantt chart for the manufacturing process. The final scheduling instruction set generation module is used to confirm the final scheduling instruction set of the mining equipment by dynamically correcting the process path of the production status map and optimizing the process of the flexible scheduling Gantt chart.

[0006] In a preferred embodiment, when the topology generation module executes process connections based on equipment status data, material inventory data, and order information of mining equipment to construct a topology representing the production status of mining equipment, it is specifically used for: The equipment status data is analyzed into a mapping relationship between vibration spectrum characteristics, temperature rise curves, and fault codes; Establish a correlation matrix between the safety stock threshold and the material turnover path in the material inventory data; The process path tree of the mining equipment is generated based on the process connections of the order information; A topology graph of the mining equipment production status is constructed using the mapping relationship as vertices, the association matrix as edge weights, and the process path tree as the connection rule.

[0007] In a preferred embodiment, when the demand intensity coefficient acquisition module extracts the affected process dependency tree in the topology graph upon the occurrence of a disturbance event, it is specifically used for: When a disturbance event occurs, identify the disturbance task node triggered in the topology graph; Perform cross-level reverse tracing of the disturbance task nodes along the process path tree, and mark all associated task nodes with time coupling constraints in the cross-level reverse tracing. Construct a minimum influence domain subtree of the topology graph using the associated task nodes and the connected process constraint edges; The minimum influence domain subtree is used as the affected process dependency tree in the topology graph.

[0008] In a preferred embodiment, when the demand intensity coefficient acquisition module evaluates the task delay cost of task nodes within the process dependency tree and outputs the demand intensity coefficient for manufacturing process rescheduling, it is specifically used for: Obtain the depth of the process precedence relationship of the task node in the minimum influence domain subtree; The basic hysteresis coefficient is assigned to the process dependency tree based on the depth of the process preceding relationships. The task delay cost of the task node within the process dependency tree is obtained by superimposing the basic delay coefficient with the fault downtime penalty coefficient and the material supply interruption risk coefficient corresponding to the task node.

[0009] In a preferred embodiment, when the demand intensity coefficient acquisition module performs the operation of adding the basic delay coefficient to the fault downtime penalty coefficient and the material supply interruption risk coefficient corresponding to the task node to obtain the task delay cost of the task node within the process dependency tree, it is specifically used for: The historical average repair time of the fault corresponding to the task node is weighted by a preset equipment value weight table to obtain the fault downtime penalty coefficient corresponding to the task node. The replenishment time and shelf-life decay period of the materials corresponding to the task node are coupled to obtain the material supply interruption risk coefficient corresponding to the task node; The basic delay coefficient, the fault downtime penalty coefficient, and the material supply interruption risk coefficient are superimposed and normalized to obtain the task delay cost of the task node in the process dependency tree.

[0010] In a preferred embodiment, when the flexible scheduling Gantt chart acquisition module performs synchronous adjustment of process cycle deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient, it is specifically used for: A relaxation window for the cycle time of the manufacturing process is generated based on the demand intensity coefficient; Adjust the processing sequence of the mining equipment task queue within the relaxation window; The load overflow tasks of parallel equipment in the manufacturing process are dynamically allocated based on the processing sequence and the equipment utilization rate.

[0011] In a preferred embodiment, the flexible scheduling Gantt chart acquisition module, when generating the flexible scheduling Gantt chart for the manufacturing process, is specifically used for: Insert buffer time slots into the assembly line task sequence, and color-code the task blocks of the initial scheduling Gantt chart according to the load balancing of the parallel devices; The initial Gantt chart with colored markers is output as a flexible scheduling Gantt chart for the manufacturing process.

[0012] In a preferred embodiment, the dynamic correction of the process path includes: When the vibration spectrum characteristics of the equipment in the topology diagram exceed the preset warning value, the equipment node in the process path tree that is under warning will be switched to the backup equipment node. After switching the backup node, the time difference of the critical path in the process path tree is checked and fed back to the topology graph to obtain the process path correction topology graph of the manufacturing process.

[0013] In a preferred embodiment, before the final scheduling instruction set generation module confirms the final scheduling instruction set of the mining equipment by executing the process path dynamic correction based on the production status map and the process optimization results of the flexible scheduling Gantt chart, it is specifically used for: Verify the matching degree between the correction instruction set of the process path correction topology diagram and the material turnover path coordinates; When the matching degree exceeds the preset correction matching value, and the process cycle deviation is less than the maximum acceleration tolerance range of the equipment during the manufacturing process, a process parameter tolerance certificate of the correction instruction set is attached to the equipment.

[0014] To address the above problems, the present invention also provides a method for optimizing intelligent manufacturing production scheduling of mining equipment, the method comprising: S1. Based on the process connections of equipment status data, material inventory data and order information of mining equipment, construct a topology map representing the production status of mining equipment; S2. Using the component task set in the pre-acquired production order as the vertical axis and the corresponding final assembly line task sequence as the horizontal axis, generate the initial scheduling Gantt chart of the manufacturing process; S3. When a disturbance event occurs, extract the affected process dependency tree in the topology graph, evaluate the task delay cost of the task nodes in the process dependency tree, and output it as the demand intensity coefficient for manufacturing process rescheduling. S4. Based on the demand intensity coefficient, synchronously adjust the process cycle deviation and equipment utilization rate in the manufacturing process to generate a flexible scheduling Gantt chart for the manufacturing process; S5. Based on the dynamic correction of the process path in the production status map and the process optimization results of the flexible scheduling Gantt chart, confirm the final scheduling instruction set of the mining equipment.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention can construct a topology graph based on the process connections of equipment status data, material inventory data, and order information of mining equipment. It generates an initial scheduling Gantt chart using component task sets and assembly line task sequences. When a disturbance event occurs, it extracts the affected process dependency tree and evaluates the task delay cost, outputs the demand intensity coefficient for rescheduling, and then synchronously adjusts the process cycle deviation and equipment utilization to generate a flexible scheduling Gantt chart. Through dynamic correction of process paths and process optimization, it confirms the final scheduling instruction set, effectively improving the planning efficiency of the scheduling scheme for the manufacturing process of mining equipment.

[0016] 2. In the manufacturing process, this invention generates a flexible scheduling Gantt chart and combines it with dynamic correction of the process path to achieve synchronous adjustment of process cycle time and equipment utilization. It can quickly respond to and optimize the scheduling scheme when disturbance events occur. At the same time, by verifying the matching degree between the correction instruction set and the material turnover path, it ensures the accuracy and feasibility of the final scheduling instruction set, which significantly improves the execution efficiency and effectiveness of the production scheduling scheme for mining equipment manufacturing. Attached Figure Description

[0017] Figure 1 A system architecture diagram of a production scheduling optimization system for intelligent manufacturing of mining equipment provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating a production scheduling optimization method for intelligent manufacturing of mining equipment, provided as an embodiment of the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] 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 belong to some, but not all, embodiments of the present invention. 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.

[0020] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0021] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0022] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0023] In practice, the server-side equipment deployed in a mining equipment intelligent manufacturing production scheduling and optimization system may consist of one or more devices. This mining equipment intelligent manufacturing production scheduling and optimization system can be implemented as: a business instance, a virtual machine, or hardware devices. For example, this mining equipment intelligent manufacturing production scheduling and optimization system can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this mining equipment intelligent manufacturing production scheduling and optimization system can be understood as software deployed on a cloud node, used to provide a mining equipment intelligent manufacturing production scheduling and optimization system to various user terminals. Alternatively, this mining equipment intelligent manufacturing production scheduling and optimization system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, this mining equipment intelligent manufacturing production scheduling and optimization system can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide a mining equipment intelligent manufacturing production scheduling and optimization system to various user terminals.

[0024] In terms of implementation, the intelligent manufacturing production scheduling and optimization system for mining equipment and the user terminal are mutually compatible. That is, if the intelligent manufacturing production scheduling and optimization system for mining equipment is implemented as an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the intelligent manufacturing production scheduling and optimization system for mining equipment is implemented as a website, then the user terminal is implemented as a webpage; or if the intelligent manufacturing production scheduling and optimization system for mining equipment is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0025] like Figure 1 The figure shown is a system architecture diagram of a production scheduling optimization system for intelligent manufacturing of mining equipment provided in an embodiment of the present invention.

[0026] The intelligent manufacturing production scheduling optimization system 100 for mining equipment described in this invention can be set up in a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the intelligent manufacturing production scheduling optimization system 100 for mining equipment may include a topology graph generation module 101, an initial scheduling Gantt chart generation module 102, a demand intensity coefficient acquisition module 103, a flexible scheduling Gantt chart acquisition module 104, and a final scheduling instruction set generation module 105. The modules described in this invention can also be called units, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0027] In this embodiment of the invention, in a production scheduling optimization system for intelligent manufacturing of mining equipment, each of the above-mentioned modules can be implemented independently and can call other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the production scheduling optimization system for intelligent manufacturing of mining equipment provided by this embodiment of the invention, the applicable scope of the system architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the system. In practical applications, the above-mentioned modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in a cloud server.

[0028] The following describes, with reference to specific embodiments, each component and specific workflow of a production scheduling optimization system for intelligent manufacturing of mining equipment: The topology generation module 101 is used to construct a topology map representing the production status of the mining equipment based on the process connections of equipment status data, material inventory data and order information of the mining equipment. In this embodiment of the invention, when the topology generation module executes process connections based on equipment status data, material inventory data, and order information of mining equipment to construct a topology diagram representing the production status of mining equipment, it is specifically used for: The equipment status data is analyzed into a mapping relationship between vibration spectrum characteristics, temperature rise curves, and fault codes; Establish a correlation matrix between the safety stock threshold and the material turnover path in the material inventory data; The process path tree of the mining equipment is generated based on the process connections of the order information; A topology graph of the mining equipment production status is constructed using the mapping relationship as vertices, the association matrix as edge weights, and the process path tree as the connection rule.

[0029] Specifically, equipment status data is collected. Vibration sensors and temperature sensors are installed on key parts of the equipment. Vibration sensors acquire vibration signals during equipment operation, while temperature sensors monitor temperature changes in equipment components in real time. A fixed sampling frequency is set to ensure continuous collection of vibration and temperature data, including timestamps, during equipment operation, providing a raw data foundation for subsequent analysis.

[0030] Furthermore, the collected raw equipment status data is preprocessed. For vibration data, filtering is used to remove noise, such as using a low-pass filter to remove high-frequency noise and retaining effective signals related to the equipment vibration characteristics. For temperature data, outliers, such as sudden temperature jumps, are checked and discarded. Missing data is interpolated to ensure the continuity and accuracy of the temperature data, so that the preprocessed vibration and temperature data can more realistically reflect the operating status of the equipment.

[0031] Furthermore, vibration spectral features are extracted from the preprocessed vibration data. Using Fourier transform, the time-domain vibration signal is converted into a frequency-domain signal, obtaining the amplitude and phase information corresponding to each frequency component. Careful analysis of the frequency-domain signal identifies the main frequency components, such as power frequency, harmonics, and other possible characteristic frequencies. The amplitude values ​​corresponding to these frequency points are recorded. This frequency and amplitude information is used as the spectral features of the equipment vibration, providing crucial vibration characteristic basis for establishing a mapping relationship with fault coding in the future.

[0032] Furthermore, a temperature rise curve is generated based on the preprocessed temperature data. With time on the horizontal axis and temperature on the vertical axis, the temperature value corresponding to each time stamp is sequentially marked on the coordinate system. These points are then connected by a smooth curve to form a complete temperature rise curve. By observing this curve, the temperature change trend over time during equipment operation can be clearly seen, such as whether the temperature gradually and steadily rises, remains stable, or exhibits abnormal fluctuations. These trend characteristics are one of the important bases for judging whether the equipment has a malfunction.

[0033] Furthermore, establish a fault coding system. Based on the equipment type, structural characteristics, and common fault types, develop a unique and clear set of fault coding rules. Assign a specific code to each possible fault.

[0034] For example, bearing wear can be set as code 001, gear cracks as code 002, and motor overload as code 003.

[0035] Furthermore, detailed records are kept of the specific fault phenomena, possible causes, and impact on equipment operation corresponding to each fault code, ensuring a clear correspondence between fault codes and actual faults.

[0036] Furthermore, a mapping relationship is established between the extracted vibration spectrum features and the generated temperature rise curve features and the fault code.

[0037] Furthermore, we will conduct in-depth analysis of historical fault data to identify the combination patterns of vibration spectrum characteristics and temperature rise curve characteristics corresponding to different fault occurrences.

[0038] For example, when a bearing wear failure occurs in equipment, the vibration spectrum will show a significant increase in amplitude within a specific frequency range (such as near the bearing's characteristic frequency), while the temperature rise curve will exhibit a slow upward trend. Based on these characteristic combinations, a clear mapping is established between them and the corresponding fault code 001. In this way, a unique combination of vibration spectrum characteristics and temperature rise curve characteristics is constructed for each fault code, forming a complete mapping table.

[0039] Furthermore, the established mapping relationship between vibration spectrum characteristics, temperature rise curves, and fault codes is verified and optimized.

[0040] Furthermore, a large amount of historical equipment operation data, including normal operation data and known fault data, is selected and input into the established mapping relationship model to check whether the output fault codes are consistent with the actual fault conditions.

[0041] Furthermore, if a mapping error is found, carefully analyze whether it is due to inaccurate extraction of vibration spectrum features, incorrect judgment of temperature rise curve features, or unreasonable setting of fault code feature combination conditions. Adjust and optimize for specific problems, such as correcting the range of characteristic frequencies and adjusting the judgment criteria for temperature rise curve trends, until the accuracy of the mapping relationship meets the requirements of practical applications. Furthermore, the mapping relationship between the verified and optimized vibration spectrum characteristics, temperature rise curves, and fault codes is organized and output.

[0042] Furthermore, the document is presented in a clear and unambiguous format, detailing the specific frequency range and amplitude requirements of the vibration spectrum characteristics corresponding to each fault code, as well as the changing trend and rate of temperature change of the temperature rise curve. It also explains the basis and verification process for establishing each mapping relationship, ensuring that relevant personnel can accurately understand and use the mapping relationship. By analyzing the vibration spectrum characteristics and temperature rise curve of the equipment, the fault code of the equipment can be quickly and accurately determined, thereby enabling the implementation of corresponding repair and maintenance measures.

[0043] Specifically, collect safety stock threshold information and detailed information on material turnover paths from material inventory data. Extract the safety stock threshold for each material from the inventory management system. This threshold is the minimum inventory quantity set to prevent material shortages. At the same time, analyze the material turnover path through logistics process documents or information systems to clarify the complete flow route of materials from the source of procurement, through transportation, warehousing, production requisition, and other links until they are consumed. Record the operating entity, time span, and flow rules of each link.

[0044] Furthermore, the key factors influencing the safety stock threshold are analyzed and mapped to specific stages in the material turnover path. The safety stock threshold is mainly affected by factors such as fluctuations in material demand, supply cycle length, transportation reliability, and warehouse management efficiency. These factors are then matched with stages in the turnover path.

[0045] For example, fluctuations in material demand correspond to the accuracy of demand forecasting in the production requisition stage; the supply cycle corresponds to the duration of the entire supply chain from order placement to warehousing and acceptance; transportation reliability corresponds to the selection of logistics service providers and the stability of transportation methods in the transportation stage; and warehouse management efficiency corresponds to the frequency of inventory counting and the speed of inbound and outbound operations in the warehousing stage.

[0046] Furthermore, all key nodes in the material turnover path are identified and their specific attributes are clarified. Following the material flow sequence, the turnover path is broken down into nodes such as purchase order placement, supplier preparation, logistics transportation, warehousing and acceptance, storage, production requisition, and consumption completion. Each node is labeled with its sequence in the path, the time required for operation, potential risk points, and its connection with other nodes. For example, the warehousing and acceptance node requires verification of material quantity and quality; only materials that pass inspection can enter the storage node; otherwise, they are returned to the supplier for re-preparation.

[0047] Furthermore, the correlation between the safety stock threshold and the material turnover path nodes is determined.

[0048] Furthermore, for each safety stock influencing factor, we analyze the nodes involved in its turnover path. For example, the supply cycle factor involves several consecutive nodes such as purchase ordering, supplier preparation, logistics and transportation, and warehousing and acceptance. The total time spent on these nodes directly determines the length of the supply cycle. The longer the supply cycle, the higher the safety stock threshold required to avoid supply disruptions. As another example, the transportation reliability factor mainly involves logistics and transportation nodes. The higher the probability of delays or damages during transportation, the more quantity needs to be reserved in the safety stock to cope with possible supply interruptions.

[0049] Furthermore, the basic framework of the correlation matrix is ​​constructed, and the content of the rows and columns is defined. The influencing factors of the safety stock threshold are used as the rows of the matrix, including demand fluctuations, supply cycles, transportation reliability, and warehouse management efficiency. The key nodes of the material turnover path are used as the columns of the matrix, including purchase orders, supplier preparation, logistics and transportation, warehousing and acceptance, storage, and production requisition. The cells of the matrix are used to describe the correlation between the rows and columns, that is, how a certain influencing factor affects the safety stock threshold through the corresponding turnover path nodes.

[0050] Fill in the correlation matrix and explain the correlation logic in detail. In each cell, explicitly write the specific correlation between the factors affecting safety stock and the nodes in the turnover path.

[0051] For example, the cell at the intersection of the "Demand Fluctuation" row and the "Production Issuance" column shows that the deviation between actual and planned demand in the production issuance process directly reflects the degree of demand fluctuation. If emergency orders or order cancellations frequently occur during the issuance process, demand fluctuation will increase. To cope with this uncertainty, the safety stock threshold needs to be increased accordingly. The cell at the intersection of the "Supply Cycle" row and the "Logistics and Transportation" column shows that the length of transportation time is an important part of the supply cycle. Slow transportation speed will prolong the entire supply cycle, thus requiring a higher safety stock threshold to cover material consumption during supply delays.

[0052] Furthermore, the accuracy and completeness of the correlation matrix are verified and adjusted / optimized. Each correlation is checked against actual inventory management data and turnover path records to ensure it aligns with actual business conditions.

[0053] For example, check whether materials that frequently experience delays in the transportation process actually have high safety stock levels, or whether materials with high warehouse management efficiency have low safety stock thresholds due to fast inventory turnover. If a mismatch is found, such as a material with a long supply cycle but a safety stock threshold set too low, analyze whether it is due to incomplete sorting of turnover path nodes or insufficient consideration of influencing factors, and make targeted corrections to the matrix content to ensure that the setting of each safety stock threshold can find clear related nodes and functional logic in the turnover path.

[0054] Furthermore, a complete safety stock threshold and material turnover path correlation matrix is ​​ultimately formed and documented. The validated matrix is ​​presented in tabular form, with detailed explanations below each table, explaining the basis for establishing each correlation and its practical significance for inventory management. For example, it explains how the reliability of the transportation link affects supply stability and thus determines the safety stock reserve, or how demand fluctuations in the production requisition link are adjusted for safety stock thresholds through historical data statistics. This matrix serves as an inventory management tool, helping managers to rationally set safety stock by optimizing turnover path nodes, avoiding both inventory backlog and material shortages, and achieving a balance between inventory costs and supply assurance.

[0055] Specifically, we collect order information for mining equipment. This order information includes the equipment's specifications, functional requirements, delivery time, and detailed data such as production quantity and quality standards. We obtain this information completely from the business system or contract documents used to place the order, providing a basis for subsequent process planning.

[0056] Furthermore, analyze the various requirements in the order information. Carefully study the order, break down the various components of the equipment and their corresponding technical specifications. For example, for crusher equipment, clarify the specific parameters such as crushing chamber size, crushing force requirements, and motor power. Use these parameters as key inputs for subsequent process design to accurately grasp the core requirements of the order.

[0057] Furthermore, all technological processes involved in the production of mining equipment are systematically analyzed. Following the equipment manufacturing process, a series of steps are listed, from raw material procurement and inspection to component processing (such as cutting, forging, welding, and machining), then to component assembly, overall debugging, and quality inspection, clearly presenting the operational content and purpose of each process step.

[0058] Furthermore, suitable process steps are selected based on order requirements. By comparing the equipment specifications and performance requirements in the order, the appropriate process steps are chosen from the identified steps. For example, for high-precision mining equipment components, higher-precision CNC machine tool processing is selected, eliminating ordinary machine tool processing, ensuring that the selected process meets the order's requirements for equipment quality and performance.

[0059] Furthermore, the sequence of each process step is determined. Based on the actual process logic of mining equipment production and combined with the characteristics of the processes, the process sequence is arranged. For example, the parts are processed first, followed by assembly; for welding processes, they should be carried out after the parts are formed, forming an orderly process chain to ensure a smooth production process.

[0060] Furthermore, a tree structure is used to construct the process path. The final product of the mining equipment is taken as the root node of the process path tree. According to the determined process sequence, each process step is extended downward as a child node. Each child node represents a process step. If a process step contains multiple sub-steps, these sub-steps are then taken as the next level child nodes, nested layer by layer, to form a complete process path tree framework.

[0061] Furthermore, the process path tree is annotated with the relevant order information. At each node corresponding to a process step, the specific requirements and parameters related to the order information are marked. For example, at the component processing node, information such as the dimensional tolerances and surface roughness required by the order are marked, ensuring a close integration between the process path tree and the order information, allowing production personnel to intuitively understand the order requirements for each process step.

[0062] Furthermore, examine and refine the process path tree. Thoroughly review the constructed process path tree to check if the process sequence is reasonable, if each process step meets order requirements, and if the logical relationships between nodes are clear. If any inconsistencies are found, such as a reversed order of two process steps, adjust them promptly to ensure the process path tree accurately and completely reflects the mining equipment process path based on order information.

[0063] Specifically, the mapping relationships, association matrices, and process path trees generated in the previous steps are organized and extracted. The mapping relationships of vibration spectrum characteristics, temperature rise curves, and fault codes are obtained from the equipment status data analysis results, with each fault code and its corresponding feature combination serving as an independent mapping relationship unit. The association matrix between the safety stock threshold and the material turnover path is extracted from the material inventory data processing results, clarifying the correlation strength between each influencing factor and the turnover node in the matrix. The process path tree is extracted from the order information processing results, clarifying the sequence of process steps and node relationships in the mining equipment production process, thus preparing complete basic data for constructing the topology map.

[0064] Furthermore, the vertex structure of the topology graph is determined and assigned a unique identifier. Each fault code in the mapping relationship is used as a core vertex because each fault code corresponds to a specific combination of equipment state characteristics and is a key node reflecting potential problems in the equipment production process.

[0065] Furthermore, attribute information is added to each vertex, including the specific frequency range and amplitude requirements of the corresponding vibration spectrum characteristics, the changing trend of the temperature rise curve, and the rate of temperature change, to ensure that the vertex can accurately represent different states in equipment production and form a node entity with practical significance in the topology graph.

[0066] Furthermore, the association matrix is ​​transformed into edge weights of a topological graph, and the connection objects of the edges are defined. Each row in the association matrix represents the factors influencing the safety stock threshold, each column represents the key nodes in the material turnover path, and the matrix cells describe the relationship between the two.

[0067] Furthermore, in the topology graph, these relationships are transformed into edges connecting safety stock-related nodes and material turnover path nodes. The weights of the edges are set according to the relationship strength described in the relationship matrix. For example, relationships with high influence are given higher weight values, and relationships with low influence are given lower weight values, so that the edge weights can intuitively reflect the degree of interaction between the safety stock threshold and the turnover path nodes.

[0068] Furthermore, the connection rules of the topology graph are determined and node connections are established based on the structure of the process path tree. The process path tree takes the final product of the equipment as the root node and each process step as a child node to form a tree structure. In the topology graph, nodes representing different process steps are connected according to the sequence and logical relationship of the process steps in the process path tree. The nodes of the preceding process step point to the nodes of the following process step, forming directed edges with direction. This ensures that the connections in the topology graph can accurately reflect the flow sequence and dependency relationship of the process steps in the production process of mining equipment, making the entire production process clearly presented in the topology graph.

[0069] Furthermore, vertex, edge weights, and connection rules are integrated to construct a complete topology graph. All fault-coded vertices representing mapping relationships, weighted edges representing the association matrix, and node connections conforming to the process path tree connection rules are integrated. It is then checked whether each vertex is correctly associated with its corresponding feature attribute, whether the weight of each edge accurately reflects the association relationship in the association matrix, and whether the connections between nodes fully follow the order and logic of the process path tree. This ensures that every element in the topology graph accurately corresponds to the product of the previous steps, with no omissions or incorrect connections.

[0070] Furthermore, the consistency of the constructed mine equipment production status topology map is verified. This involves checking the completeness and accuracy of the feature attributes of each vertex against the mapping relationship, verifying that the weight setting of each edge matches the described association strength against the association matrix, and checking the correctness of the connection order and direction between nodes against the process path tree. For example, it confirms that the "parts processing" node in the process path precedes the "assembly" node and is correctly connected, and confirms that the edges between the safety stock threshold influencing factors and the turnover path nodes correspond to the specific association logic in the association matrix. This ensures that the topology map can truly and accurately reflect the interrelationships between equipment status, material inventory, and process flow during the mine equipment production process.

[0071] Furthermore, a topology map that intuitively displays the production status of mining equipment is ultimately created and documented. The topology map is presented graphically, with each vertex represented by a specific symbol and labeled with its corresponding fault code and characteristics. Each edge is indicated by an arrowed line representing the connection direction and labeled with its corresponding association weight. The documentation details the construction logic of the topology map, including how vertices are selected, edge weights are determined, and connection rules are applied. This allows the topology map to serve as a visualization tool, helping production managers quickly grasp potential fault risks, material inventory influencing factors, and process flow status during mining equipment production by observing vertex status, edge weights, and node connections. This provides an intuitive basis for production decisions and optimization.

[0072] In summary, constructing a production status topology map based on the process connections of equipment status data, material inventory data, and order information of mining equipment can improve scheduling efficiency from two aspects: multi-source data integration and production status visualization.

[0073] In summary, this topology map integrates equipment operating status, material flow logic, and order process requirements into a unified graph structure model by parsing equipment status data, establishing a correlation matrix between material inventory safety stock thresholds and turnover paths, and generating process path trees based on order information. This enables schedulers to fully grasp the real-time correlation status of various elements in the production system, providing accurate process logic support for the generation of the initial scheduling Gantt chart and avoiding scheduling decision deviations caused by data fragmentation.

[0074] In summary, the topology graph, with its construction mechanism of mapping relationships as vertices, correlation matrices as edge weights, and process path trees as connection rules, can dynamically represent the causal transmission path of the production status of mining equipment.

[0075] In summary, when a disturbance event occurs, the system can quickly locate the affected process dependency tree along the process path tree of the topology graph, accurately define the scope of the disturbance impact by tracing the task nodes of time coupling constraints across levels, and provide a structured data foundation for task delay cost assessment and demand intensity coefficient calculation.

[0076] In summary, this production situation modeling method based on topology graphs not only supports the dynamic switching of process paths and the correction of critical path time difference when equipment fails, but also provides a visualized correlation analysis framework for the synchronous adjustment of process cycle time and equipment utilization in flexible scheduling, thereby improving the response efficiency and global optimization capability of scheduling schemes to complex production environments.

[0077] The initial scheduling Gantt chart generation module 102 is used to generate an initial scheduling Gantt chart of the manufacturing process, with the set of parts tasks in the pre-acquired production order as the vertical axis and the corresponding assembly line task sequence as the horizontal axis. In this embodiment of the invention, a complete set of component tasks and the corresponding final assembly line task sequence are extracted from the pre-acquired production order.

[0078] Furthermore, the component task set includes all component-related tasks that need to be produced or procured in the order, such as "gear machining," "bearing procurement," and "housing welding." Each task is clearly marked with its task name, estimated start time, estimated completion time, required resources, and dependencies on other tasks. The final assembly line task sequence is a series of tasks that need to be completed during the equipment final assembly process, such as "component pre-assembly," "motor installation," and "overall debugging." These tasks are arranged according to the final assembly process sequence, and the operation content and time requirements of each task are recorded to provide basic data for Gantt chart generation.

[0079] Furthermore, the extracted set of component tasks is broken down and organized in detail. Each component task is divided into smaller sub-tasks according to the production process (if necessary), ensuring that each task is independently executable.

[0080] For example, "gear machining" can be broken down into sub-tasks such as "raw material preparation", "rough machining", "finish machining" and "heat treatment". At the same time, the person in charge of each task, the equipment or tools used, and the preconditions for task execution can be clearly defined. For example, the "heat treatment" task can only be started after the "finish machining" task is completed, making the task set of parts clearer and more specific.

[0081] Furthermore, the logical sequence and time nodes of the final assembly line tasks are analyzed. The order of tasks on the final assembly line is determined based on the mining equipment final assembly process specifications.

[0082] For example, "component pre-assembly" is carried out first, followed by "motor installation" and finally "overall debugging". At the same time, combined with historical production data or process standards, reasonable operation time is estimated for each final assembly task to ensure that the task sequence conforms to the actual production process and the time arrangement is scientific and reasonable, providing accurate task sequence and time basis for horizontal axis design.

[0083] Furthermore, a two-dimensional coordinate system for the Gantt chart is established, and the content of the coordinate axes is determined. The horizontal axis represents the task sequence of the final assembly line, and each task is labeled from left to right according to the execution order of the final assembly tasks, such as "component pre-assembly," "motor installation," and "overall debugging," with appropriate blank spaces left between adjacent tasks based on process requirements and time intervals. The vertical axis represents the set of component tasks, arranged from top to bottom according to the order of component usage or task priority during the final assembly process. For example, the tasks "gear processing" and "bearing procurement" required in the early stages of final assembly are listed first, followed by the tasks "shell welding" and "bolt assembly" required in the middle and later stages, so that the content of the horizontal and vertical axes clearly corresponds to the two types of task sets in the production order.

[0084] Furthermore, time scales are marked on the horizontal axis, and the time span is determined. Based on the expected start and finish times of all tasks in the final assembly line task sequence, the time range of the Gantt chart is determined, for example, from the order effective date to the expected final assembly completion date. This period is divided into daily or hourly time scales, evenly marked above or below the horizontal axis, ensuring that the time scales clearly reflect the time arrangement of each task and provide a unified time reference standard for subsequent task time allocation.

[0085] Furthermore, the component task set and the final assembly line task sequence are mapped to the coordinate system of the Gantt chart. For each component task on the vertical axis, a horizontal rectangle is drawn to the right of the corresponding task name on the vertical axis according to its expected start and finish times. The left end of the rectangle is aligned with the start time scale on the horizontal axis, and the right end is aligned with the finish time scale.

[0086] For example, if the "gear machining" task is expected to start on day 3 and be completed on day 7, draw a rectangular bar from the day 3 mark to the day 7 mark on the horizontal axis in the row corresponding to "gear machining" on the vertical axis. Similarly, for the assembly line tasks on the horizontal axis, draw a rectangular bar below the corresponding task name on the horizontal axis according to its time schedule to clarify the time range occupied by each task on the time axis.

[0087] Furthermore, handle the dependencies between tasks and adjust task scheduling. Check the dependencies between all component tasks and final assembly line tasks. If a component task is a prerequisite for a final assembly line task, ensure that the task in the component task's rectangle is completed before the start time of the final assembly line task's rectangle. For example, the "bearing procurement" task must be completed before the start of the "motor installation" task on the final assembly line. If time conflicts are found, such as the component task's estimated completion time being later than the start time of the final assembly line task, adjust the start time of the component task or increase resource input to shorten the completion time, ensuring that the scheduling of all tasks conforms to the actual production logic.

[0088] Furthermore, a comprehensive review and correction of the initial scheduling Gantt chart is conducted. This includes verifying that the name, time span, and dependencies of each task accurately reflect the production order requirements; checking that the position and length of the rectangles match the expected start and finish times; and confirming that the task arrangement along the vertical and horizontal axes is reasonable and easy to view. For example, checking whether the component tasks on the vertical axis are arranged according to the final assembly usage sequence, and whether there are any issues with the order or time overlap of the final assembly tasks on the horizontal axis. If any problems are found, the position or length of the rectangles is adjusted promptly to ensure that the Gantt chart accurately displays the initial scheduling plan for the manufacturing process.

[0089] Furthermore, a clear and intuitive initial manufacturing process scheduling Gantt chart is generated, along with accompanying explanatory documentation. The Gantt chart is presented as a visual chart, using different colors to distinguish between component tasks and final assembly line tasks. Below or beside the chart, the resource requirements, responsible persons, and special considerations for key tasks are labeled. For example, it may specify that the "shell welding" task requires a specific type of welding equipment, and the "overall debugging" task requires the participation of professional technicians. At the same time, the documentation may explain in detail the basis for drawing the Gantt chart, the method for estimating task time, and the principle for handling dependencies, so that production schedulers can quickly grasp the time arrangement and interrelationships of each task through the Gantt chart, providing clear guidance for subsequent production scheduling and resource allocation.

[0090] In summary, generating an initial scheduling Gantt chart by using the set of component tasks in the pre-acquired production orders as the vertical axis and the corresponding assembly line task sequence as the horizontal axis can improve scheduling efficiency in terms of both task correlation visualization and production rhythm coordination.

[0091] In summary, this Gantt chart directly links the production progress of parts with the assembly sequence of the final assembly line through a two-dimensional mapping of the component task set on the vertical axis and the final assembly line task sequence on the horizontal axis. This allows schedulers to intuitively grasp the time coupling relationship between each component task and the final assembly process, identify the timing conflicts between material supply and assembly demand in advance, avoid the final assembly line shutdown or inventory backlog caused by component delays, and achieve pre-allocation optimization of production resources.

[0092] In summary, the design with the final assembly line task sequence as the horizontal axis can deduce the latest start and end times of component tasks based on the order delivery cycle, forming a task timing constraint network based on the assembly mainline. This final assembly-oriented scheduling logic ensures that the component production rhythm is synchronized with the final assembly line takt time, reduces waiting time between processes, and provides a standardized time benchmark for subsequent flexible scheduling.

[0093] In summary, when a disturbance event occurs, the task blocks of the initial Gantt chart can quickly locate the affected component tasks and the nodes associated with the final assembly process, providing accurate spatiotemporal coordinate references for the extraction of the process dependency tree and the calculation of the demand intensity coefficient, thereby improving the efficiency of the entire process of scheduling scheme from initial planning to dynamic adjustment.

[0094] The demand intensity coefficient acquisition module 103 is used to extract the affected process dependency tree in the topology graph when a disturbance event occurs, evaluate the task delay cost of the task node in the process dependency tree, and output the demand intensity coefficient for manufacturing process rescheduling. In this embodiment of the invention, when the demand intensity coefficient acquisition module extracts the affected process dependency tree in the topology graph when a disturbance event occurs, it is specifically used for: When a disturbance event occurs, identify the disturbance task node triggered in the topology graph; Perform cross-level reverse tracing of the disturbance task nodes along the process path tree, and mark all associated task nodes with time coupling constraints in the cross-level reverse tracing. Construct a minimum influence domain subtree of the topology graph using the associated task nodes and the connected process constraint edges; The minimum influence domain subtree is used as the affected process dependency tree in the topology graph.

[0095] When the demand intensity coefficient acquisition module evaluates the task delay cost of task nodes within the process dependency tree and outputs the demand intensity coefficient for manufacturing process rescheduling, it is specifically used for: Obtain the depth of the process precedence relationship of the task node in the minimum influence domain subtree; The basic hysteresis coefficient is assigned to the process dependency tree based on the depth of the process preceding relationships. The task delay cost of the task node within the process dependency tree is obtained by superimposing the basic delay coefficient with the fault downtime penalty coefficient and the material supply interruption risk coefficient corresponding to the task node.

[0096] When the demand intensity coefficient acquisition module performs the process of adding the basic delay coefficient to the fault downtime penalty coefficient and the material supply interruption risk coefficient corresponding to the task node to obtain the task delay cost of the task node within the process dependency tree, it is specifically used for: The historical average repair time of the fault corresponding to the task node is weighted by a preset equipment value weight table to obtain the fault downtime penalty coefficient corresponding to the task node. The replenishment time and shelf-life decay period of the materials corresponding to the task node are coupled to obtain the material supply interruption risk coefficient corresponding to the task node; The basic delay coefficient, the fault downtime penalty coefficient, and the material supply interruption risk coefficient are superimposed and normalized to obtain the task delay cost of the task node in the process dependency tree.

[0097] Specifically, when a disturbance event occurs, such as a sudden equipment failure, a disruption in raw material supply, or a temporary change in order requirements, it is necessary to carefully refer to the previously constructed topology diagram. Each vertex in the topology diagram represents equipment status characteristics and task information; for example, a particular vertex may correspond to a specific fault code and its related production task. By examining each vertex one by one, it is determined which vertex's corresponding task is directly affected by the current disturbance event. For example, if a sudden equipment failure forces an ongoing production task to pause, then the vertex representing that fault code and its corresponding production task will be identified as the triggered disturbance task node. During the determination process, all vertices must be thoroughly reviewed to ensure that no task node directly affected by the disturbance event is missed, laying an accurate foundation for subsequent analysis.

[0098] Furthermore, for the identified disturbance task nodes, analysis is performed using the hierarchical structure and connection relationships of the process path tree.

[0099] Furthermore, the process path tree clearly displays the sequence and logical relationships of each process step in the mining equipment production process. Starting from the disturbance task node, the process path tree is traced backward to the next higher-level node. During the tracing process, the focus is on checking whether there are time coupling constraints between each traversed node and the disturbance task node.

[0100] Furthermore, temporal coupling constraints refer to the interdependence between the tasks represented by two nodes in terms of time, such as one task must start only after the other task is completed, or the two tasks have some overlap in execution time.

[0101] Furthermore, once a node with such temporal coupling constraints is identified, it is immediately marked as an associated task node and continuously tracked until it reaches the root node of the process path tree. Through this operation, all associated task nodes with temporal coupling constraints with the disturbing task node can be marked, thereby completely clarifying the task chain affected by the disturbance event and identifying which tasks will be affected by the disturbance.

[0102] Furthermore, from all the marked associated task nodes, select the nodes that are directly connected to the disturbance task node and have a process constraint relationship, and select the process constraint edges connecting these nodes.

[0103] Furthermore, process constraint edges in the topology graph reflect various process relationships between tasks, including the order of task execution (e.g., assembly can only proceed after component processing is completed); and resource sharing relationships (e.g., certain equipment or tools are used alternately in different tasks). Combining the selected associated task nodes and process constraint edges into an independent tree structure constitutes the minimum influence domain subtree of the topology graph.

[0104] Furthermore, the minimum impact domain subtree can accurately present the scope of the direct impact of a disturbance event on the production process path, as well as the relationship between various related tasks, helping relevant personnel to quickly understand the direct impact of the disturbance event.

[0105] Furthermore, the successfully constructed minimum influence domain subtree is identified as the affected process dependency tree in the topology graph, and then it is comprehensively organized and labeled.

[0106] Furthermore, in the process dependency tree, the specific task name corresponding to each node is clearly marked, such as "gear processing" or "motor installation". The task type is also noted, such as component production task or final assembly task. The specific dependency relationship between each node and the disturbance task node should also be clearly explained. For example, it should be explained whether the task is affected by the disturbance task due to the time sequence or due to resource sharing.

[0107] Furthermore, the process constraint edges should also be explained in detail, explaining the specific process relationship logic between the tasks represented by each edge. For example, an edge represents that the completion of the preceding task is a necessary condition for the start of the subsequent task. Only after the task corresponding to the starting node of this edge is completed can the task corresponding to the ending node be started.

[0108] Furthermore, through such detailed organization and annotation, the process dependency tree can clearly and intuitively demonstrate the impact of disturbance events on the entire production process path, providing a clear, explicit, and highly valuable basis for subsequently developing targeted countermeasures and rationally adjusting production plans. Specifically, in the constructed minimum impact domain subtree, starting from the root node, the root node usually represents the starting point of the production process or a key core task.

[0109] Furthermore, following the hierarchical structure of the tree, each task node is analyzed in detail from top to bottom, layer by layer.

[0110] Furthermore, the process prerequisite relationship specifically refers to the prerequisite tasks that must be completed before a certain task can be executed in the production process system of mining equipment.

[0111] Furthermore, during the analysis, for each task node, starting from that node, we backtrack towards the root node, carefully sorting out and counting the number of task nodes with immediate process relationships along this backtracking path.

[0112] Furthermore, the quantity obtained from this statistic is defined as the depth of the process precedence relationship of the task node in the minimum influence domain subtree.

[0113] For example, if a task node has 3 task nodes with immediate technological precedence on the path back to the root node, then the depth of the immediate technological precedence of this task node is 3.

[0114] Furthermore, by performing such rigorous analysis and statistical operations on each task node in the least influential subtree, the depth of the process prerequisite relationships of all task nodes can be determined comprehensively and accurately, laying a solid data foundation for subsequent work.

[0115] Furthermore, based on the process prerequisite relationship depth of each task node determined earlier, basic delay coefficients are assigned to the task nodes in the process dependency tree.

[0116] Furthermore, the basic delay coefficient, its core function is to measure the potential delay of each task due to the existence of the production process sequence.

[0117] Furthermore, a clear, explicit, and fixed set of correspondence rules is established. These rules explicitly define the relationship between the depth of the process predecessor relationship and the basic lag coefficient. Specifically, when the depth of the process predecessor relationship is 1, meaning the task node has only one task node with a process predecessor relationship to the root node, it is assigned a relatively low basic lag coefficient. As the depth of the process predecessor relationship gradually increases, the basic lag coefficient also gradually increases according to the established rules.

[0118] For example, the basic hysteresis coefficient for a node at depth 1 is explicitly set to 0.1, for a node at depth 2 to 0.2, for a node at depth 3 to 0.3, and so on. Strictly following this pre-defined rule, each task node in the process dependency tree is assigned its corresponding basic hysteresis coefficient.

[0119] Furthermore, through this operation, the potential delay differences faced by different tasks in the entire production process sequence can be clearly demonstrated, giving each task a quantitative measure of its potential delay.

[0120] Furthermore, for each task node within the process dependency tree, the task delay cost is calculated. Specifically, the basic delay coefficient already assigned to the task node is added together with two other important coefficients: the downtime penalty coefficient and the material supply interruption risk coefficient corresponding to the task node.

[0121] Furthermore, the downtime penalty coefficient is a value pre-set before production begins, taking into account factors such as the frequency of past equipment failures, the time required for failure repair, and the degree of delay that the failure may cause to the task. The material supply interruption risk coefficient is a value pre-set to reflect the possibility of the material supply interruption affecting the task, based on factors such as the stability of the material supplier's supply, the risk factors in the transportation process, and the importance of the material in the production process.

[0122] Furthermore, in actual calculations, these three coefficients are directly added together, and the sum obtained is determined as the task delay cost of that task node.

[0123] Furthermore, if the basic delay coefficient of a certain task node is calculated to be 0.3, the fault downtime penalty coefficient pre-set based on equipment status and historical fault data is 0.2, and the material supply interruption risk coefficient pre-set based on material supply status is 0.1, then by simple addition 0.3+0.2+0.1=0.6, the task delay cost of this task node is 0.6.

[0124] Furthermore, by performing such detailed and rigorous calculations on each task node within the process dependency tree, the accurate task delay cost can be determined for each task node. This task delay cost comprehensively considers the degree of delay impact under various potential risks such as process sequence, equipment failure, and material supply, providing an intuitive and highly valuable quantitative basis for enterprises to scientifically adjust production plans and effectively assess and respond to risks.

[0125] Specifically, retrieve all historical data of the faults corresponding to the task nodes from the enterprise's equipment maintenance record system.

[0126] Furthermore, the equipment maintenance record system records in detail the specific date and time of each equipment failure, as well as the date and time of the failure repair completion.

[0127] Furthermore, by subtracting the time of occurrence of the fault from the time it takes to complete each fault repair, we can obtain the repair time for each fault.

[0128] Furthermore, by summing up all the repair times for the same task node corresponding to the fault, and then dividing this total time by the total number of fault occurrences, the historical average repair time for the fault corresponding to that task node can be obtained.

[0129] Furthermore, from the pre-defined equipment value weight table, find the value weight corresponding to the equipment or equipment component related to the task node.

[0130] Furthermore, this equipment value weighting table is formulated by enterprises based on a comprehensive evaluation of various factors such as the purchase cost of the equipment, its importance in the production process, and the difficulty of maintenance. The higher the weight value, the more important the equipment is.

[0131] Furthermore, a weighted operation is then performed. For devices with high value weights, their corresponding historical average repair time is amplified by a larger proportion; for devices with low value weights, the historical average repair time is amplified by a smaller proportion or remains unchanged. After this processing, the failure downtime penalty coefficient corresponding to the task node is finally obtained. This coefficient can accurately reflect the severity of the impact of equipment failure on the task.

[0132] Furthermore, information on replenishment time and shelf-life decay period of materials corresponding to task nodes can be obtained from the enterprise's procurement management system and material information management system.

[0133] Furthermore, the procurement management system records in detail the issuance time of each material replenishment order and the actual delivery time of the materials to the warehouse. By subtracting the order issuance time from the material delivery time, the material replenishment time can be determined.

[0134] Furthermore, the material information management system contains characteristic description documents for each material, which clearly record the shelf-life degradation period of the material. At the same time, this period can be further verified and corrected by analyzing historical data on the performance changes of the material during past use.

[0135] Furthermore, after obtaining these two pieces of information, a coupled analysis is performed on replenishment time and shelf-life degradation period. First, the durations of the two are compared. If the replenishment time is close to 80% of the shelf-life degradation period, or exceeds it, it indicates a significant risk in the material supply process. This could lead to material shortages due to untimely supply, or the material might become unusable due to performance degradation after exceeding its shelf-life degradation period. Based on the severity of this risk and combined with the company's past experience in dealing with similar risks, it is quantified into a specific numerical value. This value is the material supply disruption risk coefficient corresponding to this task node, which directly reflects the likelihood of material supply interruption or quality degradation impacting the task.

[0136] Furthermore, the previously calculated basic delay coefficient, downtime penalty coefficient, and material supply interruption risk coefficient are superimposed. The specific values ​​of these three coefficients are added together sequentially to obtain a preliminary sum.

[0137] Furthermore, since the range and magnitude of these three coefficients may vary considerably for different task nodes, it is necessary to normalize this preliminary sum in order to more conveniently and accurately compare and analyze the task delay costs of different task nodes.

[0138] Furthermore, the maximum and minimum values ​​of the sum of coefficients from all task nodes are calculated. Further, for the sum of coefficients of each task node, the minimum value obtained earlier is subtracted from the sum, and the difference is then divided by the difference between the maximum and minimum values.

[0139] Furthermore, through such calculations, the sum of the coefficients of each task node can be transformed into a value between 0 and 1. This final value is the task delay cost of the task nodes within the process tree.

[0140] Furthermore, this task delay cost comprehensively considers the impact of multiple key factors on the task, such as process sequence, equipment failure, and material supply, and presents it in a standardized and unified numerical form. Based on this value, the company's production managers can more scientifically adjust production plans, rationally allocate production resources, and formulate effective risk management strategies for different task nodes.

[0141] In summary, when a disturbance event occurs, by extracting the affected process dependency tree from the topology graph and evaluating the task delay cost to output the demand intensity coefficient, scheduling efficiency can be improved in terms of both disturbance location accuracy and cost quantification decision-making.

[0142] In summary, by identifying disturbance task nodes and tracing back along the process path tree across levels to the associated task nodes of time coupling constraints, the system constructs a minimum impact domain subtree as the affected process dependency tree. This allows for precise location of the actual impact range of disturbance events, avoids indiscriminate rescheduling of the global production process, reduces computational resource consumption and scheduling response time, and enables rapid location and localized processing of disturbance events.

[0143] In summary, a basic delay coefficient is assigned based on the depth of the process precedence relationship, and a fault downtime penalty coefficient (combining historical average repair time and equipment value weight) and a material supply interruption risk coefficient (coupled with replenishment time and shelf-life decay period) are superimposed. After normalization, a quantitative indicator of task delay cost is formed, so that the demand intensity coefficient can objectively reflect the actual impact of disturbance events on the production process.

[0144] In summary, this coefficient provides a quantitative basis for subsequent rescheduling decisions, ensuring that scheduling resources are prioritized for critical task nodes with high delay costs, avoiding misallocation of scheduling resources due to subjective judgment, improving the pertinence and effectiveness of rescheduling schemes, and thus optimizing the overall execution efficiency of mining equipment manufacturing and production scheduling.

[0145] The flexible scheduling Gantt chart acquisition module 104 is used to synchronously adjust the process cycle deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient, so as to generate a flexible scheduling Gantt chart of the manufacturing process. In this embodiment of the invention, when the flexible scheduling Gantt chart acquisition module performs synchronous adjustment of the process cycle deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient, it is specifically used for: A relaxation window for the cycle time of the manufacturing process is generated based on the demand intensity coefficient; Adjust the processing sequence of the mining equipment task queue within the relaxation window; The load overflow tasks of parallel equipment in the manufacturing process are dynamically allocated based on the processing sequence and the equipment utilization rate.

[0146] When the flexible scheduling Gantt chart acquisition module generates the flexible scheduling Gantt chart for the manufacturing process, it is specifically used for: Insert buffer time slots into the assembly line task sequence, and color-code the task blocks of the initial scheduling Gantt chart according to the load balancing of the parallel devices; The initial Gantt chart with colored markers is output as a flexible scheduling Gantt chart for the manufacturing process.

[0147] Specifically, all order data related to mining equipment is exported from the enterprise order management system, including order time, product model, quantity and delivery period. At the same time, market demand analysis reports provided by the marketing department are collected, which cover industry trends, competitor dynamics and potential customer needs.

[0148] Furthermore, the order delivery deadline is compared with the current date to calculate the remaining time for the order, and the urgency is assessed in conjunction with the order quantity; market demand analysis reports are analyzed to statistically analyze the recent market demand and growth trend of similar products, and factors such as order urgency and market demand trend are comprehensively quantified into a demand intensity coefficient, the higher the value of this coefficient, the more urgent the demand.

[0149] Furthermore, a rule table is pre-defined to correspond the demand intensity coefficient with the relaxation window of the process cycle time, clearly specifying the size of the relaxation window corresponding to different coefficient ranges. For example, a coefficient of 0-0.3 corresponds to a relaxation window of ±20% of the reference cycle time. According to this rule, a relaxation window is determined for each process in the manufacturing process, providing a clear range for subsequent process time adjustments.

[0150] Furthermore, the production task queue of mining equipment was carefully reviewed, the specific procedures corresponding to each task were clarified, and the task queue was comprehensively analyzed in conjunction with the task time arrangement presented in the Gantt chart and the logical order of tasks specified in the process path tree, combined with the determined process cycle relaxation window.

[0151] Furthermore, by using project management software, critical path tasks in the production process are marked. These tasks directly affect product delivery time and cannot be adjusted arbitrarily. For non-critical path tasks, if the corresponding process has a large slack window and current production resources (such as manpower and equipment) are under pressure on other critical tasks, different adjustment schemes are simulated through software to assess the impact of postponing the task for different durations on overall production. The scheme that can alleviate resource shortages without affecting the overall schedule is selected, and the task start time is reasonably postponed.

[0152] Furthermore, during the adjustment process, the dependencies between tasks are repeatedly checked. The task association table is used to ensure that the completion time of the preceding task is earlier than the start time of the subsequent task. For example, the finishing process can only be carried out after the heat treatment process of the parts is completed. This ensures that the adjusted processing sequence strictly follows the production process logic and achieves the optimal allocation of production process time and resources.

[0153] Furthermore, through the enterprise equipment monitoring system, real-time operational data of each parallel device is collected, including device start-up, pause, operation, and failure times. The ratio of actual working time to available working time is accurately calculated to determine the device utilization rate. A device load threshold is set; when the device's workload exceeds 80% of its maximum processing capacity per unit time, it is determined that a load overflow is imminent; exceeding 100% indicates that a load overflow has already occurred.

[0154] Furthermore, for overloaded devices, transferable tasks are selected from their current task list, prioritizing tasks that are within the process cycle time slack window, whose delayed execution will not affect product delivery time, and which are not on the critical path.

[0155] Furthermore, an equipment capability matching table is established, which records in detail the processing technology type (such as cutting, welding, assembly), processing accuracy range, maximum processing size, and other parameters of each piece of equipment. The selected tasks are compared one by one with the capability parameters of other equipment with lower loads to ensure that the receiving equipment has the process and accuracy conditions to complete the task.

[0156] Furthermore, referring to the factory layout diagram, tasks are prioritized for allocation to nearby equipment to reduce material transportation time and costs. By simulating task allocation schemes multiple times, the allocation method that maximizes the load balance of each piece of equipment is selected to achieve dynamic load balancing of parallel equipment and improve overall production efficiency.

[0157] Specifically, in the established sequence of tasks on the final assembly line, buffer time slots are inserted based on the logical relationships between tasks and potential risks.

[0158] Furthermore, a comprehensive review of the final assembly line task sequence was conducted. The process path tree was used to clarify the sequence and dependencies of each task, and to identify the critical task nodes that have a significant impact on subsequent tasks and are prone to production interruptions due to equipment failures, material delays, or other factors.

[0159] Furthermore, for these critical task nodes, the required buffer time between them and subsequent tasks is assessed based on experience and historical production data, and a buffer time slot of appropriate length is inserted.

[0160] For example, if the assembly of a large component is followed immediately by the overall debugging task, a half-day buffer time slot is inserted between the two to take into account the precision adjustment issues that may occur during the component assembly process.

[0161] Furthermore, for non-critical tasks, shorter buffer time slots are inserted at appropriate locations based on the relaxation window of their process cycle and resource availability to enhance the flexibility and resilience of the entire production process.

[0162] Furthermore, based on the load balancing of the parallel devices, the task blocks in the initial scheduling Gantt chart are colored and marked.

[0163] Furthermore, real-time load data for each parallel device is obtained from the equipment monitoring system, and the load balance of all parallel devices is calculated.

[0164] Furthermore, the load balance is judged based on the evenness of the workload currently undertaken by each device, and the load balance is divided into different level ranges.

[0165] For example, a load difference within 10% of the equipment's maximum processing capacity is considered balanced, 10%-20% is considered slightly unbalanced, and more than 20% is considered severely unbalanced.

[0166] Furthermore, by referring to the initial scheduling Gantt chart, the device execution information corresponding to each task is found.

[0167] Furthermore, task blocks executed by devices with balanced load are marked in green, indicating that the devices are operating well and the load is reasonable; task blocks executed by slightly unbalanced devices are marked in yellow, indicating that the device load needs to be monitored; and task blocks executed by severely unbalanced devices are marked in red, warning that the device load is too high and may affect production progress.

[0168] Furthermore, by using different colored markers, production managers can quickly and intuitively understand the load status of each task-performing device.

[0169] Furthermore, the initial Gantt chart, after being processed with inserted buffer time slots and color markers, is output as a flexible scheduling Gantt chart for the manufacturing process.

[0170] Furthermore, the format of the processed Gantt chart can be adjusted and optimized using professional charting software or the Gantt chart generation function in the enterprise production management system.

[0171] Furthermore, this includes clearly labeling the name, start time, and end time of each task block, as well as the corresponding device information and the location and duration of the buffer time slot.

[0172] Furthermore, the chart title bar is labeled "Flexible Scheduling Gantt Chart," and a legend is added to explain in detail the meaning of the device load balance represented by different color markers.

[0173] Furthermore, the optimized Gantt chart can be output in various formats such as PDF, image, or system-built-in formats, making it convenient for production managers to display, print and distribute on the shop floor, as well as archive and analyze it in the production management system. This makes the flexible scheduling Gantt chart an effective tool for guiding production scheduling, monitoring production progress, and adjusting production plans.

[0174] In summary, adjusting process cycle time deviation and equipment utilization based on the demand intensity coefficient to generate a flexible scheduling Gantt chart can improve scheduling efficiency in three aspects: dynamic response, resource optimization, and disturbance resistance. The demand intensity coefficient, as a quantitative indicator of task delay costs under disturbance events, accurately reflects the urgency of production scheduling adjustments: by generating a relaxation window for process cycle time, task time can be flexibly adjusted without affecting the critical path, avoiding process backlogs caused by rigid scheduling; adjusting the processing sequence of task queues based on the relaxation window can eliminate time coupling waste between processes and shorten non-value-adding time.

[0175] In summary, when adjusting equipment utilization synchronously, the load overflow tasks of parallel equipment can be dynamically allocated to divert the tasks of overloaded equipment to idle equipment, thus avoiding idle equipment resources and capacity bottlenecks. Inserting buffer time slots into the task sequence of the final assembly line can reserve time margin for subsequent processes to cope with disturbances. The coloring and marking mechanism based on equipment load balancing can intuitively present the resource occupancy status of task blocks, helping schedulers to quickly identify efficiency bottlenecks.

[0176] In summary, this mechanism enables the scheduling scheme to combine the execution accuracy under rigid constraints with the adaptive capability under flexible disturbances. Through the collaborative optimization of process and equipment resources, it achieves a dynamic balance between production cycle time and equipment capacity, thereby improving the response efficiency and execution effectiveness of the scheduling scheme in complex manufacturing environments.

[0177] The final scheduling instruction set generation module 105 is used to confirm the final scheduling instruction set of the mining equipment through the dynamic correction of the process path in the production status map and the process optimization results of the flexible scheduling Gantt chart.

[0178] In this embodiment of the invention, the dynamic correction of the process path includes: When the vibration spectrum characteristics of the equipment in the topology diagram exceed the preset warning value, the equipment node in the process path tree that is under warning will be switched to the backup equipment node. After switching the backup node, the time difference of the critical path in the process path tree is checked and fed back to the topology graph to obtain the process path correction topology graph of the manufacturing process.

[0179] Before the final scheduling instruction set generation module executes the process path dynamic correction based on the production status map and the process optimization results of the flexible scheduling Gantt chart to confirm the final scheduling instruction set of the mining equipment, it is specifically used for: Verify the matching degree between the correction instruction set of the process path correction topology diagram and the material turnover path coordinates; When the matching degree exceeds the preset correction matching value, and the process cycle deviation is less than the maximum acceleration tolerance range of the equipment during the manufacturing process, a process parameter tolerance certificate of the correction instruction set is attached to the equipment.

[0180] Specifically, in the established sequence of tasks on the final assembly line, buffer time slots are inserted based on the logical relationships between tasks and potential risks.

[0181] Furthermore, starting with the process path tree, we comprehensively sorted out the sequence and dependencies of each task in the final assembly line task sequence. For example, motor installation can only be carried out after the pre-assembly of components is completed, and overall debugging can only be carried out after the motor installation is completed.

[0182] Furthermore, by utilizing historical production records and experience data, key task nodes that have a significant impact on subsequent tasks and are prone to production interruptions due to factors such as equipment failures and material supply delays are identified.

[0183] Furthermore, taking the assembly of a mining crusher as an example, if the task of assembling the crushing chamber is delayed, it will directly affect the subsequent commissioning and delivery of the entire machine, which is the critical task node.

[0184] Furthermore, for these critical task nodes, the required buffer time is assessed and inserted into the corresponding buffer time slots by combining the average time fluctuation of similar tasks in the past and the statistics of equipment failure repair time.

[0185] Furthermore, in the crushing chamber assembly and overall machine debugging, an 8-hour buffer time slot is inserted based on the average delay time caused by component precision adjustment in historical data.

[0186] Furthermore, for non-critical tasks, short buffer time slots can be inserted at appropriate locations based on the relaxation window of the process cycle and the current idle time of manpower, equipment and other resources. For example, some auxiliary component installation tasks can be inserted with a 2-4 hour buffer time slot, thereby enhancing the flexibility and anti-interference ability of the production process to cope with emergencies.

[0187] Furthermore, based on the load balancing of parallel devices, the initial scheduling Gantt chart task blocks are color-marked.

[0188] Furthermore, the system retrieves real-time operational data for each parallel device from the enterprise's equipment monitoring system, including information such as device startup, pause, runtime, and current task list.

[0189] Furthermore, by comparing the workload of each device in the same time period, the degree of equipment load balance can be determined.

[0190] Furthermore, the load balancing level is specifically divided into ranges. If the difference between the task load and the maximum processing capacity of each device is within 10%, it is considered balanced; a difference between 10% and 20% indicates slight imbalance; and a difference exceeding 20% ​​indicates severe imbalance. Refer to the initial scheduling Gantt chart to view the device execution information for each task block.

[0191] Furthermore, task blocks executed by load-balanced equipment are marked in green, visually indicating stable equipment operation and reasonable load. Task blocks executed by slightly unbalanced equipment are marked in yellow, reminding production managers to pay attention to equipment load changes and take timely measures. Task blocks executed by severely unbalanced equipment are marked in red, warning that the equipment is overloaded, which may cause production delays, and task allocation needs to be adjusted as soon as possible. Through this color-coding method, production managers can quickly identify the load status of each task-executing device from the Gantt chart, providing a visual basis for production scheduling.

[0192] Furthermore, the initial Gantt chart, after completing the insertion buffer time slot and coloring mark processing, is transformed into a flexible scheduling Gantt chart for the manufacturing process.

[0193] Furthermore, utilize professional charting software such as Microsoft Visio, dedicated Gantt chart drawing tools, or the Gantt chart generation module built into the enterprise production management system to optimize the Gantt chart format. Clearly label each task block on the Gantt chart with its name, specific start time, and end time, accurate to the hour or minute.

[0194] Furthermore, the device number, model, and other information corresponding to each task are labeled, along with the start and end times and duration of the buffer time slot.

[0195] Furthermore, the chart title bar should prominently display "Manufacturing Process Flexible Scheduling Gantt Chart," and legends should be added to the side or bottom of the chart to explain in detail the equipment load balance status and meaning represented by green, yellow, and red markers, respectively.

[0196] Furthermore, the optimized Gantt chart can be output as a PDF for easy printing and archiving, as an image for easy display on electronic screens in the workshop, or directly saved as a built-in format of the production management system. This allows managers to easily access, view, and analyze the data in the system at any time, making the flexible scheduling Gantt chart a truly efficient and visual tool for guiding production scheduling, monitoring production progress in real time, and adjusting production plans in a timely manner.

[0197] Specifically, the correction instruction set and material turnover path coordinates of the process path correction topology map are obtained. The correction instruction set is extracted from the generated process path correction topology map. This instruction set contains a series of process adjustment instructions generated by correction operations such as equipment node switching, such as adjusting equipment operating parameters and changing the task execution order. At the same time, the material turnover path coordinates are retrieved from the material inventory management system and logistics information system. These coordinates record in detail the specific location and flow trajectory information of materials in the procurement, transportation, warehousing, and production requisition stages.

[0198] Furthermore, it is essential to ensure that the obtained correction instruction set and material turnover path coordinates are complete and accurate, providing a reliable data foundation for subsequent matching degree verification.

[0199] Furthermore, the matching degree of the correction instruction set of the process path correction topology diagram and the material turnover path coordinates is verified.

[0200] Furthermore, each instruction in the instruction set is analyzed and corrected one by one to determine whether it will affect the material turnover path.

[0201] For example, if the correction instruction set includes adjustments to the processing technology of a certain component, then it is necessary to check whether the procurement, transportation, and warehousing stages of that component in the material turnover path need to be adjusted accordingly. The impact of the correction instruction set on the material turnover path should be compared with the material turnover path coordinates to check whether the two are consistent in logic and operation.

[0202] Furthermore, according to the pre-defined matching degree calculation rules, the number of items that match between the two is counted and compared with the total number of items to obtain a specific matching degree value.

[0203] For example, if the correction instruction set has 10 items, and 8 of them match the material turnover path coordinates, then the matching degree is 80%.

[0204] Furthermore, the calculated matching degree is compared with the preset corrected matching value, and the process cycle deviation is checked to see if it is less than the maximum acceleration tolerance range of the equipment during the manufacturing process.

[0205] Furthermore, the preset correction matching value is a standard value set by the enterprise based on production experience and process requirements. It is used to measure whether the degree of matching between the correction instruction set and the material turnover path coordinates is qualified. The process cycle deviation refers to the difference between the actual process cycle after the correction operation and the original planned process cycle. It can be obtained by comparing the process time arrangement before and after the process path correction. The maximum acceleration tolerance range of the equipment during the manufacturing process is recorded in the equipment's technical parameter specification, representing the maximum degree of acceleration change that the equipment can withstand.

[0206] Furthermore, when the matching degree exceeds the preset correction matching value and the process cycle deviation is less than the maximum acceleration tolerance range of the equipment, it indicates that the correction operation will not have an adverse impact on material turnover and equipment operation.

[0207] Furthermore, provided that the matching degree and process cycle deviation conditions are met, a process parameter tolerance certificate for the equipment with a correction instruction set is added.

[0208] Furthermore, a process parameter tolerance certificate is a document that proves that the equipment can still operate normally and meet production requirements after the process parameters are adjusted by executing the correction instruction set.

[0209] Furthermore, the certificate includes information such as the equipment name, model, specific content of the correction instruction set, adjustment range of process parameters, and verification results.

[0210] Furthermore, using the company's standardized certificate template, accurately and completely fill in the relevant information and affix the company seal or a dedicated verification seal to ensure the certificate's validity and authority. Link the certificate to the equipment file and send it to production managers and equipment operators so they can confirm during production that adjustments to the equipment's process parameters are authorized and can confidently execute the relevant operations of the correction instruction set.

[0211] Specifically, the real-time monitoring function of the production management system is used to collect the completion progress of the correction instruction set and the offset of the flexible scheduling Gantt chart.

[0212] Furthermore, sensors and data acquisition terminals are installed on the equipment at the production site. These devices record information such as the operating status of the equipment and the execution of tasks in real time, and transmit the data to the production management system.

[0213] Furthermore, based on this data, the system compares it with the task requirements in the revised instruction set, counts the number of completed instructions and the progress of instructions currently being executed, and thus determines the completion progress of the revised instruction set.

[0214] Furthermore, the actual start time and completion time of each task in the flexible scheduling Gantt chart are compared with the planned time to calculate the time deviation of each task. By combining the time deviations of all tasks, the offset of the flexible scheduling Gantt chart is determined.

[0215] For example, if a task is scheduled to start in the 10th hour but actually starts in the 12th hour, then the time deviation of the task is 2 hours. The combined deviation of multiple tasks is the offset of the Gantt chart.

[0216] Furthermore, the calculated flexible scheduling Gantt chart offset is compared with the preset tolerance. The preset tolerance is an allowable deviation range pre-set by the enterprise based on production experience and process requirements, used to measure whether the production schedule meets expectations. When the offset exceeds the preset tolerance, it indicates a significant deviation in the production schedule, at which point a new disturbance event is identified.

[0217] Furthermore, the system immediately triggers an alarm mechanism, sending a notification to production management personnel and simultaneously feeding back information about the new disturbance event to the demand intensity coefficient acquisition module. Upon receiving the information, the demand intensity coefficient acquisition module reassesses and calculates the demand intensity coefficient based on current order status, market demand, and other factors. Based on the new demand intensity coefficient and the specific details of the disturbance event, it generates a new instruction set for handling the disturbance event. This instruction set includes countermeasures for the new disturbance event, such as adjusting task priorities, reallocating resources, and changing process routes.

[0218] Furthermore, the instruction set for handling the latest disturbance events is transmitted to the manufacturing process.

[0219] Furthermore, through the enterprise's internal network, the latest set of instructions for handling disturbance events is sent to various terminals of the production management system, including control panels on the shop floor, control systems of production equipment, and work computers of production management personnel.

[0220] Furthermore, after receiving the instruction set, production management personnel organize on-site operators to adjust the production plan and process according to the instructions.

[0221] For example, if the instruction set requires adjusting task priorities, administrators will rearrange the task order of each device; if resources need to be reallocated, idle resources will be allocated to critical tasks.

[0222] Furthermore, after receiving the instruction, the control system of the production equipment automatically adjusts the operating parameters of the equipment to ensure that the equipment produces according to the new process requirements, thereby enabling the entire manufacturing process to respond to new disturbance events in a timely manner and restore normal production order.

[0223] In summary, by dynamically correcting the process path in the production status map and coordinating the process optimization results of the flexible scheduling Gantt chart to confirm the final scheduling instruction set, scheduling efficiency can be improved in terms of both real-time performance and accuracy.

[0224] In summary, dynamic process path correction switches standby equipment nodes in real time based on equipment status data (such as vibration spectrum characteristics and fault codes) and corrects critical path time differences to ensure that the production process path can be quickly adjusted when equipment is abnormal, avoiding process stoppages caused by equipment failure and maintaining the continuity of the production process. On the other hand, the flexible scheduling Gantt chart optimizes task processing timing and equipment utilization by adjusting process cycle deviation, allocating tasks for parallel equipment load overflow, and inserting buffer time slots, making the scheduling scheme flexible in response to disturbance events.

[0225] In summary, when combined, the process path correction topology ensures the technological feasibility of scheduling instructions, while the process optimization results of the flexible scheduling Gantt chart guarantee resource allocation efficiency. By verifying the matching degree between the correction instruction set and the material turnover path, and by adding process parameter tolerance licenses, parameter conflicts and material supply interruption risks during scheduling execution are further eliminated. This collaborative mechanism can quickly respond to production disturbances, reduce task delay costs, and achieve a dynamic balance between process cycle time and equipment load, thereby improving the overall execution efficiency and stability of the mining equipment manufacturing production scheduling scheme.

[0226] Reference Figure 2 The diagram shown is a flowchart illustrating a production scheduling optimization method for intelligent manufacturing of mining equipment according to an embodiment of the present invention. In this embodiment, the production scheduling optimization method for intelligent manufacturing of mining equipment includes: S1. Based on the process connections of equipment status data, material inventory data and order information of mining equipment, construct a topology map representing the production status of mining equipment; S2. Using the component task set in the pre-acquired production order as the vertical axis and the corresponding final assembly line task sequence as the horizontal axis, generate the initial scheduling Gantt chart of the manufacturing process; S3. When a disturbance event occurs, extract the affected process dependency tree in the topology graph, evaluate the task delay cost of the task nodes in the process dependency tree, and output it as the demand intensity coefficient for manufacturing process rescheduling. S4. Based on the demand intensity coefficient, synchronously adjust the process cycle deviation and equipment utilization rate in the manufacturing process to generate a flexible scheduling Gantt chart for the manufacturing process; S5. Based on the dynamic correction of the process path in the production status map and the process optimization results of the flexible scheduling Gantt chart, confirm the final scheduling instruction set of the mining equipment.

[0227] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0228] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A production scheduling and optimization system for intelligent manufacturing of mining equipment, characterized in that, The system includes a topology graph generation module, an initial scheduling Gantt chart generation module, a demand intensity coefficient acquisition module, a flexible scheduling Gantt chart acquisition module, and a final production scheduling instruction set generation module, wherein: The topology generation module is used to construct a production status topology diagram of the mining equipment based on the equipment status data, material inventory data, and order information of the mining equipment, including: The equipment status data is analyzed into a mapping relationship between vibration spectrum characteristics, temperature rise curves, and fault codes; Establish a correlation matrix between the safety stock threshold and the material turnover path in the material inventory data; The process path tree of the mining equipment is generated based on the process connections of the order information; The production status topology of the mining equipment is constructed using the mapping relationship as vertices, the association matrix as edge weights, and the process path tree as the connection rule. The initial scheduling Gantt chart generation module is used to generate an initial scheduling Gantt chart of the manufacturing process, with the set of parts tasks in the pre-acquired production order as the vertical axis and the corresponding assembly line task sequence as the horizontal axis. The demand intensity coefficient acquisition module is used to extract the affected process dependency tree in the topology graph when a disturbance event occurs, evaluate the task delay cost of the task node in the process dependency tree, and output the demand intensity coefficient for manufacturing process rescheduling. The flexible scheduling Gantt chart acquisition module is used to synchronously adjust the process cycle deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient, so as to generate a flexible scheduling Gantt chart for the manufacturing process. The final production scheduling instruction set generation module is used to confirm the final production scheduling instruction set of the mining equipment by dynamically correcting the process path of the production status topology map and optimizing the process of the flexible scheduling Gantt chart.

2. The intelligent manufacturing production scheduling optimization system for mining equipment as described in claim 1, characterized in that, When the demand intensity coefficient acquisition module extracts the affected process dependency tree in the topology graph in the event of a disturbance, it is specifically used for: When a disturbance event occurs, identify the disturbance task node triggered in the topology graph; Perform cross-level reverse tracing of the disturbance task nodes along the process path tree, and mark all associated task nodes with time coupling constraints in the cross-level reverse tracing. Construct a minimum influence domain subtree of the topology graph using the associated task nodes and the connected process constraint edges; The minimum influence domain subtree is used as the affected process dependency tree in the topology graph.

3. The intelligent manufacturing production scheduling optimization system for mining equipment as described in claim 2, characterized in that, When the demand intensity coefficient acquisition module evaluates the task delay cost of task nodes within the process dependency tree and outputs the demand intensity coefficient for manufacturing process rescheduling, it is specifically used for: Obtain the depth of the process precedence relationship of the task node in the minimum influence domain subtree; The basic hysteresis coefficient is assigned to the process dependency tree based on the depth of the process preceding relationships. The task delay cost of the task node within the process dependency tree is obtained by superimposing the basic delay coefficient with the fault downtime penalty coefficient and the material supply interruption risk coefficient corresponding to the task node.

4. The intelligent manufacturing production scheduling optimization system for mining equipment as described in claim 3, characterized in that, When the demand intensity coefficient acquisition module performs the process of adding the basic delay coefficient to the fault downtime penalty coefficient and the material supply interruption risk coefficient corresponding to the task node to obtain the task delay cost of the task node within the process dependency tree, it is specifically used for: The historical average repair time of the fault corresponding to the task node is weighted by a preset equipment value weight table to obtain the fault downtime penalty coefficient corresponding to the task node. The replenishment time and shelf-life decay period of the materials corresponding to the task node are coupled to obtain the material supply interruption risk coefficient corresponding to the task node; The basic delay coefficient, the fault downtime penalty coefficient, and the material supply interruption risk coefficient are superimposed and normalized to obtain the task delay cost of the task node in the process dependency tree.

5. The intelligent manufacturing production scheduling optimization system for mining equipment as described in claim 1, characterized in that, When the flexible scheduling Gantt chart acquisition module performs synchronous adjustment of process cycle deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient, it is specifically used for: A relaxation window for the cycle time of the manufacturing process is generated based on the demand intensity coefficient; Adjust the processing sequence of the mining equipment task queue within the relaxation window; The load overflow tasks of parallel equipment in the manufacturing process are dynamically allocated based on the processing sequence and the equipment utilization rate.

6. The intelligent manufacturing production scheduling optimization system for mining equipment as described in claim 5, characterized in that, When the flexible scheduling Gantt chart acquisition module generates the flexible scheduling Gantt chart for the manufacturing process, it is specifically used for: Insert buffer time slots into the assembly line task sequence, and color-code the task blocks of the initial scheduling Gantt chart according to the load balancing of the parallel devices; The initial Gantt chart with colored markers is output as a flexible scheduling Gantt chart for the manufacturing process.

7. The intelligent manufacturing production scheduling optimization system for mining equipment as described in claim 1, characterized in that, The dynamic correction of the process path includes: When the vibration spectrum characteristics of the equipment in the topology diagram exceed the preset warning value, the equipment node in the process path tree that is under warning will be switched to the backup equipment node. The time difference of the critical path in the process path tree after switching the backup equipment node is checked and fed back to the topology graph to obtain the process path correction topology graph of the manufacturing process.

8. The intelligent manufacturing production scheduling optimization system for mining equipment as described in claim 7, characterized in that, Before the final production scheduling instruction set generation module executes the process path dynamic correction based on the production status map and the process optimization results of the flexible scheduling Gantt chart to confirm the final scheduling instruction set of the mining equipment, it is specifically used for: Verify the matching degree between the correction instruction set of the process path correction topology diagram and the material turnover path coordinates; When the matching degree exceeds the preset correction matching value, and the process cycle deviation is less than the maximum acceleration tolerance range of the equipment during the manufacturing process, a process parameter tolerance certificate of the correction instruction set is attached to the equipment.

9. A method for optimizing intelligent manufacturing production scheduling of mining equipment, characterized in that, The method includes: S1. Based on the process relationships of equipment status data, material inventory data, and order information of the mining equipment, construct a production status topology diagram of the mining equipment, including: The equipment status data is analyzed into a mapping relationship between vibration spectrum characteristics, temperature rise curves, and fault codes; Establish a correlation matrix between the safety stock threshold and the material turnover path in the material inventory data; The process path tree of the mining equipment is generated based on the process connections of the order information; The production status topology of the mining equipment is constructed using the mapping relationship as vertices, the association matrix as edge weights, and the process path tree as the connection rule. S2. Using the component task set in the pre-acquired production order as the vertical axis and the corresponding final assembly line task sequence as the horizontal axis, generate the initial scheduling Gantt chart of the manufacturing process; S3. When a disturbance event occurs, extract the affected process dependency tree in the topology graph, evaluate the task delay cost of the task nodes in the process dependency tree, and output it as the demand intensity coefficient for manufacturing process rescheduling. S4. Based on the demand intensity coefficient, synchronously adjust the process cycle deviation and equipment utilization rate in the manufacturing process to generate a flexible scheduling Gantt chart for the manufacturing process; S5. Based on the dynamic correction of the process path in the production status topology diagram and the process optimization results of the flexible scheduling Gantt chart, confirm the final production scheduling instruction set of the mining equipment.

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