Mining equipment intelligent manufacturing production scheduling optimization system and method

Abstract

The invention relates to the technical field of data reasoning, and discloses a mining 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 strength coefficient acquisition module, a flexible scheduling Gantt chart acquisition module and a final scheduling instruction set generation module; constructing a topological graph based on the process relation of the mining equipment; generating an initial scheduling Gantt chart by taking the part task set as a longitudinal axis and the corresponding general assembly line task sequence as a transverse axis; when a disturbance event occurs, an affected process dependence tree is extracted, task delay cost is evaluated, and a requirement strength coefficient for rescheduling in the manufacturing process is output; generating a flexible scheduling Gantt chart based on the process beat deviation and the equipment utilization rate in the synchronous adjustment manufacturing process; determining a final scheduling instruction set through process path dynamic correction and process optimization results; the efficiency of the mining equipment manufacturing and production process scheduling scheme can be improved.

Description

Technical Field

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

[0002] In the manufacturing process of mining equipment, the existing production scheduling scheme fails to fully combine the process connection of equipment status data, material inventory data and order information to construct an effective production situation topology map, making it difficult to accurately represent the production situation of mining equipment. As a result, in the initial scheduling stage, the Gantt chart generated by the component task set and the assembly line task sequence cannot efficiently match the actual production needs, making it difficult to coordinately optimize the process rhythm and equipment utilization, resulting in low efficiency of the production scheduling scheme.

[0003] When a disturbance occurs during the production process, existing technologies are unable to quickly extract the affected process dependency tree from the topology diagram, nor can they accurately assess the delay costs of task nodes. This results in inaccurate calculation of the demand intensity coefficient for rescheduling, making it impossible to synchronously adjust process cycle deviations and equipment utilization based on this coefficient to generate a flexible scheduling Gantt chart. Furthermore, existing technologies are ineffective in dynamically correcting process paths and optimizing processes. The synergy between the process path correction topology diagram and the flexible scheduling Gantt chart cannot be used to determine the final scheduling instruction set, resulting in ineffective scheduling solutions for mining equipment manufacturing. Summary of the Invention

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

[0005] To achieve the above-mentioned purpose, the present invention provides a mining equipment intelligent manufacturing production scheduling optimization system, characterized in that the system includes a topology map 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 map generating module is used to construct a topology map representing the production status of mining equipment based on the equipment status data, material inventory data and process connection of order information of mining equipment; The initial scheduling Gantt chart generation module is used to generate an initial scheduling Gantt chart for the manufacturing process using the component task set 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 configured to extract the affected process dependency tree in the topology diagram when a disturbance event occurs, evaluate the task delay costs of the task nodes in the process dependency tree, and output the demand intensity coefficient for rescheduling the manufacturing process; The flexible scheduling Gantt chart acquisition module is used to synchronously adjust the process beat deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient 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 through the dynamic correction of the process path of the production situation map and the process optimization result of the flexible scheduling Gantt chart.

[0006] In a preferred embodiment, the topology map generation module, when executing process connections based on equipment status data, material inventory data, and order information of mining equipment to construct a topology map representing the production status of mining equipment, is specifically configured to: Analyze the equipment status data into the mapping relationship between vibration spectrum characteristics, temperature rise curve and fault code; Establish a correlation matrix between safety stock threshold and material turnover path in material inventory data; generating a process path tree for the mining equipment based on the process connection of the order information; A topological diagram of the production situation of the mining equipment is constructed with the mapping relationship as the vertex, the association matrix as the edge weight, 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 diagram when a disturbance event occurs, it is specifically configured to: Identifying the disturbance task node in the topology graph that is triggered when a disturbance event occurs; Performing cross-level reverse tracing of the disturbance task node along the process path tree, and marking all associated task nodes with time coupling constraints in the cross-level reverse tracing; Constructing a minimum impact domain subtree of the topology graph using the associated task nodes and the connected process constraint edges; The minimum impact domain subtree is used as the affected process dependency tree in the topology graph.

[0008] In a preferred embodiment, when evaluating the task delay cost of the task node in the process dependency tree and outputting the demand intensity coefficient for manufacturing process rescheduling, the demand intensity coefficient acquisition module is specifically configured to: Obtaining the process immediate predecessor relationship depth of the task node in the minimum impact domain subtree; Assigning a basic hysteresis coefficient to the process dependency tree based on the depth of the immediate predecessor relationship of the process; The basic delay coefficient is added 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 in the process dependency tree.

[0009] In a preferred embodiment, 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 in the process dependency tree, it is specifically used to: 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 cycle of the material corresponding to the task node are coupled to obtain the material supply shortage 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 the process beat deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient, it is specifically used to: generating a relaxation window of the process rhythm in the manufacturing process according to the demand intensity coefficient; Adjusting the processing timing of the mining equipment task queue within the relaxation window; The load overflow tasks of the parallel equipment in the manufacturing process are dynamically allocated based on the processing timing and the equipment utilization rate.

[0011] In a preferred embodiment, when generating the flexible scheduling Gantt chart of the manufacturing process, the flexible scheduling Gantt chart acquisition module is specifically used to: Inserting a buffer time slot into the final assembly line task sequence, and coloring and marking the task blocks of the initial scheduling Gantt chart according to the load balancing degree of the parallel devices; The colored initial Gantt chart is output as a flexible scheduling Gantt chart of the manufacturing process.

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

[0013] In a preferred embodiment, before the final scheduling instruction set generation module performs dynamic modification of the process path of the production situation map and process optimization results of the flexible scheduling Gantt chart to confirm the final scheduling instruction set of the mining equipment, it is specifically used to: Verifying the matching degree between the correction instruction set of the process path correction topology diagram and the coordinates of the material turnover path; When the matching degree exceeds a preset correction matching value and the process beat deviation is less than the maximum acceleration tolerance range of the equipment in the manufacturing process, a process parameter tolerance license certificate of the correction instruction set is attached to the equipment.

[0014] In order to solve the above problems, the present invention also provides a method for optimizing production scheduling of intelligent manufacturing of mining equipment, the method comprising: S1. Based on the process connection of equipment status data, material inventory data and order information of mining equipment, a topological map representing the production status of mining equipment is constructed; S2. Generate an initial scheduling Gantt chart for the manufacturing process, using the component task set in the pre-acquired production order as the vertical axis and the corresponding assembly line task sequence as the horizontal axis; S3. When a disturbance event occurs, extract the affected process dependency tree in the topology diagram, evaluate the task delay costs of the task nodes in the process dependency tree, and output the cost as a demand intensity coefficient for rescheduling the manufacturing process; S4. Synchronously adjusting the process beat deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient to generate a flexible scheduling Gantt chart for the manufacturing process; S5. Confirm the final scheduling instruction set of the mining equipment through the dynamic correction of the process path of the production situation map and the process optimization result of the flexible scheduling Gantt chart.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a topological diagram based on the process connections of mining equipment status data, material inventory data, and order information. 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, evaluates task delay costs, and outputs a demand intensity coefficient for rescheduling. It then synchronously adjusts process cycle deviations and equipment utilization to generate a flexible scheduling Gantt chart. The final scheduling instruction set is confirmed through dynamic process path correction and process optimization, effectively improving the planning efficiency of mining equipment manufacturing production process scheduling solutions.

[0016] 2. During the manufacturing process, the present invention achieves synchronous adjustment of process rhythm and equipment utilization by generating a flexible scheduling Gantt chart and combining it with dynamic correction of the process path. It can quickly respond and optimize the scheduling plan when a disturbance event occurs. 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, significantly improving the execution efficiency and effectiveness of the mining equipment manufacturing production scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a system architecture diagram of a mining equipment intelligent manufacturing production scheduling optimization system provided by one embodiment of the present invention; Figure 2 A flowchart of a method for optimizing production scheduling of intelligent manufacturing of mining equipment provided by one embodiment of the present invention.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments belong to some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise, and "a plurality" generally includes at least two.

[0021] As used herein, the words “if” or “when” may be interpreted as “at the time of” or “when” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrases “if it is determined” or “if (stated condition or event) is detected” may be interpreted as “when it is determined” or “in response to the determination” or “when detecting (stated condition or event)” or “in response to detecting (stated condition or event),” depending on the context.

[0022] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0023] In practice, the server-side device deployed in a mining equipment intelligent manufacturing production scheduling optimization system may be composed of one or more devices. The aforementioned mining equipment intelligent manufacturing production scheduling optimization system can be implemented as: a service instance, a virtual machine, and hardware devices. For example, the mining equipment intelligent manufacturing production scheduling optimization system can be implemented as a service instance deployed on one or more devices in a cloud node. Simply put, the mining equipment intelligent manufacturing production scheduling optimization system can be understood as software deployed on a cloud node, used to provide a mining equipment intelligent manufacturing production scheduling optimization system to each user terminal. Alternatively, the mining equipment intelligent manufacturing production scheduling optimization system can be implemented as a virtual machine deployed on one or more devices in a cloud node. Application software for managing each user terminal is installed in the virtual machine. Alternatively, the mining equipment intelligent manufacturing production scheduling optimization system can be implemented as a server-side device composed of multiple hardware devices of the same or different types, with one or more hardware devices configured to provide a mining equipment intelligent manufacturing production scheduling optimization system to each user terminal.

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

[0025] like Figure 1 FIG. 1 is a system architecture diagram of a mining equipment intelligent manufacturing production scheduling optimization system provided by an embodiment of the present invention.

[0026] The intelligent manufacturing production scheduling optimization system 100 for mining equipment described in the present invention can be installed in a cloud server. In terms of implementation, it can be implemented as one or more service devices, as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or developed as a website. Depending on the functionality implemented, the intelligent manufacturing production scheduling optimization system 100 for mining equipment can include a topology map 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. A module described in the present invention, also referred to as a unit, refers to a series of computer program segments that can be executed by an electronic device processor and perform fixed functions, and is stored in the electronic device's memory.

[0027] In an embodiment of the present invention, in a production scheduling optimization system for intelligent manufacturing of mining equipment, each of the above modules can be implemented independently and called with other modules. The call here can be understood as a module that can connect to multiple modules of another type and provide corresponding services to the multiple modules connected to it. In a production scheduling optimization system for intelligent manufacturing of mining equipment provided by an embodiment of the present invention, the scope of application of the architecture of the production scheduling optimization system for intelligent manufacturing of mining equipment can be adjusted by adding modules and directly calling them without modifying the program code, thereby realizing cluster-based horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the production scheduling optimization system for intelligent manufacturing of mining equipment. In actual applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0028] The following describes the various components and specific workflows of a mining equipment intelligent manufacturing production scheduling optimization system in conjunction with specific embodiments: The topology map generating module 101 is used to construct a topology map representing the production status of mining equipment based on the equipment status data, material inventory data and process connection of order information of mining equipment; In an embodiment of the present invention, when executing 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, the topology generation module is specifically configured to: Analyze the equipment status data into the mapping relationship between vibration spectrum characteristics, temperature rise curve and fault code; Establish a correlation matrix between safety stock threshold and material turnover path in material inventory data; generating a process path tree for the mining equipment based on the process connection of the order information; A topological diagram of the production situation of the mining equipment is constructed with the mapping relationship as the vertex, the association matrix as the edge weight, and the process path tree as the connection rule.

[0029] Specifically, equipment status data is collected. Vibration sensors and temperature sensors are installed at key locations on the equipment. The vibration sensors are used to capture vibration signals during operation, while the temperature sensors are used to 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 operation, providing the 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. For example, a low-pass filter is used to remove high-frequency noise, retaining valid signals related to the equipment's vibration characteristics. For temperature data, abnormal values, such as sudden temperature jumps, are checked for these. These unreasonable data points are removed, and missing data is interpolated to ensure the continuity and accuracy of the temperature data. This ensures that the preprocessed vibration and temperature data can more accurately reflect the equipment's operating status.

[0031] Furthermore, vibration spectrum features are extracted from the preprocessed vibration data. Using the Fourier transform method, the time-domain vibration signal is converted into a frequency-domain signal, obtaining the amplitude and phase information corresponding to each frequency component. The frequency-domain signal is carefully analyzed to identify the main frequency components, such as the power frequency, harmonics, and other possible characteristic frequencies. The amplitudes corresponding to these frequency points are recorded. This frequency and amplitude information is used as the spectrum features of the equipment vibration, providing the key vibration feature basis for establishing a mapping relationship with the fault code.

[0032] Furthermore, a temperature rise curve is generated based on the preprocessed temperature data. With time as the horizontal axis and temperature as the vertical axis, the temperature values ​​corresponding to each timestamp are plotted sequentially in the coordinate system. These points are then connected with a smooth curve to form a complete temperature rise curve. This curve clearly shows the temperature trend over time during device operation, such as whether the temperature gradually rises steadily, remains stable, or exhibits abnormal fluctuations. These trend characteristics are crucial for determining whether the device is faulty.

[0033] Furthermore, a fault coding system should be established. Based on the equipment type, structural characteristics, and common fault types, a unique and clear set of fault coding rules should be developed. A specific code should be assigned to each possible fault.

[0034] For example, bearing wear is set as code 001, gear crack is set as code 002, motor overload is set as code 003, etc.

[0035] Furthermore, the specific fault phenomenon, possible fault cause and impact on equipment operation corresponding to each fault code are recorded in detail to ensure a clear correspondence between the fault code and the actual fault.

[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 conduct an in-depth analysis of historical fault data to find out the combination rules of vibration spectrum characteristics and temperature rise curve characteristics corresponding to different faults.

[0038] For example, when a bearing wear fault occurs in equipment, the vibration spectrum will show a significant increase in amplitude in a specific frequency range (such as near the bearing characteristic frequency), while the temperature rise curve will also show a slowly increasing trend. Based on these characteristic combinations, a clear mapping is established with 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 relationship table.

[0039] Furthermore, the mapping relationship between the established vibration spectrum characteristics, temperature rise curve and fault coding 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 code is consistent with the actual fault condition.

[0041] Furthermore, if mapping errors are found, a careful analysis should be conducted to determine whether the extraction of vibration spectrum features is inaccurate, the judgment of temperature rise curve features is incorrect, or the setting of fault coding feature combination conditions is unreasonable. Adjustments and optimizations should be made to specific problems, such as correcting the range of characteristic frequencies, adjusting the judgment criteria for the temperature rise curve change trend, etc., until the accuracy of the mapping relationship meets the requirements of actual applications. Furthermore, the mapping relationship between the verified and optimized vibration spectrum characteristics, temperature rise curve and fault coding is sorted and output.

[0042] Furthermore, it is presented in a clear and unambiguous document form, which lists in detail the specific frequency range and amplitude requirements of the vibration spectrum characteristics corresponding to each fault code, as well as the changing trend of the temperature rise curve and the temperature change rate and other characteristics. At the same time, the basis for establishing each mapping relationship and the verification process are explained to ensure that relevant personnel can accurately understand and use the mapping relationship, and by analyzing the vibration spectrum characteristics and temperature rise curve of the equipment, quickly and accurately determine the fault code of the equipment, and take corresponding repair and maintenance measures.

[0043] Specifically, information on safety stock thresholds and detailed information on material turnover paths is collected from material inventory data. The safety stock threshold for each material is extracted from the inventory management system. This threshold is the minimum inventory quantity set to prevent material shortages. Furthermore, the material turnover path is organized through logistics process documentation or information systems, clearly defining the complete material flow path from procurement source, through transportation, warehousing, production, and other links until consumption. The operator, time span, and flow rules for each link are recorded.

[0044] Furthermore, we analyze the key factors influencing the safety stock threshold and map them to the specific links in the material turnover path. The safety stock threshold is primarily influenced by factors such as material demand fluctuations, supply cycle length, transportation reliability, and warehouse management efficiency. These factors are then matched to the links in the turnover path.

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

[0046] Furthermore, all key nodes in the material flow path are sorted out and the specific attributes of each node are clarified. According to the order of material flow, the flow path is broken down into nodes such as purchase order placement, supplier stocking, logistics transportation, warehouse acceptance, storage, production use, and consumption completion. Each node is labeled with its sequence in the path, the time required for operation, possible risk points, and its connection with other nodes. For example, the warehouse acceptance node requires verification of material quantity and quality. Only materials that pass the acceptance can enter the warehouse storage node; otherwise, they will be returned to the supplier for restocking.

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

[0048] Furthermore, for each factor affecting safety stock, the nodes involved in the turnover path are analyzed. For example, the supply cycle factor involves several consecutive nodes such as procurement order placement, supplier stocking, logistics transportation, and warehousing acceptance. The total time spent at 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 interruption. For example, the transportation reliability factor mainly involves logistics transportation nodes. The higher the probability of delays or damage during transportation, the more quantity needs to be reserved in the safety stock to deal with possible supply interruptions.

[0049] Furthermore, the basic framework of the association 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 cycle, transportation reliability, warehouse management efficiency, etc. The key nodes of the material turnover path are used as the columns of the matrix, including purchase orders, supplier stocking, logistics transportation, warehouse acceptance, warehouse storage, production and production. The cells of the matrix are used to describe the association between rows and columns, that is, how a certain influencing factor affects the safety stock threshold through the corresponding turnover path node.

[0050] Fill in the details of the correlation matrix and explain the correlation logic in detail. In each cell, clearly state the specific relationship between the factors affecting safety stock and the nodes in the turnover path.

[0051] For example, in the intersection cell of the "Demand Fluctuation" row and the "Production Collection" column, it is shown that the deviation between the actual demand and the planned demand in the production collection link directly reflects the degree of demand fluctuation. If urgent orders or order cancellations often occur during the collection process, the demand fluctuation will increase. In order to cope with this uncertainty, the safety stock threshold needs to be increased accordingly; in the intersection cell of the "Supply Cycle" row and the "Logistics and Transportation" column, it is shown that the length of transportation time is an important part of the supply cycle. Slow transportation speed will extend the entire supply cycle, thereby requiring the safety stock threshold to be set higher to cover material consumption during supply delays.

[0052] Furthermore, the accuracy and completeness of the association matrix is ​​verified and adjusted and optimized. The actual inventory management data and turnover path records are compared to check whether each association is consistent with the actual business situation.

[0053] For example, check whether materials that are frequently delayed in the transportation process have indeed been set with a higher safety stock, or whether materials with high warehouse management efficiency have a lower safety stock threshold 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 inadequate consideration of influencing factors, and modify the matrix content in a targeted manner to ensure that the setting of each safety stock threshold can find a clear related node and action logic in the turnover path.

[0054] Furthermore, a complete matrix of safety stock thresholds and material turnover paths is ultimately formed and documented. The validated matrix is ​​presented in tabular form, with detailed explanations below the table explaining the basis for establishing each relationship and its practical significance for inventory management. For example, this 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 and procurement link adjust the safety stock threshold through historical data statistics. This allows the matrix to serve as an inventory management tool, helping managers to rationally set safety stock by optimizing turnover path nodes, avoiding both inventory backlogs and material shortages, and achieving a balance between inventory costs and supply security.

[0055] Specifically, we collect order information for mining equipment. This information includes equipment specifications, functional requirements, and delivery time. It also covers detailed data such as production quantity and quality standards. This information is fully obtained from the business system or contract documents where the order was placed, providing a basis for subsequent process path planning.

[0056] Further, analyze the various requirements in the order information. Carefully study the order and break down the various components of the equipment and their corresponding technical specifications. For example, for crusher equipment, clarify 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, we organized all the process steps involved in mining equipment production. Following the equipment manufacturing process, we listed a series of process steps, from raw material procurement and inspection, to component processing (such as cutting, forging, welding, and machining), to component assembly, overall commissioning, and quality inspection, clearly presenting the operation content and purpose of each process step.

[0058] Furthermore, we screen suitable process steps based on the order requirements. We compare the equipment specifications and performance requirements in the order and select those that meet the requirements from the sorted process steps. For example, for high-precision mining equipment components, we select higher-precision CNC machine tool processing and eliminate standard machine tool processing to ensure that the selected process meets the order's equipment quality and performance requirements.

[0059] Furthermore, the order of each process step is determined. The process sequence is arranged based on the actual process logic of mining equipment production and the process characteristics. For example, parts processing should be performed first, followed by assembly. Welding should be performed after the parts are formed, forming an orderly process chain to ensure a smooth production process.

[0060] Furthermore, a process path is constructed using a tree structure. The final product of the mining equipment is used as the root node of the process path tree. Following the established process sequence, each process step is then extended downward as a sub-node. Each sub-node represents a process step. If a process step contains multiple sub-steps, these sub-steps are then nested as the next-level sub-nodes, forming a complete process path tree framework.

[0061] Furthermore, the process tree is annotated with order-related information. At each process node, the specific requirements and parameters related to the order information are marked. For example, at the component processing node, information such as the dimensional tolerance and surface roughness required by the order are marked. This allows the process tree to be closely integrated with the order information, making it easier for production personnel to intuitively understand the order requirements of each process step.

[0062] Furthermore, the process path tree should be inspected and improved. A comprehensive review of the established process path tree should be conducted to check whether the process sequence is reasonable, whether each process step meets the order requirements, and whether the logical relationships between nodes are clear. If any irregularities are found, such as the order of two process steps being reversed, timely adjustments should be made to ensure that the process path tree accurately and completely reflects the mining equipment process path based on the 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 between vibration spectrum characteristics, temperature rise curves, and fault codes are obtained from the equipment status data analysis results. Each fault code and its corresponding feature combination is used as an independent mapping relationship unit. The association matrix between safety stock thresholds and material turnover paths is extracted from the material inventory data processing results, clarifying the strength of the association 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 process sequence and node relationships of mining equipment production, and preparing complete basic data for constructing the topology map.

[0064] Furthermore, the vertex composition of the topology graph is determined and assigned a unique identifier. Each fault code in the mapping relationship is regarded as a core vertex, because each fault code corresponds to a specific combination of equipment status characteristics and is a key node reflecting possible 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 temperature change rate, etc., to ensure that the vertex can accurately represent the different status conditions in equipment production and form a node entity with practical significance in the topology diagram.

[0066] Furthermore, the association matrix is ​​converted into edge weights of a topological graph and the edge connections are defined. Each row in the association matrix represents the factors affecting 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 topological graph, these association relationships are converted into edges connecting safety stock-related nodes and material turnover path nodes. The edge weights are set according to the association strength described in the association matrix. For example, association relationships with a high degree of influence are given higher weight values, and those with a low degree of 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 diagram are determined and node connections are established based on the structure of the process path tree. The process path tree forms a tree structure with the final product of the equipment as the root node and each process link as a child node. In the topology diagram, the nodes representing different process links are connected according to the order and logical relationship of the process links in the process path tree. The nodes of the previous process link point to the nodes of the subsequent process link, forming a directed edge with direction. This ensures that the connections in the topology diagram can accurately reflect the flow order and dependency relationship of the process links in the mining equipment production process, so that the entire production process is clearly presented in the topology diagram.

[0069] Furthermore, the complete topology graph is constructed by integrating vertex, edge weights, and connection rules. All fault code vertices representing mapping relationships, weighted edges representing the association matrix, and node connections that conform to the process path tree connection rules are integrated. Check that each vertex is correctly associated with the corresponding feature attribute, that the weight of each edge accurately reflects the association relationship in the association matrix, and that the connections between nodes fully adhere to the order and logic of the process path tree. This ensures that each element in the topology graph accurately corresponds to the product of the previous step, with no omissions or incorrect connections.

[0070] Furthermore, the constructed mining equipment production status topology map is verified for consistency. The mapping relationships are used to check whether the characteristic attributes of each vertex are complete and accurate. The association matrix is ​​used to check whether the weight setting of each edge is consistent with the described association strength. The process path tree is used to check whether the connection order and direction between nodes are correct. For example, the "Parts Processing" node in the process path is confirmed to be before the "Assembly" node and correctly connected. The edges between the safety stock threshold influencing factors and the turnover path nodes are confirmed to correspond to the specific association logic in the association matrix. This ensures that the topology map can truly and accurately reflect the relationship between equipment status, material inventory, and process flow during the mining equipment production process.

[0071] Furthermore, a topology diagram that intuitively displays the production status of mining equipment is ultimately formed and documented. The topology diagram is presented graphically, with each vertex represented by a specific symbol and labeled with the corresponding fault code and characteristics. Each edge is represented by a line with an arrow to indicate the connection direction and labeled with the corresponding association weight. The document also details the construction logic of the topology diagram, including how vertices are selected, how edge weights are determined, and how connection rules are applied. This allows the topology diagram to serve as a visualization tool, helping production managers quickly grasp the potential failure risks, material inventory influencing factors, and process flow status of mining equipment production by observing vertex status, edge weights, and node connectivity, providing an intuitive basis for production decision-making and optimization.

[0072] In general, constructing a production situation topology map based on the process connection 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 situation visualization.

[0073] In general, this topology diagram integrates equipment operating status, material flow logic, and order process requirements into a unified graph structure model by analyzing equipment status data, establishing a correlation matrix between material inventory safety stock thresholds and turnover paths, and generating a process path tree based on order information. This allows schedulers to fully understand the real-time correlation status of various elements of 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 general, the construction mechanism of the topological graph, which uses mapping relationships as vertices, association matrix as edge weights, and process path tree as connection rules, can dynamically characterize the causal transmission path of the production situation of mining equipment.

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

[0076] In general, this topology-based production situation modeling method not only supports the dynamic switching of process paths and critical path time difference correction when equipment failure occurs, but also provides a visual correlation analysis framework for the synchronous adjustment of process rhythm and equipment utilization in flexible scheduling, thereby improving the response efficiency and global optimization capabilities of the scheduling plan to complex production environments.

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

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

[0079] Furthermore, the extracted component task set is broken down and organized in detail. Each component task is split into smaller subtasks (if necessary) according to the production process to ensure that each task is independently executable.

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

[0081] Furthermore, the logical order and time nodes of the final assembly line task sequence are sorted out. According to the mining equipment final assembly process regulations, the order of final assembly line tasks is determined.

[0082] For example, "component pre-assembly" is carried out first, then "motor installation", and finally "overall debugging". At the same time, combined with historical production data or process standards, a reasonable operation time is estimated for each 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 coordinate axis content is determined. The horizontal axis represents the assembly line task sequence. Each task is labeled from left to right in the order in which the assembly tasks are to be executed, such as "component pre-assembly," "motor installation," and "overall commissioning." Appropriate spaces are left between adjacent tasks based on process requirements and time intervals. The vertical axis represents the component task set, which is arranged from top to bottom according to the order in which the components are used in the assembly process or the priority of the tasks. For example, the "gear processing" and "bearing procurement" tasks required in the early stages of assembly are listed first, followed by the "housing welding" and "bolt assembly" tasks required in the middle and late stages. This ensures that the content of the vertical and horizontal axes clearly corresponds to the two types of task sets in the production order.

[0084] Furthermore, mark the time scale on the horizontal axis and determine the time span. Determine the Gantt chart's time range based on the estimated start and finish times of all tasks in the final assembly line task sequence. For example, start from the order effective date and end on the estimated final assembly completion date. Divide this time range into daily or hourly time scales, evenly marking them above or below the horizontal axis. Ensure that the time scale clearly reflects the schedule of each task, providing a unified time reference standard for allocating time to subsequent tasks.

[0085] Furthermore, the component task sets and assembly line task sequences are mapped to the Gantt chart's coordinate system. For each component task on the vertical axis, a horizontal rectangular bar is drawn to the right of the corresponding vertical axis task name based on its estimated start and finish times. The left end of the rectangular bar 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 processing" task is expected to start on the 3rd day and be completed on the 7th day, a rectangular bar is drawn on the row corresponding to "gear processing" on the vertical axis, from the 3rd day scale to the 7th day scale on the horizontal axis; for the general assembly line tasks on the horizontal axis, rectangular bars are also drawn under the corresponding horizontal axis task names according to their time schedules to clearly define the interval occupied by each task on the timeline.

[0087] Furthermore, the dependencies between tasks are addressed and task schedules are adjusted. All component tasks and assembly line tasks are checked for dependencies. If a component task is a prerequisite for an assembly line task, the component task's rectangle must be completed before the start time of the assembly line task's rectangle. For example, the "bearing procurement" task must be completed before the start time of the "motor installation" task on the assembly line. If a time conflict is found, such as when the component task's estimated completion time is later than the assembly line task's start time, the component task's start time is adjusted or resources are added to shorten the completion time, ensuring that the scheduling of all tasks conforms to actual production logic.

[0088] Furthermore, the drawn initial scheduling Gantt chart is thoroughly reviewed and revised. Check that the name, time span, and dependencies of each task accurately reflect the production order requirements. Check that the position and length of the rectangular bars are consistent with the expected start and completion times. Confirm that the task arrangement on the vertical and horizontal axes is reasonable and easy to view. For example, check that the component tasks on the vertical axis are arranged in the order of final assembly use, and whether the final assembly tasks on the horizontal axis are out of order or overlap in time. If any problems are found, adjust the position or length of the rectangular bars 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 with attached documentation. The Gantt chart is presented in a visual chart format, using different colors to distinguish between component tasks and assembly line tasks, and the resource requirements, responsible persons, and special precautions of key tasks are marked below or next to the chart. For example, it is noted 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 instruction document explains in detail the basis for drawing the Gantt chart, the task time estimation method and the dependency processing principles, so that production schedulers can quickly grasp the time arrangement and relationship between each task through the Gantt chart, and provide clear guidance for subsequent production scheduling and resource allocation.

[0090] In general, generating an initial scheduling Gantt chart with the component task set in the pre-acquired production order as the vertical axis and the corresponding assembly line task sequence as the horizontal axis can improve scheduling efficiency from two aspects: visualizing task dependencies and coordinating production rhythm.

[0091] In general, the Gantt chart directly links the component production progress with the assembly line assembly sequence through a two-dimensional mapping of the component task set on the vertical axis and the assembly line task sequence on the horizontal axis. This allows scheduling personnel to intuitively grasp the time coupling relationship between each component task and the assembly process, discover timing conflicts between material supply and assembly requirements in advance, avoid assembly line shutdowns or inventory backlogs due to component delays, and achieve pre-allocation optimization of production resources.

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

[0093] In general, when a disturbance event occurs, the task block of the initial Gantt chart can quickly locate the affected component tasks and the nodes associated with the final assembly process, providing accurate time and space 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 scheduling process from initial planning to dynamic adjustment.

[0094] The demand intensity coefficient acquisition module 103 is configured to extract the affected process dependency tree in the topology diagram when a disturbance event occurs, evaluate the task delay costs of the task nodes in the process dependency tree, and output the demand intensity coefficient for rescheduling the manufacturing process; In an embodiment of the present invention, when the demand intensity coefficient acquisition module extracts the affected process dependency tree in the topology diagram when a disturbance event occurs, it is specifically configured to: Identifying the disturbance task node in the topology graph that is triggered when a disturbance event occurs; Performing cross-level reverse tracing of the disturbance task node along the process path tree, and marking all associated task nodes with time coupling constraints in the cross-level reverse tracing; Constructing a minimum impact domain subtree of the topology graph using the associated task nodes and the connected process constraint edges; The minimum impact domain subtree is used as the affected process dependency tree in the topology graph.

[0095] The demand intensity coefficient acquisition module is specifically used to evaluate the task delay cost of the task node in the process dependency tree and output the demand intensity coefficient for manufacturing process rescheduling when performing the following operations: Obtaining the process immediate predecessor relationship depth of the task node in the minimum impact domain subtree; Assigning a basic hysteresis coefficient to the process dependency tree based on the depth of the immediate predecessor relationship of the process; The basic delay coefficient is added 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 in the process dependency tree.

[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 in the process dependency tree, it is specifically used to: 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 cycle of the material corresponding to the task node are coupled to obtain the material supply shortage 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, interruption in the supply of raw materials, temporary changes in order requirements, etc., it is necessary to carefully compare the previously constructed topology map. Each vertex in the topology map represents the equipment status characteristics and task information. For example, a vertex may correspond to a specific fault code and its related production task. By checking each vertex one by one, it is determined which tasks corresponding to the vertices are directly affected by the current disturbance event. For example, if a sudden equipment failure causes the ongoing production task to be forced to suspend, then the vertex representing the fault code and the corresponding production task will be determined as the triggered disturbance task node. During the judgment process, all vertices must be comprehensively reviewed to ensure that no task node directly affected by the disturbance event is missed, laying an accurate foundation for subsequent analysis.

[0098] Furthermore, the identified disturbance task nodes are analyzed with the help of the hierarchical structure and connection relationship of the process path tree.

[0099] Furthermore, the process path tree clearly illustrates 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 back to the upper node. During the tracing process, the focus is on checking whether there are temporal coupling constraints between each node and the disturbance task node.

[0100] Furthermore, the temporal coupling constraint refers to the situation where the tasks represented by two nodes are interdependent in terms of time, for example, one task must start after the completion of another task, or there is partial overlap in the execution time of two tasks.

[0101] Furthermore, once a node with such a temporal coupling constraint is discovered, it is immediately marked as an associated task node and continuously tracked all the way back to the root node of the process path tree. This operation can mark all associated task nodes with temporal coupling constraints on the disturbing task node, thus completely clarifying the task chain affected by the disturbance event and clearly identifying which tasks are affected by the disturbing task.

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

[0103] Furthermore, process constraint edges in the topology map reflect various process relationships between tasks, including the order in which tasks must be executed, such as the assembly task can only be performed after the component processing task is completed; and resource sharing relationships, such as the interchangeable use of certain equipment or tools between different tasks. The selected associated task nodes and process constraint edges are combined to form an independent tree structure. This newly constructed tree structure is the minimum influence domain subtree of the topology map.

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

[0105] Furthermore, the successfully constructed minimum impact domain subtree is determined as the affected process dependency tree in the topology diagram, and then it is comprehensively sorted and labeled.

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

[0107] Furthermore, the process constraint edges should also be described in detail to explain the specific process association logic between the tasks represented by each edge. For example, an edge represents that the completion of the previous task is a necessary condition for the start of the subsequent task. Only after completing the task corresponding to the starting node of this edge can the task corresponding to the ending node be carried out.

[0108] Furthermore, through such detailed organization and annotation, the process dependency tree can clearly and intuitively show the impact of disturbance events on the entire production process path, providing a clear and valuable reference for the subsequent formulation of targeted response measures and reasonable adjustment of production plans. Specifically, in the constructed minimum impact domain subtree, starting from the root node, the root node usually represents the starting link or key core task of the production process.

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

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

[0111] Furthermore, during the analysis process, for each task node, we start from the node and trace back toward the root node, carefully sorting out and counting the number of task nodes that have a close process relationship on this traceback path.

[0112] Furthermore, the number obtained by this statistic is defined as the process predecessor relationship depth of the task node in the minimum impact domain subtree.

[0113] For example, if a task node has three task nodes with a process-immediate predecessor relationship on the path back to the root node, then the process-immediate predecessor relationship depth of this task node is 3.

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

[0115] Furthermore, based on the previously determined process predecessor relationship depth of each task node, a basic delay coefficient is assigned to the task nodes in the process dependency tree.

[0116] Furthermore, the core function of the basic delay coefficient is to measure the possibility of potential delays caused by various tasks due to the existence of production process sequence relationships.

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

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

[0119] Furthermore, through such operations, the potential delay differences faced by different tasks in the entire production process sequence can be clearly reflected, so that each task has a quantitative measurement standard in terms of the possibility of delay.

[0120] Furthermore, for each task node in the process dependency tree, the task delay cost is calculated. This is done by adding the base delay coefficient assigned to the task node to two other important coefficients: the corresponding downtime penalty coefficient and the material supply interruption risk coefficient.

[0121] Furthermore, the fault downtime penalty coefficient is a value pre-set before the start of production work, based on comprehensive consideration of multiple factors such as the frequency of past equipment failures, the time required for fault 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 that material supply interruptions will affect the task, based on factors such as the supply stability of the material supplier, risk factors in the transportation process, and the importance of the material in the production process.

[0122] Furthermore, during actual calculation, these three coefficients are directly added together, and the sum obtained after the addition is determined as the task delay cost of the task node.

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

[0124] Furthermore, by performing this detailed and rigorous calculation for every task node within the process dependency tree, we can ultimately determine the exact delay cost for each task node. This delay cost comprehensively considers the impact of delays due to various potential risks, such as process sequence, equipment failure, and material supply. This provides an intuitive and valuable quantitative basis for companies to scientifically adjust production plans, effectively assess risks, and address them.

[0125] Specifically, all historical data of the fault corresponding to the task node is retrieved 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 when the failure was repaired.

[0127] Furthermore, the time it takes to repair each fault can be calculated by subtracting the time it takes for the fault to occur from the time it takes for the fault to be repaired.

[0128] Furthermore, add up all the repair times for the faults corresponding to the same task node, and then divide this total time by the total number of faults. By this calculation method, the historical average repair time for the faults corresponding to the task node can be obtained.

[0129] Furthermore, the value weight corresponding to the equipment or equipment components related to the task node is found from a pre-established equipment value weight table.

[0130] Furthermore, this equipment value weight table is formulated by the enterprise based on a comprehensive assessment of multiple factors such as the equipment's purchase cost, its importance in the production process, and the difficulty of maintenance. The larger the weight value, the more important the equipment.

[0131] Furthermore, a weighted operation is performed. For devices with high value weights, the corresponding historical average repair time is magnified at a larger ratio; for devices with low value weights, the historical average repair time is magnified at a smaller ratio or remains unchanged. After such processing, the fault 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, the replenishment time and shelf life decay cycle information of the materials corresponding to the task nodes are obtained from the enterprise's procurement management system and material information management system.

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

[0134] Furthermore, in the material information management system, there is a document describing the characteristics of each material, which clearly records the shelf life of the material. At the same time, this cycle can be further verified and corrected by analyzing the historical data of performance changes of the material during past use.

[0135] After obtaining these two pieces of information, a coupled analysis of replenishment time and shelf life is performed. First, the two durations are compared. If the replenishment time approaches 80% of the shelf life, or exceeds the shelf life, it indicates a significant risk in the material supply process. Untimely supply may lead to a material shortage, or the material may become unusable due to performance degradation after exceeding the shelf life. Based on the severity of this risk and the company's previous experience in addressing similar risks, it is quantified into a specific value. This value is the material supply shortage risk coefficient corresponding to the task node, which intuitively reflects the likelihood that a material supply interruption or quality degradation will affect the task.

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

[0137] Furthermore, since the value ranges and sizes of these three coefficients for different task nodes may vary greatly, in order to be able to more conveniently and accurately compare and analyze the task delay costs of different task nodes, this preliminary sum needs to be normalized.

[0138] Furthermore, the maximum and minimum values ​​of the coefficient sum of all task nodes are counted. Furthermore, for the coefficient sum of each task node, the minimum value obtained by the previous statistics is subtracted from the sum, and the difference is divided by the difference between the maximum and minimum values.

[0139] Furthermore, through such calculation, the sum of the coefficients of each task node can be converted into a value between 0 and 1. This final value is the task delay cost of the task node in the process dependency 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, unified numerical value. Based on this value, production managers can more scientifically adjust production plans, rationally allocate production resources, and develop effective risk management strategies based on the risks at different task nodes.

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

[0142] In general, the system identifies the disturbance task nodes and traces the associated task nodes of the time coupling constraints backward across levels along the process path tree to construct a minimum impact domain subtree as the affected process dependency tree. This can accurately locate the actual impact range of the disturbance event, avoid indiscriminate rescheduling of the global production process, reduce computing resource consumption and scheduling response time, and achieve rapid positioning and localized processing of disturbance events.

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

[0144] In general, this coefficient provides a quantitative decision-making basis for subsequent rescheduling, ensuring that scheduling resources are allocated first to key task nodes with high delay costs, avoiding scheduling resource mismatches due to subjective judgments, and improving the pertinence and effectiveness of rescheduling plans, thereby optimizing the overall execution efficiency of mining equipment manufacturing production scheduling.

[0145] The flexible scheduling Gantt chart acquisition module 104 is used to synchronously adjust the process beat deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient to generate a flexible scheduling Gantt chart for the manufacturing process; In an embodiment of the present invention, when the flexible scheduling Gantt chart acquisition module performs synchronous adjustment of the process beat deviation and the equipment utilization rate in the manufacturing process based on the demand intensity coefficient, it is specifically used to: generating a relaxation window of the process rhythm in the manufacturing process according to the demand intensity coefficient; Adjusting the processing timing of the mining equipment task queue within the relaxation window; The load overflow tasks of the parallel equipment in the manufacturing process are dynamically allocated based on the processing timing and the equipment utilization rate.

[0146] When executing the generation of the flexible scheduling Gantt chart of the manufacturing process, the flexible scheduling Gantt chart acquisition module is specifically used to: Inserting a buffer time slot into the final assembly line task sequence, and coloring and marking the task blocks of the initial scheduling Gantt chart according to the load balancing degree of the parallel devices; The colored initial Gantt chart is output as a flexible scheduling Gantt chart of the manufacturing process.

[0147] Specifically, all order data related to mining equipment is exported from the enterprise order management system, including information such as 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, the remaining time of the order is calculated, and the urgency is evaluated in combination with the order quantity; the market demand analysis report is analyzed, the recent market demand and growth trend of similar products are counted, and factors such as order urgency and market demand trends are comprehensively quantified into a demand intensity coefficient. The higher the coefficient value, the more urgent the demand.

[0149] Furthermore, a rule table corresponding to the demand intensity coefficient and the process rhythm relaxation window is formulated in advance, which clearly stipulates the relaxation window size corresponding to different coefficient intervals. For example, a coefficient of 0-0.3 corresponds to a relaxation window of ±20% of the benchmark rhythm. According to this rule, the relaxation window is determined for each process in the manufacturing process in turn, providing a clear range for the time adjustment of subsequent processes.

[0150] Furthermore, we carefully sorted out the production task queue of mining equipment, clarified the specific process corresponding to each task, referred to the task time schedule presented in the Gantt chart and the logical order of tasks specified in the process path tree, and combined with the determined process rhythm relaxation window, conducted a comprehensive analysis of the task queue.

[0151] Furthermore, with the help of project management software, the critical path tasks in the production process are marked. These tasks directly affect the product delivery time and cannot be adjusted at will. For non-critical path tasks, if the corresponding process slack window is large and the current production resources (such as manpower and equipment) are in a tight state on other key tasks, different adjustment plans are simulated through software to evaluate the impact of postponing the task for different lengths of time on the overall production. The plan that can alleviate resource tension without affecting the overall progress is selected, and the start time of the task is reasonably postponed.

[0152] Furthermore, during the adjustment process, the dependencies between tasks are repeatedly checked, and the task association table is used to ensure that the completion time of the predecessor 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 realizes the optimal allocation of production process time and resources.

[0153] Furthermore, through the enterprise equipment monitoring system, real-time operational data for each parallel device is collected, including device startup, pause, operation, and failure time. This accurately calculates the ratio of actual device operating time to available operating time, thereby determining device utilization. A device load threshold is set: when the device's workload exceeds 80% of its maximum processing capacity per unit time, it is considered to be about to overflow; if it exceeds 100%, it is considered to have overflowed.

[0154] Furthermore, for load overflow equipment, transferable tasks are screened from its current task list, with priority given to tasks that are within the process tact slack window, whose delayed execution does not affect product delivery time, and are not on the critical path.

[0155] Furthermore, an equipment capability matching table is established to record in detail the processing technology type (such as cutting, welding, assembly), processing accuracy range, maximum processing size and other parameters of each 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 precision conditions to complete the task.

[0156] Furthermore, referring to the factory layout diagram, tasks are assigned to equipment that is closer in distance first to reduce material transportation time and cost. By simulating task allocation plans multiple times, the allocation method that maximizes the load balance of each device is selected to achieve dynamic load balancing of parallel equipment and improve overall production efficiency.

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

[0158] Furthermore, a comprehensive review of the task sequence of the final assembly line is conducted, and the sequence and dependencies of each task are clarified through the process path tree, so as to identify the key task nodes that have a greater impact on subsequent tasks and are prone to production interruptions due to factors such as equipment failure and material delays.

[0159] Furthermore, for these critical task nodes, the required buffer time is evaluated between them and subsequent tasks based on experience and historical production data, and buffer time slots of corresponding lengths are inserted.

[0160] For example, if a large component assembly task is followed by an overall debugging task, a half-day buffer time slot is inserted between the two, taking into account issues such as precision adjustment that may arise during the component assembly process.

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

[0162] Furthermore, task blocks of the initial scheduling Gantt chart are colored and marked according to the load balance of the parallel devices.

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

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

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

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

[0167] Furthermore, task blocks executed by devices with balanced load balancing are marked in green, indicating that the devices are in good operating condition and the load is reasonable; task blocks executed by devices with mild imbalance are marked in yellow, indicating that attention needs to be paid to the device load; task blocks executed by devices with severe imbalance are marked in red, warning that the device load is too high and may affect production progress.

[0168] Furthermore, through different color markings, production management personnel can quickly and intuitively understand the load status of each task execution equipment.

[0169] Furthermore, the initial Gantt chart processed by inserting buffer time slots and coloring marks is output as a flexible scheduling Gantt chart of the manufacturing process.

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

[0171] Furthermore, it includes clearly marking the name, start time, 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, write "Flexible Scheduling Gantt Chart" in the title bar of the chart and add a legend to explain in detail the meaning of the device load balancing represented by different color marks.

[0173] Furthermore, the optimized Gantt chart can be output in various forms such as PDF, images or system built-in formats, which makes it convenient for production management personnel to display, print and distribute it on-site in the workshop, as well as archive and subsequent analysis in the production management system, making the flexible scheduling Gantt chart an effective tool for guiding production scheduling, monitoring production progress and adjusting production plans.

[0174] In general, generating a flexible scheduling Gantt chart by synchronously adjusting process cycle deviations and equipment utilization based on the demand intensity coefficient can improve scheduling efficiency in terms of dynamic response, resource optimization, and disturbance resistance. As a quantitative indicator of the cost of task delays under disturbance events, the demand intensity coefficient accurately reflects the urgency of production scheduling adjustments. By generating a slack window for process cycle time, task times 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 slack window can eliminate time coupling between processes and shorten non-value-added time.

[0175] In general, when synchronously adjusting equipment utilization, by dynamically allocating load overflow tasks of parallel equipment, the tasks of overloaded equipment can be diverted to idle equipment, avoiding idle equipment resources and production capacity bottlenecks; inserting buffer time slots in the task sequence of the final assembly line can reserve time margin for subsequent processes to deal with disturbances, and the coloring marking mechanism based on equipment load balancing can intuitively present the resource occupancy status of the task block, assisting dispatchers to quickly identify efficiency bottlenecks.

[0176] In general, this mechanism enables the scheduling plan to have both execution accuracy under rigid constraints and adaptability under flexible disturbances. Through the coordinated optimization of process and equipment resources, it achieves a dynamic balance between production rhythm and equipment capacity, thereby improving the response efficiency and execution effectiveness of the scheduling plan in complex manufacturing environments.

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

[0178] In an embodiment of the present 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 warning equipment node in the process path tree is switched to the backup equipment node; The time difference of the critical path in the process path tree after switching the standby node is checked and fed back to the topology diagram to obtain a process path correction topology diagram of the manufacturing process.

[0179] Before the final scheduling instruction set generation module performs the dynamic modification of the process path of the production situation map and the process optimization result of the flexible scheduling Gantt chart to confirm the final scheduling instruction set of the mining equipment, it is specifically used to: Verifying the matching degree between the correction instruction set of the process path correction topology diagram and the coordinates of the material turnover path; When the matching degree exceeds a preset correction matching value and the process beat deviation is less than the maximum acceleration tolerance range of the equipment in the manufacturing process, a process parameter tolerance license certificate of the correction instruction set is attached to the equipment.

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

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

[0182] Furthermore, historical production records and experience data are used to identify key task nodes that have a great impact on subsequent tasks and are prone to production interruptions due to factors such as equipment failure and material supply delays.

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

[0184] Furthermore, for such critical task nodes, the required buffer time is evaluated and the corresponding buffer time slot is inserted between them and subsequent tasks, taking into account the average time fluctuations of similar tasks in the past, equipment fault repair time statistics, etc.

[0185] Furthermore, during the crushing chamber assembly and whole 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, shorter buffer time slots are inserted at appropriate locations based on the slack window of the process rhythm and the idle periods of current human, equipment and other resources. For example, for some auxiliary component installation tasks, 2-4 hours of buffer time slots can be inserted to enhance the flexibility and anti-interference ability of the production process to deal with emergencies.

[0187] Furthermore, according to the load balancing degree of the parallel devices, a coloring and marking operation of the task blocks of the initial scheduling Gantt chart is performed.

[0188] Furthermore, the operating data of each parallel device is retrieved in real time from the enterprise equipment monitoring system, covering information such as device startup, pause, operating time, and current execution task list.

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

[0190] Furthermore, load balancing is divided into specific intervals. If the difference between the task load and the maximum processing capacity of each device is within 10%, it is considered balanced; if the difference is between 10% and 20%, it is slightly unbalanced; and if it exceeds 20%, it is severely unbalanced. Check the device execution information corresponding to each task block by comparing it with the initial scheduling Gantt chart.

[0191] Furthermore, task blocks executed by load-balanced devices are marked with a green fill, visually indicating stable device operation and a reasonable load. Task blocks executed by slightly unbalanced devices are marked with a yellow fill, reminding production managers to pay attention to changes in device load and take timely measures. Task blocks executed by severely unbalanced devices are marked with a red fill, warning that the device is overloaded and may cause production delays, requiring prompt adjustment of task allocation. This color-coded approach allows production managers to quickly identify the load status of each task-executing device on the Gantt chart, providing an intuitive basis for production scheduling.

[0192] Furthermore, the initial Gantt chart after inserting buffer time slots and coloring mark processing is converted and output into a flexible scheduling Gantt chart of the manufacturing process.

[0193] Furthermore, you can optimize the Gantt chart format using professional charting software, such as Microsoft Visio, dedicated Gantt chart drawing tools, or the Gantt chart generation module included in your enterprise production management system. Clearly mark the name, specific start and end time of each task block on the Gantt chart, accurate to the hour or minute.

[0194] Furthermore, the device number, model and other information corresponding to each task are marked, as well as the start and end time and duration of the buffer time slot.

[0195] Furthermore, "Gantt Chart of Flexible Scheduling of Manufacturing Process" is prominently written in the title bar of the chart, and a legend is added to the side or bottom of the chart to explain in detail the equipment load balancing status and meaning represented by the green, yellow, and red marks respectively.

[0196] Furthermore, the optimized Gantt chart can be exported to PDF format for printing and archiving, exported to image format for display on the workshop electronic screen, or directly saved as the built-in format of the production management system, so that managers can access, view and analyze data at any time in the system, making the flexible scheduling Gantt chart a highly efficient visual tool for guiding production scheduling, real-time monitoring of production progress, and timely adjustment of production plans.

[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 order of task execution. At the same time, the material turnover path coordinates are retrieved from the material inventory management system and logistics information system. These coordinates record the specific location and flow trajectory of materials in the procurement, transportation, warehousing, production and other links.

[0198] Furthermore, it ensures that the acquired correction instruction set and material turnover path coordinates are complete and accurate, providing a reliable data basis for subsequent matching verification.

[0199] Furthermore, the matching degree between 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 correction instruction set is analyzed one by one to determine whether it will affect the material turnover path.

[0201] For example, if a revision instruction set includes adjustments to a component's processing technology, then it's necessary to check whether the procurement, transportation, and warehousing stages of the component's material flow path require corresponding adjustments. The revision instruction set's impact on the material flow path is compared with the material flow path coordinates to verify logical and operational consistency.

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

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

[0204] Furthermore, the calculated matching degree is compared with the preset corrected matching value, and it is checked whether the process beat deviation is less than the maximum acceleration tolerance range of the equipment in the manufacturing process.

[0205] Furthermore, the preset correction matching value is a standard value set in advance by the enterprise based on production experience and process requirements, which is used to measure whether the degree of matching between the correction instruction set and the material turnover path coordinates is qualified; the process rhythm deviation refers to the difference between the actual process rhythm after the correction operation and the original planned process rhythm, which can be obtained by comparing the process time schedule before and after the process path correction; the maximum acceleration tolerance range of the equipment during the manufacturing process is recorded in the technical parameter manual of the equipment, which represents 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 beat deviation is less than the maximum acceleration tolerance range of the equipment, it indicates that the correction operation will not have an adverse effect on material turnover and equipment operation.

[0207] Furthermore, when the matching degree and process cycle deviation conditions are met, a process parameter tolerance license certificate for the correction instruction set is attached to the equipment.

[0208] Furthermore, the process parameter tolerance license certificate is a document that proves that the equipment can still operate normally and meet production requirements after executing the process parameter adjustment of the revised instruction set.

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

[0210] Furthermore, using a unified corporate certificate template, accurately complete the relevant information and affix the company seal or a dedicated verification stamp to ensure the certificate's validity and authority. This certificate is stored in association with the equipment file and sent to production managers and equipment operators, allowing them to confirm that any process parameter adjustments to the equipment are authorized and to confidently execute the relevant operations related to the modified instruction set during production.

[0211] Specifically, the real-time monitoring function of the production management system is used to collect the completion progress of the revised 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 equipment's operating status, task execution status, etc. in real time, and transmit the data to the production management system.

[0213] Furthermore, based on these data, the system compares the task requirements in the revised instruction set, counts the number of completed instructions and the progress of instructions being executed, and thus obtains 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, the time deviation of each task is calculated, and the offset of the flexible scheduling Gantt chart is determined by combining the time deviations of all tasks.

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

[0216] The calculated offset from the flexible scheduling Gantt chart is then compared with the preset tolerance. This tolerance is a permissible deviation range set by the company based on production experience and process requirements. It is used to measure whether production progress is meeting expectations. If the offset exceeds the preset tolerance, it indicates a significant deviation from the production schedule, and a new disturbance event is considered to have occurred.

[0217] Furthermore, the system immediately triggers an alarm, alerting production managers and feeding the new disturbance event information back to the demand intensity coefficient acquisition module. Upon receiving this information, the module re-evaluates and calculates the demand intensity coefficient based on factors such as current order status and market demand. Based on the new demand intensity coefficient and the specific circumstances 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 paths.

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

[0219] Furthermore, the instruction set for handling the latest disturbance event is sent to various terminals of the production management system through the enterprise's internal network, including the control panel at the workshop site, the control system of the production equipment, and the work computer of the production manager.

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

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

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

[0223] In general, by collaboratively confirming the final scheduling instruction set through dynamic correction of the process path based on the production situation map and the process optimization results based on the flexible scheduling Gantt chart, scheduling efficiency can be improved in terms of both real-time performance and accuracy.

[0224] In general, the dynamic correction of the process path switches the backup equipment nodes in real time based on the equipment status data (such as vibration spectrum characteristics and fault codes), and verifies the critical path time difference to ensure that the production process path is quickly adjusted when the equipment is abnormal, avoid process stagnation caused by equipment failure, and maintain the continuity of the production process; on the other hand, the flexible scheduling Gantt chart optimizes the task processing timing and equipment utilization through process beat deviation adjustment, parallel equipment load overflow task allocation and insertion of buffer time slots, so that the scheduling plan has the flexibility to deal with disturbance events.

[0225] In summary, when these two technologies are combined, the process path correction topology ensures the process feasibility of scheduling instructions, while the process optimization results of the flexible scheduling Gantt chart ensure efficient resource allocation. By verifying the matching degree between the correction instruction set and the material turnover path and attaching a process parameter tolerance license, parameter conflicts and material supply shortage risks during scheduling execution are further eliminated. This collaborative mechanism enables rapid response to production disturbances, reduces task delay costs, and achieves a dynamic balance between process cycle time and equipment load, thereby improving the overall execution efficiency and stability of mining equipment manufacturing production scheduling solutions.

[0226] Reference Figure 2 FIG. 1 is a flow chart of a method for optimizing production scheduling of intelligent manufacturing of mining equipment provided by an embodiment of the present invention. In this embodiment, the method for optimizing production scheduling of intelligent manufacturing of mining equipment includes: S1. Based on the process connection of equipment status data, material inventory data and order information of mining equipment, a topological map representing the production status of mining equipment is constructed; S2. Generate an initial scheduling Gantt chart for the manufacturing process, using the component task set in the pre-acquired production order as the vertical axis and the corresponding assembly line task sequence as the horizontal axis; S3. When a disturbance event occurs, extract the affected process dependency tree in the topology diagram, evaluate the task delay costs of the task nodes in the process dependency tree, and output the cost as a demand intensity coefficient for rescheduling the manufacturing process; S4. Synchronously adjusting the process beat deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient to generate a flexible scheduling Gantt chart for the manufacturing process; S5. Confirm the final scheduling instruction set of the mining equipment through the dynamic correction of the process path of the production situation map and the process optimization result of the flexible scheduling Gantt chart.

[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 the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve 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 limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A mining equipment intelligent manufacturing production scheduling optimization system, characterized in that: The system includes a topology diagram 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 map generating module is used to construct a topology map representing the production status of mining equipment based on the equipment status data, material inventory data and process connection of order information of mining equipment; The initial scheduling Gantt chart generation module is used to generate an initial scheduling Gantt chart for the manufacturing process using the component task set 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 configured to extract the affected process dependency tree in the topology diagram when a disturbance event occurs, evaluate the task delay costs of the task nodes in the process dependency tree, and output the demand intensity coefficient for rescheduling the manufacturing process; The flexible scheduling Gantt chart acquisition module is used to synchronously adjust the process beat deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient 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 through the dynamic correction of the process path of the production situation map and the process optimization result of the flexible scheduling Gantt chart.

2. The mining equipment intelligent manufacturing production scheduling optimization system according to claim 1, characterized in that: The topology map generation module is specifically used to: Analyze the equipment status data into the mapping relationship between vibration spectrum characteristics, temperature rise curve and fault code; Establish a correlation matrix between safety stock threshold and material turnover path in material inventory data; generating a process path tree for the mining equipment based on the process connection of the order information; A topological diagram of the production situation of the mining equipment is constructed with the mapping relationship as the vertex, the association matrix as the edge weight, and the process path tree as the connection rule.

3. The mining equipment intelligent manufacturing production scheduling optimization system according to claim 2, characterized in that: When the demand intensity coefficient acquisition module extracts the affected process dependency tree in the topology diagram when a disturbance event occurs, it is specifically used to: Identifying the disturbance task node in the topology graph that is triggered when a disturbance event occurs; Performing cross-level reverse tracing of the disturbance task node along the process path tree, and marking all associated task nodes with time coupling constraints in the cross-level reverse tracing; Constructing a minimum impact domain subtree of the topology graph using the associated task nodes and the connected process constraint edges; The minimum impact domain subtree is used as the affected process dependency tree in the topology graph.

4. A mining equipment intelligent manufacturing production scheduling optimization system according to claim 3, characterized in that: The demand intensity coefficient acquisition module is specifically used to evaluate the task delay cost of the task node in the process dependency tree and output the demand intensity coefficient for manufacturing process rescheduling when performing the following operations: Obtaining the process immediate predecessor relationship depth of the task node in the minimum impact domain subtree; Assigning a basic hysteresis coefficient to the process dependency tree based on the depth of the immediate predecessor relationship of the process; The basic delay coefficient is added 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 in the process dependency tree.

5. The mining equipment intelligent manufacturing production scheduling optimization system according to claim 4, 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 in the process dependency tree, it is specifically used to: 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 cycle of the material corresponding to the task node are coupled to obtain the material supply shortage 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.

6. The mining equipment intelligent manufacturing production scheduling optimization system according to claim 1, characterized in that: When the flexible scheduling Gantt chart acquisition module performs synchronous adjustment of the process beat deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient, the flexible scheduling Gantt chart acquisition module is specifically used to: generating a relaxation window of the process rhythm in the manufacturing process according to the demand intensity coefficient; Adjusting the processing timing of the mining equipment task queue within the relaxation window; The load overflow tasks of the parallel equipment in the manufacturing process are dynamically allocated based on the processing timing and the equipment utilization rate.

7. A mining equipment intelligent manufacturing production scheduling optimization system according to claim 6, characterized in that: When executing the generation of the flexible scheduling Gantt chart of the manufacturing process, the flexible scheduling Gantt chart acquisition module is specifically used to: Inserting a buffer time slot into the final assembly line task sequence, and coloring and marking the task blocks of the initial scheduling Gantt chart according to the load balancing degree of the parallel devices; The colored initial Gantt chart is output as a flexible scheduling Gantt chart of the manufacturing process.

8. The mining equipment intelligent manufacturing production scheduling optimization system according to claim 2, 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 warning equipment node in the process path tree is switched to the backup equipment node; The time difference of the critical path in the process path tree after switching the standby node is checked and fed back to the topology diagram to obtain a process path correction topology diagram of the manufacturing process.

9. The mining equipment intelligent manufacturing production scheduling optimization system according to claim 8, characterized in that: Before the final scheduling instruction set generation module performs the dynamic modification of the process path of the production situation map and the process optimization result of the flexible scheduling Gantt chart to confirm the final scheduling instruction set of the mining equipment, it is specifically used to: Verifying the matching degree between the correction instruction set of the process path correction topology diagram and the coordinates of the material turnover path; When the matching degree exceeds a preset correction matching value and the process beat deviation is less than the maximum acceleration tolerance range of the equipment in the manufacturing process, a process parameter tolerance license certificate of the correction instruction set is attached to the equipment.

10. A method for optimizing production scheduling of intelligent manufacturing of mining equipment, characterized in that: The method comprises: S1. Based on the process connection of equipment status data, material inventory data and order information of mining equipment, a topological map representing the production status of mining equipment is constructed; S2. Generate an initial scheduling Gantt chart for the manufacturing process, using the component task set in the pre-acquired production order as the vertical axis and the corresponding assembly line task sequence as the horizontal axis; S3. When a disturbance event occurs, extract the affected process dependency tree in the topology diagram, evaluate the task delay costs of the task nodes in the process dependency tree, and output the cost as a demand intensity coefficient for rescheduling the manufacturing process; S4. Synchronously adjusting the process beat deviation and equipment utilization rate in the manufacturing process based on the demand intensity coefficient to generate a flexible scheduling Gantt chart for the manufacturing process; S5. Confirm the final scheduling instruction set of the mining equipment through the dynamic correction of the process path of the production situation map and the process optimization result of the flexible scheduling Gantt chart.

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