Mining construction process equipment collaborative operation method and system based on Internet of Things

Abstract

The invention provides a mining construction process equipment collaborative operation method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: firstly obtaining a mining operation task scene description set and a mining equipment collaborative capability list, building scene capability association mapping, and carrying out the dynamic adjustment to obtain a scene equipment dynamic association result; on the basis of the generated equipment collaborative operation planning scheme, performing rehearsal identification on collaborative breakpoints and complementing the collaborative breakpoints; in the execution process, collecting actual connection state and scene constraint change information, and adjusting the planning scheme to obtain an optimized scheme; and finally, generating a collaborative execution instruction, distributing the collaborative execution instruction to each equipment control module through an Internet of Things interaction channel to drive operation, and returning feedback. According to the method, efficient and accurate dynamic collaborative operation of the mining equipment is realized, the mining construction efficiency is improved, the safety risk is reduced, and the resource utilization is optimized.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more specifically, to a method and system for collaborative operation of mining construction equipment based on IoT. Background Technology

[0002] With the continuous expansion of mining scale and the increasing complexity of mining environments, higher demands are placed on the collaborative operation capabilities of mining equipment. In traditional mining operations, each piece of mining equipment often operates independently, lacking an effective collaborative mechanism. When faced with diverse operational scenarios, such as mining under different geological conditions or excavating ore bodies of different sizes, the lack of precise description of the operational scenario and comprehensive consideration of the collaborative capabilities of each piece of equipment makes it difficult to achieve efficient collaborative operation between the equipment.

[0003] Existing methods of coordinating mining equipment typically involve simple pre-construction planning with fixed schemes, making timely adjustments impossible to adapt to dynamic changes during actual operations. During operations, gaps in the coordination between equipment can easily occur, leading to low efficiency and potentially safety accidents. Furthermore, the allocation of collaborative resources lacks flexibility and precision, failing to dynamically optimize based on actual needs and resulting in resource waste. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for collaborative operation of mining construction equipment based on the Internet of Things, the method comprising: Obtain a set of operation task scenario descriptions for mining construction and a list of collaborative capabilities for each mining equipment. The set of operation task scenario descriptions includes the scenario type of mining operation, the operation objectives within the scenario, and the scenario-specific constraints. The list of collaborative capabilities includes the collaborative operation types, collaborative response methods, and collaborative resource supply range for each mining equipment. Establish a scenario capability association mapping between the set of task scenario descriptions and the list of collaborative capabilities. By using the real-time synchronized device collaboration history and scenario execution feedback from the Internet of Things, adjust the matching rules of the association mapping to obtain the dynamic association results of scenario devices. Based on the dynamic association results of scene devices, a collaborative operation planning scheme for equipment is generated. At the same time, the action sequence of each device is pre-rehearsed through the Internet of Things to identify collaborative breakpoints and generate breakpoint completion schemes. The collaborative operation planning scheme for equipment includes the collaborative role positioning of each mining device, cross-device action connection process, and collaborative resource allocation path. During the execution of the equipment collaborative operation planning scheme by each mining equipment, the actual connection status and scenario constraint change information of the collaborative actions between equipment are collected by the Internet of Things. Based on the actual connection status and scenario constraint change information, the cross-equipment action connection process and collaborative resource allocation path are adjusted to obtain the optimized equipment collaborative operation planning scheme. Based on the optimized equipment collaborative operation plan, collaborative execution instructions for mining construction equipment are generated. A collaborative instruction interaction channel is established for each equipment control module through the Internet of Things. The collaborative execution instructions for mining construction equipment are distributed to the control modules of each mining equipment, driving each equipment to complete the operation actions according to the collaborative execution instructions and send back action execution feedback.

[0005] Furthermore, embodiments of the present invention also provide an IoT-based collaborative operation system for mining construction equipment, characterized in that it includes: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the above-described IoT-based mining construction process equipment collaborative operation method by executing the machine-executable instructions.

[0006] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of the IoT-based mining construction process equipment collaborative operation system reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the IoT-based mining construction process equipment collaborative operation system to perform the above-described IoT-based mining construction process equipment collaborative operation method.

[0007] Based on the above, by acquiring a set of operational task scenario descriptions for mining construction and a list of collaborative capabilities for each mining equipment, various elements of the operational scenario and the collaborative characteristics of the equipment are obtained. Then, a scenario capability association mapping is established and dynamically adjusted, allowing for flexible adaptation to different operational task scenarios based on actual conditions. Based on the dynamic association results, a collaborative operation planning scheme is generated, and collaborative breakpoints are identified and filled through IoT pre-simulation, effectively avoiding collaborative interruptions during operations and improving the smoothness and stability of collaborative operations. During execution, the actual connection status of collaborative actions between equipment and changes in scenario constraints are collected in real time through IoT, and the cross-equipment action connection process and collaborative resource allocation path are dynamically adjusted accordingly to obtain an optimized collaborative operation planning scheme, further improving the efficiency and resource utilization of collaborative operations. The final generated collaborative execution instructions for mining construction equipment are accurately distributed to each equipment control module through the collaborative instruction interaction channel built by IoT, driving the equipment to complete the operation actions according to the instructions and transmit feedback, realizing efficient and precise collaborative operation of mining construction equipment, significantly improving the overall efficiency of mining construction, reducing safety risks, and optimizing resource utilization. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the execution flow of the IoT-based equipment collaborative operation method for mining construction provided in an embodiment of the present invention.

[0009] Figure 2 This is a schematic diagram of exemplary hardware and software components of an IoT-based collaborative operation system for mining construction equipment provided in an embodiment of the present invention. Detailed Implementation

[0010] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating an IoT-based collaborative operation method for mining construction equipment, provided in one embodiment of the present invention. The following is a detailed description of this IoT-based collaborative operation method for mining construction equipment.

[0011] Step S110: Obtain a set of operation task scenario descriptions for mining construction and a list of collaborative capabilities for each mining equipment. The set of operation task scenario descriptions includes the scenario type of mining operation, the operation objectives within the scenario, and the special constraints of the scenario. The list of collaborative capabilities includes the collaborative operation types, collaborative response methods, and collaborative resource supply range for each mining equipment.

[0012] In this embodiment, a tunnel excavation operation in underground metal mining is used as an example. First, various information related to the tunnel excavation operation is collected to construct a set of operation task scenario descriptions. The scenario type is clearly defined as an underground metal mine tunnel excavation scenario, which has specific environmental characteristics, such as a relatively enclosed working space, limited lighting conditions, and the potential presence of harmful gases such as methane. The operational objective within the scenario is set to complete tunnel excavation of a specific length and cross-sectional size within a predetermined time, while ensuring that the tunnel support quality meets relevant safety standards. Specific constraints of the scenario include the geological conditions of the working area, such as the rock hardness level and joint development, as well as safety constraints during the operation, such as ensuring that the methane concentration does not exceed the prescribed limits and that the dust concentration is controlled within a certain range.

[0013] For the list of collaborative capabilities of various mining equipment, a detailed analysis of the equipment participating in roadway excavation is required. For example, the collaborative operation types of tunneling machines include roadway excavation and loading. Their collaborative response involves receiving control commands and initiating corresponding actions within a preset time. The scope of collaborative resource supply mainly involves the continuous operating time supported by their own power system and the storage capacity of lubricating grease and hydraulic oil required during operation. The collaborative operation type of support equipment is roadway support. Its collaborative response involves installing anchor bolts and cables according to a predetermined procedure after the tunneling machine has completed a certain advance. The scope of collaborative resource supply includes the storage quantity and supply capacity of support materials. The collaborative operation type of transportation equipment is transporting the slag generated during tunneling out of the working face. Its collaborative response involves adjusting the transportation frequency and route according to the tunneling machine's operating progress. The scope of collaborative resource supply involves the loading capacity of the transportation vehicles and the coverage area of ​​the transportation route.

[0014] Step S120: Establish a scenario capability association mapping between the set of task scenario descriptions and the list of collaborative capabilities. By using the real-time synchronized device collaboration history records and scenario execution feedback from the Internet of Things, adjust the matching rules of the association mapping to obtain the dynamic association results of scenario devices.

[0015] In tunnel excavation operations, it is necessary to accurately match the various elements in the task scenario description set with the capabilities of each piece of equipment in the collaborative capability list, and to make dynamic adjustments based on historical data and real-time feedback to ensure the accuracy and applicability of the mapping results.

[0016] Step S121: Extract the key elements of each task scenario from the task scenario description set. The key elements of the scenario include the characteristics of the task objects in the scenario, the spatial layout features of the scenario, and the sequential constraints of the tasks in the scenario.

[0017] For tunnel excavation operations, the characteristics of the work object within the scenario mainly refer to the physical and mechanical properties of the rock to be excavated, such as its compressive strength, tensile strength, and integrity coefficient. These characteristics directly affect the selection of the tunneling machine and the setting of operating parameters. The spatial layout characteristics of the scenario include the designed cross-sectional shape of the tunnel (e.g., rectangular, arched), cross-sectional dimensions, the relative position of the working face and subsequent transportation channels, and the arrangement of fixed facilities (e.g., ventilation ducts, lighting equipment) within the working area. The sequential constraints of operations within the scenario are reflected in the sequential requirements of the work process. For example, tunnel excavation must be carried out first before muck removal can begin. After a certain stage of muck removal, timely support work is required to ensure the stability of the working face and prevent safety accidents such as collapses.

[0018] Step S122: Extract the key equipment collaboration items for each mining equipment from the collaborative capability list. The key equipment collaboration items include the types of work objects that the equipment can handle, the range of work space that the equipment can adapt to, and the order of work that the equipment can cooperate with.

[0019] Taking a tunneling machine as an example, the types of rock it can handle must match the physical and mechanical properties of the rock to be excavated. For instance, a certain model of tunneling machine can handle rock types with compressive strength within a certain range. The operating space of the equipment must meet the cross-sectional dimensions of the tunnel; that is, the dimensions of the tunneling machine and the range of motion of its boom should be compatible with the designed cross-section of the tunnel to ensure that excavation work can be completed within a limited space. The sequence of operations that the equipment can coordinate with is as follows: after excavation and loading, it is necessary to wait for the transport equipment to remove the slag to make room for subsequent support work. Therefore, the operation of the tunneling machine must be coordinated with the operation sequence of the transport and support equipment.

[0020] The support equipment can handle the surrounding rock of roadways, selecting appropriate support methods and materials based on the stability level of the surrounding rock. The equipment's operating space must be adaptable to the roadway's cross-section and height, ensuring flexible movement and accurate installation of the support structure within the working space. The equipment should only be deployed to the working face for support work after the tunneling machine has completed a certain advance and muck removal is largely finished, avoiding conflicts with the tunneling machine and transport equipment.

[0021] The transport equipment can handle slag generated during tunneling. Its load-bearing capacity and transport method must be compatible with the properties of the slag (such as block size and moisture content). The equipment's operating space adaptability includes the width of the transport route and turning radius, ensuring that transport vehicles can move smoothly within the tunnel. The equipment can be used in the following sequence: after the tunneling machine completes one cycle of excavation and loading, the slag should be transported out promptly to create conditions for the next round of tunneling operations.

[0022] Step S123: Initially establish the correspondence between key elements of the scene and key items of equipment collaboration, correspond the characteristics of the work objects in the scene with the types of work objects that the equipment can handle, correspond the spatial layout characteristics of the scene with the adaptability range of the equipment's work space, and correspond the sequential constraints of the work in the scene with the sequential order of the work that the equipment can cooperate with.

[0023] In a tunnel excavation scenario, the initial correspondence is established as follows: The characteristics of the work object within the scenario (the physical and mechanical properties of the rock to be excavated) are mapped to the types of work objects that the tunneling machine, support equipment, etc., can handle, ensuring that the selected equipment can effectively handle that type of work object. For example, if the compressive strength of the rock to be excavated is high, a tunneling machine suitable for hard rock excavation is selected. The spatial layout characteristics of the scenario (tunnel cross-sectional dimensions, spatial arrangement, etc.) correspond to the operational space adaptability range of each piece of equipment. For example, based on the tunnel cross-sectional dimensions, tunneling machines, support equipment, and transport equipment with appropriate machine width and height are selected to ensure that the equipment can operate normally within the operational space. The operational sequence constraints within the scenario (the order of excavation-muck removal-support) correspond to the operational sequence that each piece of equipment can cooperate in, clearly defining the operational sequence of the tunneling machine, transport equipment, and support equipment to avoid coordination problems caused by chaotic operational sequences.

[0024] Step S124: Retrieve the device collaboration history through the Internet of Things. The device collaboration history includes the collaboration participation of each device in the same scenario type in the past, the completion status of collaborative actions, and the achievement status of the work target after collaboration.

[0025] By utilizing IoT devices deployed at mining sites, such as various sensors and data acquisition terminals, real-time equipment operation data is transmitted to a data center via wireless network, allowing retrieval of historical equipment collaboration records for tunnel excavation operations. Collaboration participation records which equipment participated in collaborative operations in previous similar tunnel excavation projects, along with the duration and frequency of each device's participation. Collaboration action completion records include the execution of various actions by each device during the collaboration process, such as whether the tunnel boring machine's excavation progress reached the planned value, whether the support equipment's support work was completed as required, and whether the slag transport volume by the transportation equipment met the schedule requirements. Post-collaboration work objective achievement reflects whether the total tunnel excavation progress, support quality, and work efficiency achieved the predetermined work objectives after the collaborative operation.

[0026] Step S125: Select equipment scenario combinations from the equipment collaboration history records that meet the preset standards for collaborative action completion and achieve the work objectives, and extract the correspondence between the key elements of the selected equipment scenario combinations and the key items of equipment collaboration.

[0027] In the equipment collaboration history of tunnel excavation scenarios, it is necessary to screen a large number of equipment scenario combinations. Preset standards for the completion of collaborative actions can be set as follows: the excavation completion rate of the tunnel boring machine reaches a certain percentage or higher; the support action qualification rate of the support equipment is above a specified value; and the transportation action connection qualification rate of the transportation equipment meets requirements. The achievement requirements for operational objectives include the total tunnel excavation footage completion rate reaching the planned value and the support quality score being above the passing score. Through analysis of historical data, equipment scenario combinations that simultaneously meet the above conditions are selected, such as collaborative operation combinations of a specific model of tunnel boring machine, support equipment, and transportation equipment under certain rock conditions and tunnel cross-sectional dimensions.

[0028] From the selected equipment scenario combinations, the correspondence between key scenario elements and key equipment coordination items is further extracted. For example, in scenarios where the rock compressive strength is within a certain range, the types of work objects that a specific model of tunneling machine can handle match it, resulting in high work efficiency and high quality of action completion; in roadway scenarios with specific cross-sectional dimensions, the working space adaptability range of a certain support equipment can well meet the requirements, resulting in a high qualification rate for support operations; under the constraint of excavation-muck removal-support sequence, the possible sequence of cooperative operations of transportation equipment can be closely linked with the work progress of the tunneling machine and support equipment, ensuring the smooth progress of the entire operation process.

[0029] Step S1251: Extract collaborative action completion data and job target achievement data for all device scenario combinations from the device collaboration history. The collaborative action completion data includes action completion rate, action connection qualification rate, and action execution error rate. The job target achievement data includes target completion rate and target completion quality score.

[0030] For the equipment collaboration history of tunnel excavation operations, relevant data for each equipment scenario combination is extracted one by one. The action completion rate in the collaborative action completion data refers to the ratio of the actual number of actions completed by the equipment to the planned number of actions. For example, if a tunneling machine plans to excavate 10 cycles and actually completes 9 cycles, the action completion rate is 90%. The action connection qualification rate refers to the ratio of the number of smooth connections between adjacent equipment actions to the total number of connections. For example, after the tunneling machine completes excavation, the proportion of times the transport equipment arrives at the loading position on time. The action execution error rate refers to the degree of deviation between the actual parameters and the set parameters during equipment action execution, such as the deviation rate between the tunneling machine's excavation cross-section dimensions and the design dimensions. The target completion rate in the work target achievement data refers to the ratio of the actual completed work target (such as total advance) to the planned work target. The target completion quality score is a comprehensive evaluation of the work results based on relevant quality standards. For example, the support quality score covers multiple indicators such as the anchoring force of anchor bolts and the preload of anchor cables.

[0031] Step S1252: Set preset standards for the completion of collaborative actions. If the action completion rate is not lower than the preset completion ratio, the action connection qualification rate is not lower than the preset connection ratio, and the action execution error rate is not higher than the preset error ratio, then the completion of collaborative actions is determined to meet the preset standards.

[0032] In tunnel excavation scenarios, preset standards are set based on operational requirements and equipment performance. For example, the preset completion rate is set at 90%, meaning the actual number of actions completed by the equipment cannot be less than 90% of the planned number; the preset connection rate is set at 95%, requiring that the number of smooth connections between adjacent equipment actions accounts for more than 95% of the total number of connections; and the preset error rate is set at 5%, meaning the deviation of the equipment's action execution parameters cannot exceed 5% of the set parameters. Only when the action completion rate, action connection rate, and action execution error rate of the equipment scenario combination all meet the above preset standards can the collaborative action completion be deemed to meet the requirements.

[0033] Step S1253: Set the achievement requirements for the work objectives. If the target completion rate is not lower than the preset target ratio and the target completion quality score is not lower than the preset quality score, then the work objectives are deemed to have been achieved.

[0034] Regarding the achievement of work objectives, the preset target ratio can be set to 95%, meaning that the actual completed work objectives (such as total footage) must reach more than 95% of the planned target; the preset quality score is set to 80 points (out of 100), and the target completion quality score must be 80 points or above. When both the target completion rate and the target completion quality score of the equipment scenario combination meet the above requirements, the work objective achievement is deemed to have met the standards.

[0035] Step S1254: Compare the collaborative action completion data with the preset standards and the work target achievement data with the standard requirements, and select equipment scenario combinations that simultaneously meet the preset standards for collaborative action completion and the standard for work target achievement to form a list of effective equipment scenario combinations.

[0036] The extracted collaborative action completion data for each equipment scenario combination is compared one by one with preset standards, and the work target achievement data is compared with the standard requirements. For example, if a certain equipment scenario combination has an action completion rate of 92% (higher than the preset completion rate of 90%), an action connection qualification rate of 96% (higher than the preset connection rate of 95%), an action execution error rate of 4% (lower than the preset error rate of 5%), a target completion rate of 96% (higher than the preset target rate of 95%), and a target completion quality score of 85 points (higher than the preset quality score of 80 points), then this equipment scenario combination is selected and added to the list of valid equipment scenario combinations.

[0037] Step S1255: Select device scene combinations of the same scene type from the list of valid device scene combinations and classify them into the same scene combination group.

[0038] The list of valid equipment scenario combinations is categorized according to scenario type. Since this embodiment revolves entirely around the scenario of tunnel excavation, all equipment scenario combinations belonging to this scenario type are grouped into the same scenario combination group for subsequent pattern extraction.

[0039] Step S1256: For each scenario combination group, extract the key elements of the scenario and the corresponding key items of device collaboration for each device scenario combination within the group, and record the correspondence and frequency of each key element of the scenario and the key items of device collaboration.

[0040] For each tunnel excavation scenario combination, key elements of the scenario are extracted, such as the physical and mechanical properties of rock under different conditions, tunnel cross-sectional dimensions, and work sequence, as well as corresponding key equipment coordination items, such as the types of work objects that the tunneling machine, support equipment, and transportation equipment can handle, their operational space adaptability, and their possible work sequence. Simultaneously, the correspondence between each scenario's key elements and the key equipment coordination items is recorded in detail. For example, the compressive strength of a certain rock corresponds to the types of work objects that a certain type of tunneling machine can handle, and the frequency of these correspondences within the group.

[0041] Step S1257: Count the frequency of the correspondence between key elements of the same scene and key items of collaboration of different devices. If the proportion of the frequency of the correspondence between any key item of collaboration of a device and the key elements of the scene exceeds the preset proportion of the total frequency of the correspondence of key elements of the scene, then the key item of collaboration of the device is determined to be the main matching item of the key elements of the scene.

[0042] During the statistical process, for the same key element in a scenario (such as the rock compressive strength within a specific range), the frequency of its correspondence with different key items of equipment (the types of work objects that different models of tunneling machines can handle) is counted. The preset percentage can be set according to the actual situation, for example, 30%. If the proportion of the corresponding frequency of the work object type that a certain model of tunneling machine can handle with the rock compressive strength exceeds 30% of the total corresponding frequency of key elements in that scenario, then the work object type that model of tunneling machine can handle is determined as the main matching item for the key element of rock compressive strength in that scenario.

[0043] Step S1258: Analyze the changing patterns of the main matching items of key elements of the scene and key items of equipment collaboration under different scene types, and record the changes in the main matching items and the reasons for the changes when the scene type changes.

[0044] Although this embodiment only involves a tunnel excavation scenario, from the perspective of technical universality, it is necessary to analyze the changing patterns under different scenario types. For example, when the scenario type changes from tunnel excavation to open-pit mining, the characteristics of the work object and spatial layout features among the key elements of the scenario will change significantly, and the main matching items of the key equipment coordination items will also change accordingly. For the tunnel excavation scenario, the main matching item may be small to medium-sized equipment suitable for small-space operations; while in the open-pit mining scenario, the main matching item may be large-scale, high-efficiency equipment. Recording these changes and their reasons will help to quickly adapt the method when applying it in other scenarios.

[0045] Step S1259: Summarize the main matching items and change patterns of key elements of the scene and key items of device collaboration under all scene types, form a document of corresponding patterns of key elements of the scene and key items of device collaboration, and store the document of corresponding patterns in association with the list of effective device scene combinations.

[0046] The key elements of the scenarios and key items for equipment collaboration obtained from the above analysis, along with their variation patterns under different scenario types, are summarized and compiled into a corresponding pattern document. In the tunnel excavation scenario, this document details the patterns related to rock physical and mechanical properties and tunneling machine selection, tunnel cross-sectional dimensions and equipment spatial adaptability, and the order of operations and equipment coordination. This document is linked and stored with a list of effective equipment scenario combinations for easy retrieval and application when establishing and adjusting scenario capability association mappings later.

[0047] Step S126: Collect scenario execution feedback through the Internet of Things. The scenario execution feedback includes the connection problems of device collaborative actions in past scenario executions and the collaborative adjustment needs caused by changes in scenario constraints.

[0048] During tunnel excavation, IoT devices collect real-time feedback on the execution of tasks. Issues with the coordination of equipment actions may manifest as follows: after the tunneling machine completes excavation, the transport equipment fails to reach the loading position in a timely manner, leading to slag accumulation and affecting subsequent operations; or the support equipment's support operations may conflict with the tunneling machine's exit route, impacting operational efficiency. Changes in scenario constraints may necessitate adjustments to the coordination process. These adjustments might be required due to sudden changes in geological conditions (such as encountering a fault), necessitating changes to the original tunneling parameters and consequently altering the operational plans for both support and transport equipment; or a sudden increase in methane concentration in the work area necessitating a halt to tunneling operations and activation of ventilation equipment, requiring both transport and support equipment to suspend operations and adjust their status.

[0049] Step S127: Based on the connection issues recorded in the scenario execution feedback, adjust the corresponding priority of the key elements of the scenario and the key items of device collaboration. If the correspondence between the device's operating space adaptation range and the scenario's spatial layout characteristics is prone to connection issues in any type of scenario, then increase the corresponding priority of other key items of device collaboration in that type of scenario.

[0050] In tunnel excavation scenarios, if coordination issues repeatedly arise due to a mismatch between the equipment's operating space adaptability and the scene's spatial layout—for example, a support equipment model with a large body width may conflict with the passageway of transport equipment in a tunnel with a specific cross-sectional size, leading to poor coordination between support and transport operations—it is necessary to lower the priority of the corresponding relationship between the equipment's operating space adaptability and the scene's spatial layout characteristics, while simultaneously increasing the priority of other key coordination items for equipment in this scenario, such as the possible sequence of operations that the equipment can perform. By adjusting the priorities, subsequent correlation mapping will place greater emphasis on the coordination of the work sequence between equipment, thereby reducing the occurrence of coordination problems.

[0051] Step S128: Based on the collaborative adjustment requirements recorded in the scenario execution feedback, supplement the matching rules for scenario capability association mapping. If any type of scenario constraint change often leads to insufficient supply range of device collaborative resources, then add the resource supply range adaptation requirements under scenario constraint changes to the matching rules.

[0052] In tunnel excavation scenarios, due to changes in scenario constraints (such as encountering complex geological conditions that slow down the excavation speed, causing the consumption rate of support materials to exceed expectations), the supply range of coordinated resources for equipment often becomes insufficient. For example, the storage capacity of support materials in the support equipment may not meet the needs of long-term operations, leading to the interruption of support work. To address the aforementioned need for coordinated adjustment, the matching rules of the scenario capability association mapping are supplemented with requirements for the adaptation of the resource supply range under changes in scenario constraints. That is, when selecting support equipment, it is required not only to consider its resource supply range under normal conditions, but also to assess whether its resource supply range can meet the adjusted operational needs under possible changes in scenario constraints, such as increasing the storage capacity of support materials or improving the supply efficiency of support materials.

[0053] Step S129: Based on the adjusted priority and the supplemented matching rules, reconstruct the correspondence between key elements of the scene and key items of device collaboration to form a preliminary version of the scene capability association mapping.

[0054] Taking into account the adjusted priorities and supplemented matching rules, the correspondence between key elements of the scenario and key items of equipment collaboration is reconstructed. In the tunnel excavation scenario, according to the new priority order, the characteristics of the work objects in the scenario, the constraints of the sequence of work in the scenario, and the spatial layout features of the scenario are matched with the key items of equipment collaboration in sequence. Combined with the supplemented resource supply range adaptation requirements, it is ensured that the selected equipment can meet the scenario requirements in terms of work object processing, work sequence coordination, spatial adaptation, and resource supply, thus forming a preliminary version of the scenario capability association mapping.

[0055] Step S1210: Verify the preliminary version by selecting a combination of devices that have not participated in the collaboration in the past through the Internet of Things. If the device collaboration actions are smooth and meet the scenario constraints during the verification, the preliminary version is determined to be the result of dynamic association of scenario devices. If there are connection problems or the constraints are not met, repeat the steps of adjusting the matching rules until the verification is successful.

[0056] Using IoT technology, a combination of equipment scenarios that have not previously participated in collaboration is selected, namely a new combination of tunneling machines, support equipment, and transportation equipment. Collaborative operation verification is conducted in a simulated environment according to a preliminary version of the scenario capability association mapping. During verification, the connection status of equipment collaborative actions is monitored in real time, such as whether the start and end times of each equipment action are coordinated, whether the work process is smooth, and whether it meets scenario constraints, such as whether the equipment working space meets requirements and whether resource supply is sufficient. If the verification results show that the equipment collaborative actions are smoothly connected and meet scenario constraints, it indicates that the preliminary version of the association mapping is effective and can be identified as the dynamic association result of the scenario equipment. If there are connection problems or non-compliance with constraints, such as delays in equipment action connection or insufficient resource supply, it is necessary to return to step S128, readjust the matching rules, supplement new adaptation requirements, and then perform verification again until verification is successful.

[0057] Step S1211: Store the dynamic association results of scene devices according to scene type, and at the same time associate the corresponding matching rule adjustment records and verification results.

[0058] The dynamic association results of scene equipment obtained in tunnel excavation scenarios are categorized and stored according to scene type for quick retrieval in subsequent operations with the same scenario. Simultaneously, the corresponding matching rule adjustment records (such as specific details of priority adjustments, supplementary matching rule clauses, etc.) and verification results (equipment collaboration performance during verification, existence of problems, and resolution status, etc.) are also stored together. This facilitates the traceability and management of the association mapping results, allowing for quick access to relevant records when encountering similar scenarios or problems again, thus improving processing efficiency.

[0059] Step S130: Generate a collaborative operation planning scheme for equipment based on the dynamic association results of scene equipment. At the same time, use the Internet of Things to pre-rehearse the action sequence of each equipment to identify collaborative breakpoints and generate a breakpoint completion scheme. The collaborative operation planning scheme includes the collaborative role positioning of each mining equipment, cross-equipment action connection process, and collaborative resource allocation path.

[0060] In tunnel excavation operations, the dynamic association results of the equipment within the scenario clarify the matching relationship between each piece of equipment and the scenario. Generating a collaborative operation plan based on this is the core content guiding the actual collaborative operation of the equipment. Simultaneously, using the Internet of Things (IoT) for pre-simulation and breakpoint identification and completion can identify potential problems in advance, ensuring the feasibility and efficiency of the plan.

[0061] Step S131: Extract the list of compatible devices for each work scenario and the scenario adaptation criteria for each device from the dynamic association results of the scenario devices. The scenario adaptation criteria include the matching points between key items of device collaboration and key elements of the scenario.

[0062] Based on the dynamic association results of scene equipment, a list of compatible equipment for roadway excavation operations is extracted, including compatible tunneling machine models, support equipment models, and transportation equipment models. The scene adaptation criteria for each piece of equipment detail the matching points between key equipment collaboration items and key scene elements. For example, the types of work objects that the tunneling machine can handle match the physical and mechanical properties of the rock to be excavated, and its operating space adaptability meets the roadway cross-sectional size requirements; the types of work objects that the support equipment can handle match the stability level of the surrounding rock in the roadway, and its operating space adaptability meets the spatial requirements of the support operation; the types of work objects that the transportation equipment can handle match the properties of the slag, and its operating space adaptability meets the requirements of the transportation route.

[0063] Step S132: Based on the importance of the matching points of each device in the scene adaptation criteria, assign a collaborative role to each device. If the matching points of a device are concentrated on the correspondence between the characteristics of the work objects in the scene and the types of work objects that the device can handle, then the device is positioned as the main role of work execution; if the matching points are concentrated on the correspondence between the spatial layout features of the scene and the adaptability range of the device's work space, then the device is positioned as the work space coordinator role.

[0064] The importance of the matching points for each device in the scenario adaptation criteria is evaluated. In tunnel excavation, the matching point for the tunnel boring machine (TBM) mainly focuses on the correspondence between the characteristics of the work object within the scenario (rock physical and mechanical properties) and the types of work objects the equipment can handle. It plays a leading role in the entire operation process, responsible for tunnel excavation; therefore, the TBM is positioned as the leading role in operation execution. The matching point for the transportation equipment involves not only the characteristics of the work object (slag properties) but also is closely related to the spatial layout characteristics of the scenario (transportation routes). It needs to rationally plan transportation routes within the work space and coordinate with the work spaces of the TBM and support equipment; therefore, the transportation equipment is positioned as the coordinating role in work space. The matching point for the support equipment mainly focuses on the correspondence between the characteristics of the work object within the scenario (surrounding rock stability) and the types of work objects the equipment can handle. It also needs to adapt to the spatial layout characteristics of the scenario. However, its core function is to provide support after the TBM excavates, ensuring the safety of the working face; therefore, it can also be positioned as an auxiliary role in operation execution, cooperating with the TBM to complete the task.

[0065] Step S133: Collect the collaborative role positioning information of all devices, analyze the action dependencies between different roles, and if the action of the main role in job execution is completed, the spatial adjustment action of the work space coordination role needs to be triggered. Then, the action completion signal of the main role in job execution is used as the starting condition for the action of the work space coordination role, and the initial link of the cross-device action connection process is constructed.

[0066] Collect the collaborative role positioning information of the tunneling machine (the main role in operation execution), transportation equipment (the role in coordinating the workspace), and support equipment (the role in assisting operation execution). Analyze the action dependencies between each role. For example, after the tunneling machine completes a cycle of excavation and loading, it needs to send an action completion signal to the transportation equipment. Upon receiving this signal, the transportation equipment initiates a spatial adjustment action to enter the working face for slag transportation. At this time, the tunneling machine's action completion signal is the initiation condition for the transportation equipment's action. After the transportation equipment completes slag transportation, it sends an action completion signal to the support equipment. Upon receiving the signal, the support equipment enters the working face to perform support operations. The transportation equipment's action completion signal becomes the initiation condition for the support equipment's action. Based on these action dependencies, construct a preliminary link for the cross-equipment action connection process: tunneling machine operation → transportation equipment operation → support equipment operation.

[0067] Step S134: Combining the constraints of the order of operations within the scenario and the collaborative response methods of each device, optimize the initial link of the cross-device action connection process. Set an action confirmation node at the action connection point of adjacent devices. After the preceding device completes the action, it needs to send an action completion confirmation message to the subsequent device. After receiving the confirmation message, the subsequent device starts its own action, forming a cross-device action connection process.

[0068] Based on the constraints of the operational sequence (excavation-muck removal-support) within the tunnel excavation scenario, the initial workflow is optimized by considering the coordinated response methods of each piece of equipment. The tunneling machine's coordinated response method is to send an action completion signal within 10 seconds of completing its action; the transportation equipment's coordinated response method is to initiate its action within 20 seconds of receiving the signal; and the support equipment's coordinated response method is to initiate its action within 30 seconds of receiving the signal. Action confirmation nodes are set at the connection points between adjacent equipment actions. For example, after the tunneling machine completes the excavation and loading action, it sends an action completion confirmation message to the transportation equipment via the Internet of Things (IoT). This message includes key data such as the action completion time and the progress made. Upon receiving the confirmation message, the transportation equipment's control module verifies the information and initiates the transportation action after confirmation. Similarly, after completing the transportation action, the transportation equipment sends an action completion confirmation message to the support equipment. The support equipment receives and confirms the message before initiating its support action. By setting action confirmation nodes, the orderly connection of each piece of equipment's actions is ensured, forming an optimized cross-equipment action connection process.

[0069] Step S135: Based on the collaborative resource supply range of each device and the operation objectives within the scene, plan the collaborative resource allocation path. If the collaborative resource supply range of the device covers multiple operation areas, prioritize the resource output port closest to the operation execution point as the allocation starting point and the operation execution point as the allocation endpoint, avoiding obstacle areas in the scene spatial layout features, and determine the basic path for resource allocation.

[0070] For resource allocation within tunnel excavation scenarios, such as the allocation of support materials (rock bolts, anchor cables, concrete, etc.), the collaborative resource supply range of support equipment covers multiple material storage areas. In this case, based on the distance between each storage area and the work execution point (tunnel excavation face), the nearest resource output port should be prioritized as the allocation starting point. Using the work execution point as the allocation endpoint, the planned path must avoid obstacles in the scene's spatial layout, such as ventilation equipment, lighting facilities, and temporarily stored tools within the tunnel. Through on-site surveys and spatial modeling, an unobstructed path from the nearest resource output port to the work execution point is determined as the basic path for resource allocation.

[0071] Step S136: Set up resource transfer nodes on the basic path. If the resource allocation distance exceeds the resource delivery capacity of a single device, the transfer nodes will be used to realize the relay delivery of resources from multiple devices, supplementing the resource storage capacity and transfer time requirements of the resource transfer nodes, and improving the collaborative resource allocation path.

[0072] If the resource allocation distance from the resource output port to the work execution point exceeds the single-device resource transport capacity of the transport equipment (e.g., the maximum transport distance of the transport equipment is 500 meters, while the basic path length is 800 meters), a resource transfer node needs to be set up on the basic path. A transfer node is set up 300 meters from the resource output port. The transport equipment moves resources from the output port to the transfer node, and then another transport equipment takes over from the transfer node to the work execution point. Simultaneously, the resource temporary storage capacity requirements of the transfer node must be supplemented, such as ensuring the loading capacity of at least two transport equipment to guarantee continuous resource transport. The transfer time requirement is that after the previous transport equipment arrives at the transfer node, the next transport equipment must complete resource transfer and depart within 10 minutes to reduce transfer waiting time and improve the collaborative resource allocation path.

[0073] Step S137: Transform the collaborative role positioning of each device, the cross-device action connection process, and the collaborative resource allocation path into a digital action sequence through the Internet of Things, simulate each device to perform work according to the digital action sequence, and record the action execution time, action connection interval, and resource allocation time in real time.

[0074] By leveraging IoT technology, the collaborative role positioning information (such as role type and scope of responsibility) of tunneling machines, transportation equipment, and support equipment, the cross-equipment action connection process (the sequence of actions of each equipment, initiation conditions, confirmation nodes, etc.), and the collaborative resource allocation path (path start point, end point, transfer nodes, path length, etc.) are transformed into digital action sequences. These digital action sequences are stored in the form of instruction codes, with each instruction code corresponding to a specific action or operation of a device. The digital action sequences are loaded into simulation software, simulating the execution of operations by each device in a virtual tunneling scenario. During the simulation, the execution time of each device's actions (such as the time taken for the tunneling machine to complete one excavation cycle, the time taken for the transportation equipment to complete one transport, and the time taken for the support equipment to complete one support), action connection intervals (such as the interval between the tunneling machine completing an action and the transportation equipment initiating an action, and the interval between the transportation equipment completing an action and the support equipment initiating an action), and resource allocation time (such as the transportation time of resources from the output port to the transfer node, the transportation time from the transfer node to the work execution point, and the transfer time of the transfer node, etc.) are recorded in real time.

[0075] Step S138: Analyze the recorded data to identify collaborative breakpoints. If the action connection interval between any two devices exceeds the preset connection time, it is determined to be an action connection breakpoint. If the resource allocation time exceeds the preset allocation time and does not reach the resource demand point, it is determined to be a resource allocation breakpoint.

[0076] Data recorded during the simulation is analyzed to identify coordination breakpoints. Preset connection times are set based on the equipment's coordination response methods and operational requirements. For example, the preset interval for action connection between the tunneling machine and the transport equipment is 30 seconds, and the preset connection time between the transport equipment and the support equipment is 40 seconds. If the analysis reveals that the transport equipment starts its action 45 seconds after the tunneling machine completes its action, exceeding the preset connection time of 30 seconds, it is identified as an action connection breakpoint. Preset allocation time is set based on the length of the resource allocation path and the equipment's transport capacity. For example, the preset time for resource allocation from the resource output port to the work execution point is 20 minutes. If resource allocation takes 25 minutes and the work execution point has not yet been reached, it is identified as a resource allocation breakpoint.

[0077] Step S139: For the action connection breakpoint, query the corresponding action confirmation node in the cross-device action connection process, extend the confirmation information sending waiting time after the preceding device completes the action to the preset sending waiting threshold, or reduce the action start preparation time after the subsequent device receives the confirmation information to the preset preparation threshold, and generate a breakpoint completion scheme.

[0078] For identified action connection breakpoints, such as the connection breakpoint between the tunneling machine and the transportation equipment, query the corresponding action confirmation node in the cross-equipment action connection process. This node is located at the stage where the tunneling machine sends confirmation information to the transportation equipment after completing its action.

[0079] Step S1391: Filter out action connection breakpoints from the identified collaborative breakpoints, and record the preceding device identifier, subsequent device identifier, and corresponding action confirmation node position involved in each action connection breakpoint.

[0080] In the collaborative breakpoints, the action connection breakpoints are selected, and it is determined that the preceding equipment involved in the breakpoint is the tunneling machine (identified as J01), and the subsequent equipment is the transportation equipment (identified as Y01). The corresponding action confirmation node is the communication link between the signal sending unit of the tunneling machine control module and the signal receiving unit of the transportation equipment control module.

[0081] Step S1392: Query the preset information transmission process of the action confirmation node in the cross-device action connection process, and obtain the triggering conditions, information transmission path, and processing steps of the subsequent device after receiving the confirmation information.

[0082] According to the preset information transmission process, the trigger condition for the tunneling machine to send confirmation information is that the position sensor of its working component detects that the excavation action has completely stopped and the bucket has returned to its initial position. The information transmission path is: tunneling machine control module → IoT gateway → transportation equipment control module. The subsequent processing steps after the equipment receives the confirmation information include information verification (verifying the completeness and accuracy of the information), parameter configuration (adjusting the loading parameters of the transportation equipment according to the working progress in the information), and start-up preparation (checking whether the power system, braking system, etc. of the transportation equipment are normal).

[0083] Step S1393: Retrieve detailed data of the action connection breakpoint during the rehearsal process via the Internet of Things, including the actual completion time of the preceding device's action, the time when the preceding device sends the confirmation information, the time when the confirmation information is transmitted to the subsequent device, the preparation time of the subsequent device after receiving the confirmation information, and the actual start time of the subsequent device's action.

[0084] By retrieving detailed data from the pre-rehearsal process through the IoT data center, we can obtain the actual completion time of the tunneling machine's actions as T1, the time to send the confirmation information as T2, the time to transmit the confirmation information to the transportation equipment as T3, the preparation time of the transportation equipment after receiving the confirmation information as T4, and the actual start time of the action as T5.

[0085] Step S1394: Calculate the time consumed in each step. The time consumed from the completion of the preceding device action to the sending of the confirmation information is the sending waiting time, and the time consumed from the subsequent device receiving the confirmation information to the start action is the start preparation time.

[0086] The sending wait time is T2-T1, and the startup preparation time is T5-T3.

[0087] Step S1395: If the proportion of the sending waiting time to the total connection interval exceeds the preset sending proportion, it is determined that the confirmation information sending waiting time after the completion of the preceding device action needs to be extended. Analyze the reasons why the sending waiting time is less than the preset sending waiting threshold. If the insufficient sending waiting time is caused by the delay in the detection of the completion of the preceding device action, adjust the sensitivity of the action completion detection and extend the detection time to ensure that the confirmation information is sent only after the action is completely completed. At the same time, set the minimum threshold for the sending waiting time to ensure that the action has been stably completed before the confirmation information is sent.

[0088] The total connection interval time is T5-T1. The preset transmission ratio is 40%, and the preset transmission waiting threshold is 15 seconds. If the transmission waiting time (T2-T1) accounts for more than 40% of the total connection interval time (T5-T1), but the transmission waiting time is less than 15 seconds, the analysis revealed that it was due to a delay in the detection of the tunneling machine's action completion. The position sensor issued an action completion signal before the action had completely stopped, resulting in insufficient transmission waiting time. In this case, the detection sensitivity of the position sensor was adjusted, and the number of detections and the detection duration were increased to ensure that the action was confirmed only after it had completely stopped. At the same time, the minimum threshold for transmission waiting time was set to 10 seconds, meaning that at least 10 seconds should be waited after the action is completed to ensure that the equipment status is stable before sending the confirmation information, thereby extending the transmission waiting time to the preset transmission waiting threshold.

[0089] Step S1396: If the proportion of startup preparation time to total connection interval time exceeds the preset preparation proportion, it is determined that the startup preparation time of subsequent devices after receiving confirmation information needs to be reduced. Analyze the reasons why the startup preparation time exceeds the preset preparation threshold. If it is because the parameter self-check process before subsequent devices start takes longer than the preset self-check time, optimize the parameter self-check process, reduce the self-check items of non-critical parameters, or execute some self-check items and confirmation information receiving process in parallel to reduce the startup preparation time to within the preset preparation threshold.

[0090] The preset preparation time is 50%, and the preset preparation threshold is 20 seconds. If the startup preparation time (T5-T3) accounts for more than 50% of the total connection interval time (T5-T1), and the startup preparation time exceeds 20 seconds, the analysis shows that the parameter self-check process before the transportation equipment starts includes 20 parameter checks. Some non-critical parameters (such as equipment casing temperature) take a long time to check, causing the total self-check time to exceed the preset self-check time by 15 seconds. The parameter self-check process is optimized by reducing the number of non-critical parameter self-check items by 5, and by executing some parallel self-check items (such as power system voltage and current checks) in parallel with the confirmation information reception process, thereby reducing the startup preparation time to within 20 seconds.

[0091] Step S1397: Generate verification standards for the adjustment scheme. If the transmission waiting time is extended, verify whether the actions of the preceding devices are completed stably within the adjusted transmission waiting time. If the startup preparation time is reduced, verify whether the subsequent devices can start normally and whether the action accuracy meets the requirements within the adjusted startup preparation time.

[0092] For the adjustment plan that extends the sending waiting time, the verification standard is that within the adjusted sending waiting time, the vibration and position sensors of the tunneling machine detect whether the equipment's actions are completed stably. The vibration amplitude must be less than the preset vibration threshold, and the position deviation must be within the preset position deviation range. For the adjustment plan that reduces the start-up preparation time, the verification standard is that the transport equipment can successfully start its actions within the adjusted start-up preparation time, and the initial running speed, loading position accuracy, and other action accuracy indicators after start-up meet the operational target requirements of the scenario.

[0093] Step S1398: Integrate the adjustment scheme and verification standard to form a breakpoint completion scheme for the breakpoint of the action connection.

[0094] The specific adjustment parameters for extending the waiting time for tunneling machine confirmation information transmission (such as detection sensitivity parameters and minimum waiting threshold), optimization measures for reducing the start-up preparation time of transportation equipment (such as reducing self-inspection items and parallel execution processes), and corresponding verification standards are integrated together to form a breakpoint completion scheme for the connection breakpoint of this action.

[0095] Step S1399: Repeat the above steps to generate a corresponding breakpoint completion scheme for each action connection breakpoint, and summarize all action connection breakpoint completion schemes to form a set of action connection breakpoint completion schemes.

[0096] For other identified action connection breakpoints (such as the connection breakpoint between the transport equipment and the support equipment), analyze and process them according to the process of steps S1391 to S1398 to generate their respective breakpoint completion schemes. Then, summarize all the action connection breakpoint completion schemes to form a set of action connection breakpoint completion schemes.

[0097] Step S1310: For resource allocation breakpoints, optimize the number of transit nodes in the collaborative resource allocation path, or adjust the resource delivery rate to a preset rate range so that the resource allocation time meets the preset requirements, and generate a breakpoint completion scheme.

[0098] For resource allocation breakpoints, such as those where the resource allocation time from the resource output port to the job execution point exceeds the preset allocation time, if the original path has two transfer nodes causing excessive transfer time, the number of transfer nodes can be optimized to one, shortening the transfer time. Alternatively, if the resource delivery rate of the transportation equipment is lower than the preset rate range, the engine speed and transmission system parameters of the transportation equipment can be adjusted to increase the delivery rate to the preset range, thereby reducing the resource transit time. Through these optimization measures, the resource allocation time is brought to the preset requirement, generating a breakpoint completion scheme for the resource allocation breakpoint.

[0099] Step S1311: Integrate the collaborative role positioning, cross-device action connection process, collaborative resource allocation path and breakpoint completion scheme to form a collaborative equipment operation planning scheme.

[0100] The collaborative roles of each piece of equipment are defined (tunneling machine as the main operator, transport equipment as the coordinator of the work space, and support equipment as the auxiliary operator), the optimized cross-equipment action connection process (including action confirmation nodes and information transmission mechanisms), the comprehensive collaborative resource allocation path (including basic paths and transfer node settings), and all breakpoint completion schemes (a set of action connection breakpoint completion schemes and resource allocation breakpoint completion schemes) are integrated. During the integration process, it is ensured that all parts are coordinated and conflict-free, forming a complete and feasible equipment collaborative operation plan to guide the actual collaborative operation of each piece of equipment in tunnel excavation.

[0101] Step S140: During the execution of the equipment collaborative operation planning scheme by each mining equipment, the actual connection status and scenario constraint change information of the collaborative actions between equipment are collected through the Internet of Things. Based on the actual connection status and scenario constraint change information, the cross-equipment action connection process and collaborative resource allocation path are adjusted to obtain the optimized equipment collaborative operation planning scheme.

[0102] During tunnel excavation, when equipment performs collaborative operations according to the planned scheme, the actual situation may differ from the pre-simulation. Therefore, it is necessary to collect data in real time and make dynamic adjustments to ensure the smooth progress of the operation.

[0103] Step S141: Collect the actual start time, actual completion time, and action execution accuracy of the device actions, as well as the time when the device receives the action start signal and sends the action completion signal. Calculate the actual interval time between actions of adjacent devices, and record whether the action execution accuracy meets the operational target requirements within the scenario. This forms the actual connection status data of the collaborative actions between devices. The actual interval time is the time difference between the time when the subsequent device receives the action start signal and the time when the preceding device sends the action completion signal.

[0104] The actual start and finish times of the tunneling machine, transport equipment, and support equipment are collected by time sensors installed on each device. For example, the actual start time of excavation by the tunneling machine is t1s, and the actual finish time is t1e; the actual start time of transport by the transport equipment is t2s, and the actual finish time is t2e; the actual start time of support by the support equipment is t3s, and the actual finish time is t3e. Precision detection sensors collect the execution accuracy of each device's actions, such as the dimensional deviation of the excavation section of the tunneling machine and the angular deviation of the anchor bolts installed by the support equipment. Simultaneously, the time when the equipment receives the action start signal and sends the action completion signal is recorded through an IoT communication module. For example, the time when the tunneling machine sends the completion signal is t1send, the time when the transport equipment receives the start signal is t2recv, the time when the transport equipment sends the completion signal is t2send, and the time when the support equipment receives the start signal is t3recv. The actual interval time between actions of adjacent devices is calculated. For example, the interval between the tunneling machine and the transport equipment is t2recv-t1send, and the interval between the transport equipment and the support equipment is t3recv-t2send. Record whether the execution accuracy of each device's actions meets the operational target requirements within the scenario, such as whether the deviation of the excavation cross-section size is within ±50mm, whether the deviation of the anchor bolt angle is within ±3°, etc. Integrate the above data to form actual connection status data of the coordinated actions between devices.

[0105] Step S142: Collect data on changes in geological conditions, spatial obstacles, and environmental parameters in the work area. The collected data on changes in geological conditions, spatial obstacles, and environmental parameters in the work area together constitute the information on changes in scene constraints.

[0106] Data on changes in geological conditions within the work area are collected using methods such as ground-penetrating radar and borehole sampling. Examples include a sudden increase in the hardness of rocks ahead of the work area, the appearance of cracks or faults. Laser scanning technology and visual sensors are used to collect data on changes in the location of spatial obstacles, such as new debris falling from the top of the tunnel due to loosened rocks, or temporary obstacles caused by equipment malfunctions on the transport route. Environmental sensors are used to collect data on changes in environmental parameters in the work area, such as increased methane concentration, increased dust concentration, and increased temperature. All of this data collectively constitutes information on changes in scene constraints, reflecting the dynamic changes in scene conditions during the operation.

[0107] Step S143: Transmit the actual connection status data and scenario constraint change information to the data processing node in real time for classification and organization, and establish data association according to device identification and time sequence, so that the actual connection status data at each time point corresponds to unique scenario constraint change information.

[0108] Actual connection status data and scenario constraint change information are transmitted to the data processing node at the ground monitoring center via the Internet of Things (IoT). The data processing node categorizes and organizes the received data, sorting it according to device identifiers (e.g., J01, Y01, Z01) and time sequence. Data association is established using timestamp matching; a corresponding timestamp is added to each piece of actual connection status data. Then, records with the same or similar timestamps are searched in the scenario constraint change information, ensuring that each point in time corresponds to a unique piece of scenario constraint change information, facilitating subsequent correlation analysis.

[0109] Step S144: Analyze the actual connection status data and the preset requirements of the cross-device action connection process. If the actual interval time exceeds the preset connection time and there are no scene factors in the scene constraint change information that would cause the interval to be extended, it is determined that there is room for optimization in the cross-device action connection process.

[0110] The actual interval time in the actual connection status data is compared with the preset connection time of the cross-device action connection process. For example, if the preset connection time between the tunneling machine and the transportation equipment is 30 seconds, and the actual interval time is 40 seconds, and the scene constraint change information shows that the geological conditions are stable, no new obstacles appear, and the environmental parameters are normal during this time period, that is, there are no scene factors that would cause the interval to be extended, then it is determined that there is room for optimization in the cross-device action connection process, and the process needs to be adjusted to shorten the connection interval time.

[0111] Step S145: To optimize the cross-device action connection process, adjust the information transmission method of the action confirmation node to reduce the information transmission delay to a preset delay threshold. At the same time, optimize the signal processing flow of the device control module to reduce the processing time after signal reception to a preset processing threshold. Adjust the cross-device action connection process.

[0112] The information transmission of the action confirmation node originally used wireless Wi-Fi, resulting in high latency. The transmission method was changed to 5G industrial Ethernet. By optimizing the network topology and increasing the number of access points, the latency was reduced to within a preset latency threshold (e.g., 50 milliseconds). For the signal processing flow of the device control module, the original process was serial (information verification → parameter configuration → startup preparation), which was time-consuming. This was optimized to parallel processing, allowing information verification and parameter configuration to occur simultaneously. During parameter configuration, some startup preparation work can be initiated, reducing the processing time after signal reception to within a preset processing threshold (e.g., 15 seconds). These adjustments optimized the cross-device action connection process and shortened the actual interval time.

[0113] Step S146: Analyze the impact of scene constraint change information on the collaborative resource allocation path. If the scene constraint change information shows that a new obstacle area has appeared on the original path, then replan the collaborative resource allocation path, avoid the new obstacle area, and select a new resource output port and transit node.

[0114] When the scenario constraint change information shows that new obstacle areas have appeared on the original collaborative resource allocation path (such as path blockage caused by the collapse of the alleyway sidewall), the path needs to be replanned.

[0115] Step S1461: Extract the location information, size, and type of the new obstacle region from the scene constraint change information, and determine the specific coordinate range of the new obstacle region in the scene spatial layout.

[0116] The location information of the new obstacle area was extracted as follows: 100 meters from the working face on the left side wall of the tunnel. The area is 5 meters long, 3 meters wide, and 2 meters high. The obstacle type is collapsed rock. Through on-site measurement and spatial modeling, the specific coordinate range of the new obstacle area in the scene spatial layout was determined to be (X1, Y1, Z1) to (X2, Y2, Z2).

[0117] Step S1462: Retrieve detailed data of the original collaborative resource allocation path, including the coordinates of the resource output port, the coordinates of the transit node, the coordinates of the resource demand point, and the length and direction of each segment on the path.

[0118] Retrieve the original collaborative resource allocation path data. The resource output port coordinates are (A1, B1, C1), the transfer node coordinates are (A2, B2, C2), and the resource demand point (work execution point) coordinates are (A3, B3, C3). The lengths of each segment on the path are L1 (from the output port to the transfer node) and L2 (from the transfer node to the demand point), and the directions are along the central axis of the roadway, turning at 30°, etc.

[0119] Step S1463: Compare the coordinate range of the new obstacle area with the coordinates of the original collaborative resource allocation path to determine the overlapping segment between the original path and the new obstacle area, and mark the start and end coordinates of the overlapping segment.

[0120] By comparing the coordinates of each segment of the original path with the coordinate range of the new obstacle area point by point, it was found that the segment of the original path from (Xa, Ya, Za) to (Xb, Yb, Zb) is located within the coordinate range of the new obstacle area. This segment was determined to be an overlapping segment with the starting coordinates of (Xa, Ya, Za) and the ending coordinates of (Xb, Yb, Zb).

[0121] Step S1464: Analyze the spatial layout characteristics of the scene around the new obstacle area, find alternative path areas that can bypass the obstacle, and the alternative path areas must meet the equipment's operating space adaptation range requirements and have no other potential obstacles.

[0122] The spatial layout of the scene surrounding the new obstacle area is analyzed. Point cloud data of the surrounding space is acquired through 3D laser scanning to identify usable passages and spaces. The alternative path area must meet the operating space requirements of the transport equipment, with a width no less than the width of the transport equipment plus the safety distance on both sides, and a height no less than the height of the transport equipment plus the safety distance at the top. At the same time, through historical data and real-time monitoring, it is ensured that there are no other potential obstacles (such as unsupported unstable rock masses, temporarily piled materials, etc.) in the alternative path area.

[0123] Step S1465: Reselect resource output ports within the alternative path area, prioritizing resource output ports that are closest to the resource demand point and are not affected by new obstacle areas. If the original resource output port is not affected, continue to use it; if it is affected, select new resource output ports from the collaborative resource supply range of other devices.

[0124] The original resource output ports (A1, B1, C1) were not affected by the new obstacle area and were closest to the resource demand point, so they were selected as the resource output ports.

[0125] Step S1466: Based on the location of the newly selected resource output port and resource demand point, set up new transfer nodes in the alternative path area. If the resource transmission distance is within the single device's resource transmission capacity, no transfer node needs to be set up; if it exceeds the capacity, set up transfer nodes in segments according to the resource transmission capacity so that each transmission distance is within the device's transmission capacity.

[0126] The straight-line distance between the newly selected resource output port and the resource demand point is Ltotal, and the single-device resource conveying capacity range of the transportation equipment is Lmax. If Ltotal ≤ Lmax, no transfer node needs to be set. If Ltotal > Lmax, then within the alternative path area, new transfer nodes are set according to the principle that the distance of each segment does not exceed Lmax. For example, the first transfer node (A4, B4, C4) is set at a distance of Lmax from the resource output port, and the remaining distance is Ltotal - Lmax. If the remaining distance is still greater than Lmax, then a second transfer node is set, and so on, until each conveying distance is within the equipment's conveying capacity range.

[0127] Step S1467: Plan a new collaborative resource allocation path, starting from the new resource output port, passing through the new transit node, and reaching the resource demand point. The collaborative resource allocation path avoids the coordinate range of the new obstacle area, and at the same time reduces the total length of the collaborative resource allocation path to within a preset length threshold.

[0128] Starting from the resource output ports (A1, B1, C1), a planned path is established, bypassing the coordinate range of the new obstacle area, passing through the newly set transit nodes (A4, B4, C4), and finally reaching the resource demand point (A3, B3, C3). During the planning process, path planning algorithms such as the A* algorithm are used, with the total path length as the optimization objective, so that the total length of the new collaborative resource allocation path is reduced to within a preset length threshold (such as 120% of the original path length).

[0129] Step S1468: Calculate the total delivery time of the new collaborative resource allocation path, compare the total delivery time with the total delivery time of the original path, and if the total delivery time of the new path does not exceed the preset delivery time limit, then determine the new path as a candidate path. The total delivery time is the sum of the delivery time of each segment of the path and the transit time of the transit node.

[0130] The new collaborative resource allocation path is divided into multiple segments. The delivery time for each segment is the path length divided by the average delivery speed of the transport equipment in that segment. The transfer time at each transfer node is the sum of the unloading and loading times of the transport equipment at that node. The total delivery time is the sum of the delivery times of each segment and the transfer times of each transfer node. The total delivery time of the new path is compared with the total delivery time of the original path. If the total delivery time of the new path does not exceed the preset delivery time limit (e.g., 150% of the total delivery time of the original path), then the new path is determined as a candidate path.

[0131] Step S1469: If the total delivery time of the new route exceeds the preset upper limit, adjust the number of transfer nodes or optimize the route to reduce the delivery time to within the preset upper limit until the total delivery time meets the requirements.

[0132] If the total transport time of the new route exceeds the preset upper limit, the transfer time can be reduced by decreasing the number of transfer nodes (such as merging two transfer nodes into one, provided that the single-segment transport distance after merging is within the equipment's transport capacity), or by optimizing the route (choosing a shorter and smoother route) to reduce the route transport time. This adjustment can be repeated until the total transport time meets the preset upper limit requirement.

[0133] Step S14610: Compile the detailed data of the new collaborative resource allocation path, including the new resource output port information, the new transit node information, the coordinates and length of each segment of the path, and the total transportation time, to form a new path plan.

[0134] Detailed data on the new collaborative resource allocation path is compiled. New resource output port information includes identifier, coordinates, storage resource type, and quantity; new transit node information includes identifier, coordinates, temporary storage capacity, and transit equipment model; coordinates and length of each segment of the path include the starting coordinates, ending coordinates, and actual length of each segment; total transportation time includes the transportation time of each segment, transit time, and total time. This data is then compiled into a new path plan document.

[0135] Step S14611: Input the new route plan into the resource allocation simulation program through the Internet of Things to simulate the process of transporting resources along the new route and verify whether the resources can reach the demand point smoothly without any interruption in transport.

[0136] The new route plan is transmitted to a resource allocation simulation program via the Internet of Things (IoT). A virtual tunnel excavation scenario and equipment model are constructed within the program, and parameters for the transport equipment (speed, load capacity, etc.) and resource parameters (density, volume, etc.) are set. The simulation tracks the entire transport process of resources from the output port, through transfer nodes, to the demand point, monitoring the real-time operating status of the transport equipment and changes in the location and quantity of resources. It verifies whether resources can successfully reach the demand point and whether any transport interruptions such as jams or collisions occur en route.

[0137] Step S14612: If the simulation results show that the resources can be transported smoothly, then the new route plan is determined as the adjusted collaborative resource allocation route; if problems occur in the simulation, then the alternative route areas and transit node settings are re-analyzed, and the planning steps are repeated until the simulation passes.

[0138] If the simulation results show that resources can reach the demand point smoothly without any interruption in transportation, then the new route plan is feasible and is determined as the adjusted collaborative resource allocation route. If a resource transportation interruption occurs during the simulation (such as a collision between transportation equipment and virtual obstacles), then return to step S1464, reanalyze the alternative route area, adjust the route direction or the location of the transfer node, and then repeat the subsequent planning steps until the simulation passes and a feasible new route plan is obtained.

[0139] Step S147: If the scenario constraint change information shows that the change in the environmental parameters of the work area has led to a decrease in resource delivery efficiency, then adjust the resource delivery rate parameter in the collaborative resource allocation path to the rate value corresponding to the preset efficiency, or increase the resource temporary storage capacity of the resource transfer node to the preset capacity value.

[0140] When the change information of the scenario constraint shows that the environmental parameters of the operation area change (such as the increase in dust concentration leads to a decrease in visibility, and the transportation equipment needs to slow down), resulting in a decline in the resource delivery efficiency. If the original resource delivery rate parameter is V1 and the rate value corresponding to the preset efficiency is V2 (V2 < V1 to adapt to the low visibility environment), then the resource delivery rate parameter in the collaborative resource allocation path is adjusted to V2. Or, if the decline in resource delivery efficiency leads to an extension of the resource in-transit time, and there may be a situation where resource supply is not timely, the resource temporary storage capacity of the resource transfer node can be increased to the preset capacity value (such as 1.5 times the original temporary storage capacity) to store more backup resources and ensure the continuous progress of the operation.

[0141] Step S148: Substitute the adjusted cross-device action connection process and collaborative resource allocation path into the device collaborative operation planning scheme, replace the original corresponding content, and form an adjusted device collaborative operation planning scheme.

[0142] Substitute the adjusted cross-device action connection process in Step S145 (such as the optimized information transmission method and signal processing process) and the adjusted collaborative resource allocation path in Steps S146 and S147 (such as the new path scheme, adjusted delivery rate parameter or temporary storage capacity) into the original device collaborative operation planning scheme, replace the cross-device action connection process and collaborative resource allocation path content in the scheme, and form an adjusted device collaborative operation planning scheme.

[0143] Step S149: Select some devices through the Internet of Things to perform local collaborative operations according to the adjusted device collaborative operation planning scheme, collect the actual connection state data and resource allocation data of the local collaborative operations, and verify the adjustment effect.

[0144] Select a roadheader, a transportation device and a support device to perform local collaborative operations according to the adjusted planning scheme (such as completing a short-distance tunneling - mucking - support cycle). During the operation, collect the actual connection state data (such as the connection interval time and action execution accuracy of each device) and resource allocation data (such as resource delivery time and delivery volume) of the local collaborative operations through the Internet of Things, and compare them with the data before adjustment and the preset requirements to verify whether the adjusted planning scheme effectively shortens the connection interval time and improves the resource allocation efficiency.

[0145] Step S1410: If the actual connection state data of the local collaborative operation meets the preset requirements and the resource allocation is smooth, then determine the adjusted planning scheme as the optimized device collaborative operation planning scheme; if it does not meet the preset requirements, re-analyze the actual connection state data and the change information of the scenario constraint, repeat the adjustment steps until the preset requirements are met.

[0146] If, in the actual connection status data of local collaborative operations, the connection intervals of each device's actions are all within the preset connection duration range, the action execution accuracy meets the operational target requirements within the scenario, and there are no interruptions or delays during resource allocation, and resources can arrive at the required points on time and in the required quantity, then the adjustment effect is good, and the adjusted planning scheme is determined to be the optimized device collaborative operation planning scheme. If the actual connection status data does not meet the preset requirements or resource allocation is not smooth, then return to step S144, re-analyze the actual connection status data and scenario constraint change information, find the cause of the problem, and repeat the subsequent adjustment steps until the planning scheme meets the preset requirements.

[0147] Step S150: Generate collaborative execution instructions for mining construction equipment based on the optimized equipment collaborative operation plan. Establish a collaborative instruction interaction channel between the control modules of each equipment through the Internet of Things. Distribute the collaborative execution instructions for mining construction equipment to the control modules of each mining equipment, drive each equipment to complete the operation actions according to the collaborative execution instructions, and send back action execution feedback.

[0148] In tunnel excavation operations, the optimized equipment collaborative operation plan provides detailed guidance for equipment collaborative execution. Generating and distributing collaborative execution instructions is the final step in realizing equipment collaborative operation. At the same time, it is necessary to establish an effective feedback mechanism to monitor the operation execution process.

[0149] Step S151: Analyze the optimized equipment collaborative operation planning scheme, extract the collaborative role positioning of each equipment, the action parameters in the cross-equipment action connection process, and the resource parameters in the collaborative resource allocation path. The action parameters include action type, action execution duration, and action accuracy requirements. The resource parameters include resource type, resource delivery volume, and resource delivery time.

[0150] The optimized equipment collaborative operation plan was analyzed, and the collaborative role of the tunneling machine was identified as the leading role in operation execution. Its action parameters include action type (excavation, loading), action execution duration (30 minutes per excavation cycle), and action accuracy requirement (excavation cross-section dimension deviation ±50mm). The collaborative role of the transportation equipment was identified as the workspace coordination role. Its action parameters include action type (transportation), action execution duration (20 minutes per transport), and action accuracy requirement (loading position deviation ±30mm). The collaborative role of the support equipment was identified as the auxiliary role in operation execution. Its action parameters include action type (anchor bolt installation, anchor cable installation), action execution duration (5 minutes per anchor bolt installation), and action accuracy requirement (anchor bolt angle deviation ±3°). The resource parameters in the collaborative resource allocation path include resource type (anchor bolts, anchor cables, concrete), resource delivery volume (50 anchor bolts / time, 20 anchor cables / time, 10 cubic meters of concrete / time), and resource delivery time (anchor bolt delivery 30 minutes / time, anchor cable delivery 40 minutes / time, concrete delivery 25 minutes / time).

[0151] Step S152: Classify the action parameters and resource parameters according to the device identifier, and generate structured instruction content for each device. The instruction content includes the device identifier, the instruction type (action instruction or resource allocation instruction), the instruction execution parameters (action parameters or resource parameters), and the instruction execution order.

[0152] Action parameters and resource parameters are categorized according to equipment identifiers (J01, Y01, Z01). Action instructions are generated for the tunneling machine (J01). The instruction content includes equipment identifier J01, instruction type: action instruction, instruction execution parameters (action type: excavation, action execution duration: 30 minutes, action accuracy requirement: ±50mm; action type: loading, action execution duration: 10 minutes, action accuracy requirement: ±30mm), and instruction execution sequence (excavation action is executed first, followed by loading action). Action instructions and resource allocation instructions are generated for the transportation equipment (Y01). The action instructions include the equipment identifier Y01, the instruction type is action instruction, the instruction execution parameters (action type: transportation, action execution duration: 20 minutes, action accuracy requirement: ±30mm), and the instruction execution sequence (executed after the tunneling machine's loading action is completed). The resource allocation instructions include the equipment identifier Y01, the instruction type is resource allocation instruction, the instruction execution parameters (resource type: anchor bolt, resource delivery quantity: 50 bolts, resource delivery time: 30 minutes), and the instruction execution sequence (executed after the support equipment issues a resource request signal). The instructions for the support equipment (Z01) are similar, generating corresponding structured instructions based on its role and parameters.

[0153] Step S153: Construct a unified format for collaborative execution instructions of mining construction equipment based on the instruction content. The format includes an instruction identifier segment, an instruction content segment, and an instruction verification segment. The instruction identifier segment marks the instruction generation time and instruction priority. The instruction content segment carries the structured instruction content. The instruction verification segment contains integrity verification information for the instruction content.

[0154] Mining construction equipment collaborative execution instructions adopt a unified binary format. The instruction identifier segment occupies 16 bytes. The first 8 bytes are the instruction generation time (using UTC timestamp format), and the last 8 bytes are the instruction priority (00H-FFH, with larger values ​​indicating higher priority, such as setting the priority of an emergency roadway support instruction to FFH). The instruction content segment dynamically allocates bytes based on the length of the instruction content. It uses TLV (Type-Length-Value) format to encode the structured instruction content. The Type field indicates the data type (such as equipment identifier, instruction type, etc.), the Length field indicates the length of the Value field, and the Value field is a specific numerical value or string. The instruction verification segment occupies 4 bytes. The CRC32 checksum algorithm is used to calculate the checksum of the instruction identifier segment and the instruction content segment. The resulting checksum is stored in this field and used by the receiver to verify the integrity of the instruction.

[0155] Step S154: According to the order of instruction execution of each device, sort the mining construction equipment collaborative execution instructions of each device in chronological order to form a mining construction equipment collaborative execution instruction set.

[0156] Based on the execution sequence of instructions from each piece of equipment and in conjunction with the overall schedule of tunnel excavation, the collaborative execution instructions for each piece of mining equipment are time-sequential. For example, at time t0, the excavation instruction for the tunneling machine is generated; at t0+30 minutes, the loading instruction for the tunneling machine is generated; at t0+40 minutes, the transportation instruction for the transport equipment is generated; and at t0+60 minutes, the anchor bolt installation instruction for the support equipment is generated. All equipment instructions are arranged in chronological order to form a collaborative execution instruction set for mining equipment, ensuring that the instructions of each piece of equipment are coordinated and consistent in time.

[0157] Step S155: Establish a collaborative command interaction channel through the edge gateway node of the Internet of Things. The edge gateway node establishes a two-way communication connection with the control modules of each device and allocates communication frequency bands using time-division multiplexing communication method.

[0158] Edge gateway nodes (model GW-500) were selected and deployed near the tunnel working face to establish a collaborative command interaction channel. The edge gateway nodes established wired bidirectional communication connections with the control modules of the tunneling machine, transport equipment, and support equipment via industrial Ethernet interfaces. A backup 5G wireless communication module was also provided to handle wired connection failures. Time-division multiplexing was used to allocate communication frequency bands, dividing each communication cycle into multiple time slots. Each device control module was allocated a fixed time slot for sending and receiving data; for example, time slot 1 (0-10ms) was allocated to the tunneling machine control module, time slot 2 (10-20ms) to the transport equipment control module, and time slot 3 (20-30ms) to the support equipment control module. This avoided communication conflicts between devices and improved communication efficiency.

[0159] Step S156: Set up an instruction cache queue in the edge gateway node, and store the instructions in the instruction set of the mining construction equipment collaborative execution instruction set into the cache queue according to the instruction priority and execution order.

[0160] An instruction cache queue is set up in the memory of the edge gateway node, using a priority queue data structure. Instructions from the mining construction equipment collaborative execution instruction set are stored in the cache queue in descending order of instruction priority, and within the same priority, in the order of execution from first to last. For example, the emergency support instruction with priority FFH is placed at the head of the queue, and the regular excavation instruction with priority 80H is placed after it, ensuring that high-priority instructions are processed and sent first.

[0161] Step S157: The edge gateway node distributes mining construction equipment collaborative execution instructions to each device control module in the order of the cache queue. After sending each mining construction equipment collaborative execution instruction, it waits for the device control module to receive the instruction confirmation information. After receiving the confirmation information, it sends the next mining construction equipment collaborative execution instruction.

[0162] The edge gateway node retrieves the head instruction from the instruction cache queue, identifies the target device control module based on the device identifier in the instruction, and sends the instruction to the control module via the collaborative instruction interaction channel. After transmission, a timer is started to wait for the device control module to return an instruction reception confirmation message. The confirmation message contains a checksum of the instruction identifier segment. The edge gateway node verifies the confirmation message, confirming that the instruction has been correctly received. Upon receiving a valid confirmation message, the next instruction is retrieved from the cache queue and sent, repeating the above process.

[0163] Step S158: If the edge gateway node does not receive confirmation information from any device control module within the preset waiting time, it switches the communication frequency band and resends the collaborative execution command of the mining construction equipment. At the same time, it records the number of resends. If the number of resends reaches the preset limit and no confirmation information is received, it starts the command receiving channel of the backup device control module and sends the command through the backup channel.

[0164] The default waiting time is set to 500 milliseconds. If the edge gateway node does not receive confirmation from the device control module within 500 milliseconds after sending a command, it determines that there may be interference or a fault in the communication. It then switches to a backup communication frequency band (e.g., switching from the original frequency band A to frequency band B) to resend the command, and records the number of retransmissions. The default maximum number of retransmissions is 3. If no confirmation is received after 3 retransmissions, the primary command receiving channel is determined to be faulty, and the backup device control module's command receiving channel is activated (e.g., switching from a wired channel to a 5G wireless channel), and the command is resent through the backup channel.

[0165] Step S159: After receiving the collaborative execution instruction from the mining construction equipment, each equipment control module parses the instruction content, drives the execution components of the equipment to complete the operation according to the instruction execution parameters, and collects real-time status data during the execution process. The real-time status data includes the execution progress, execution accuracy, and resource consumption.

[0166] For example, in step S1591: After receiving the collaborative execution instruction from the mining construction equipment, the equipment control module verifies the integrity verification information of the instruction verification segment. If the verification passes, it enters the instruction parsing stage; if the verification fails, it sends an instruction retransmission request to the edge gateway node.

[0167] Upon receiving the collaborative execution command, the device control module first initiates a verification program to process the command verification segment. This program, based on a preset verification algorithm, calculates all data in the command identifier segment and command content segment to generate a verification result. The generated verification result is then compared bit-by-bit with the integrity verification information carried in the command verification segment. If the two are completely identical, it is determined that no data loss or tampering occurred during transmission, the verification passes, and the control module immediately switches to command parsing mode. If the comparison results are inconsistent, the control module immediately constructs a command retransmission request. This request includes the original command's identifier information and the reason code for the verification failure, and is sent to the edge gateway node through the collaborative command interaction channel. Simultaneously, relevant information about the invalid command is temporarily stored locally for subsequent troubleshooting by technical personnel.

[0168] Step S1592: In the instruction parsing stage, the equipment control module extracts the instruction type and instruction execution parameters from the instruction content segment. If the instruction type is an action instruction, the action type, action execution duration, and action precision requirements are extracted. If the instruction type is a resource allocation instruction, the resource type, resource delivery quantity, and resource delivery time are extracted.

[0169] Upon entering the instruction parsing stage, the equipment control module performs structured parsing of the instruction content segment according to the preset instruction format specifications. First, it identifies the instruction type field, which distinguishes between action instructions and resource allocation instructions through a specific encoding value. When identified as an action instruction, the control module sequentially extracts the action type code, action execution duration parameter, and action accuracy requirement parameter from the instruction content segment. The action type code corresponds to the specific operational action that the equipment can perform, such as excavation, loading, and support; the action execution duration parameter specifies the allowed time range from start to completion of the action; and the action accuracy requirement parameter clarifies the allowable deviation range of various physical quantities during the action execution. When identified as a resource allocation instruction, it extracts the resource type code, resource delivery quantity parameter, and resource delivery time parameter. The resource type code corresponds to different support materials or other required resources; the resource delivery quantity parameter specifies the quantity of resources to be delivered; and the resource delivery time parameter specifies the time limit for completing the resource delivery task.

[0170] Step S1593: Call the corresponding execution control program according to the instruction type. If it is an action instruction, call the action execution program; if it is a resource allocation instruction, call the resource delivery program.

[0171] The equipment control module's program storage area pre-stores various execution control programs, each corresponding to a specific instruction type. After parsing the instruction type, the control module's scheduling unit retrieves the corresponding program from the program storage area based on the mapping relationship between the instruction type and the execution control program. If the instruction type is an action instruction, the scheduling unit activates the action execution program. This program contains sub-modules for different action types, such as an excavation action control sub-module and a loading action control sub-module. The corresponding sub-module can be further called based on the extracted action type code. If the instruction type is a resource allocation instruction, the resource transportation program is activated. This program also contains transportation control logic for different resource types and can plan the resource transportation path and method based on the resource type code and parameters such as resource transportation quantity and transportation time.

[0172] Step S1594: The action execution program drives the execution component of the device to start the action, controls the operation time according to the action execution duration, and collects the action execution accuracy data in real time through the accuracy detection sensor on the execution component during the action execution process. The data is compared with the action accuracy requirements in the instruction. If there is an accuracy deviation, the operating parameters of the execution component are adjusted so that the action accuracy meets the action accuracy requirements in the instruction.

[0173] After the action execution program is invoked, it first performs an initialization check on the device's execution components to ensure that each component is in normal working condition. Then, based on the action type and execution duration parameters, it sends a start signal and operating parameters to the drive unit of the execution component. The execution component begins its action according to the set operating parameters. Simultaneously, various precision detection sensors installed on the execution component (such as position sensors, angle sensors, pressure sensors, etc.) collect precision data in real time during the action execution process, such as the actual position of the execution component, its movement trajectory, and the applied force. This precision data is continuously transmitted to the action execution program, which compares and analyzes it in real time against the action precision requirements in the instruction. When the actual precision data is detected to exceed the allowable deviation range, the action execution program immediately calculates the deviation compensation value and adjusts the control signal of the drive unit to change parameters such as the running speed, force, or direction of the execution component to correct the precision deviation, ensuring that the precision throughout the entire action execution process always meets the instruction requirements.

[0174] Step S1595: The resource conveying program drives the resource conveying component of the equipment to start the conveying action, controls the total conveying amount according to the resource conveying quantity, controls the conveying speed according to the resource conveying time, and collects the actual conveying quantity in real time through the resource metering sensor during the conveying process, compares it with the resource conveying quantity in the instruction, and adjusts the conveying speed if there is a deviation, so that the total conveying quantity reaches the resource conveying quantity requirement in the instruction.

[0175] After the resource transport program is initiated, it first performs a self-check on the resource transport components (such as conveyor belts, pumps, and robotic arms) to confirm their normal operating status. Based on the resource type, transport volume, and transport time parameters, the theoretical transport speed is calculated. The resource transport program sends control signals to the drive system of the transport components, driving them to initiate the transport action at the theoretical speed. During transport, resource metering sensors (such as flow sensors, weight sensors, and counting sensors) installed along the transport path collect real-time data on the actual transport volume and feed it back to the resource transport program. The program periodically compares the actual transport volume with the theoretical cumulative transport volume calculated over time. When a deviation is detected between the actual and theoretical transport volumes, the resource transport program adjusts the drive parameters of the transport components, changing the transport speed. If the actual transport volume is lower than the theoretical value, the transport speed is increased; if the actual transport volume is higher than the theoretical value, the transport speed is decreased. Through this closed-loop control method, it ensures that the actual transport volume meets the required resource transport volume in the instruction within the specified transport time.

[0176] Step S1596: During the execution of an action or the transfer of resources, the equipment control module collects real-time status data at preset collection intervals. When an action is executed, the module collects the action execution progress, the action execution accuracy, and the consumption of resources required for the action execution.

[0177] The equipment control module has an internal status data acquisition timer that triggers the status data acquisition process at preset acquisition intervals (such as fixed time units per interval). During the execution of an action, the control module's status acquisition unit collects the following types of real-time status data at each acquisition interval: action execution progress, calculated by comparing the current duration of the action with the total execution time, usually expressed as a percentage; action execution accuracy, i.e., the deviation between the actual accuracy data of the executing component at the current moment and the required accuracy of the action; and the consumption of resources required for action execution, including the energy consumption of the equipment's power system, the consumption of hydraulic oil or lubricating oil, etc. These data are timestamped and temporarily stored in the control module's buffer.

[0178] Step S1597: During resource transportation, collect data on resource transportation progress, resource transportation accuracy, and resource loss during the transportation process.

[0179] During resource transportation, the status data acquisition timer also triggers data acquisition at preset intervals. The real-time status data acquired at this time includes: resource transportation progress, calculated by comparing the current cumulative transportation volume with the total resource transportation volume, expressed as a percentage; resource transportation accuracy, i.e., the deviation between the actual transportation position and the target transportation position, or stability indicators of the resource transportation flow rate; and resource loss during transportation, obtained by comparing the total resource volume before and after transportation or by setting loss detection sensors (such as leakage sensors and debris detection sensors) at key nodes in the transportation path, typically expressed as a percentage of the total transportation volume. This data is also timestamped and stored in the control module's buffer, using the same data format as the data acquired during action execution.

[0180] Step S1598: Mark the collected real-time status data with timestamps to form a time-stamped status data sequence.

[0181] The real-time status data stored in the buffer of the equipment control module all bear their own acquisition timestamps. The data analysis unit of the control module sorts and integrates these scattered status data according to the chronological order of the timestamps, forming a continuous, time-ordered sequence of status data. Each data sequence item contains various status data (such as progress, accuracy, resource consumption or wear and tear, etc.) at that point in time, along with the corresponding timestamp. This time-stamped status data sequence clearly reflects the changes in the equipment's operating status at different times.

[0182] Step S1599: The device control module performs format unification processing on the status data sequence. Within each preset feedback cycle, the device control module transmits the format-unified status data sequence back to the edge gateway node through the collaborative command interaction channel. If an abnormal state occurs within the feedback cycle, the abnormal state data is immediately transmitted back.

[0183] The device control module performs format standardization processing on the status data sequence, converting different types of status data according to preset data encoding rules to ensure data format consistency and compatibility. The control module has an internal feedback cycle timer. When the preset feedback cycle is reached, the control module packages the formatted status data sequence into a status feedback message. This message includes the device identifier, the timestamp range of the data sequence, and each status data item in the sequence. Subsequently, the control module sends the status feedback message to the edge gateway node through the collaborative command interaction channel. If, within a certain feedback cycle, the status data acquisition unit detects an abnormal state in the device (such as severely out-of-tolerance motion accuracy, interrupted resource delivery, or critical component failure), the control module will immediately trigger an emergency feedback mechanism. Instead of waiting for the feedback cycle to end, it will immediately package the abnormal status data into a separate emergency status message and send it to the edge gateway node first, so that the monitoring system can promptly grasp the abnormal device situation and take appropriate measures.

[0184] Step S1510: The device control module transmits real-time status data as action execution feedback back to the edge gateway node through the collaborative command interaction channel. The edge gateway node summarizes the action execution feedback of all devices and generates a collaborative execution status report in chronological order.

[0185] The device control module packages the collected real-time status data into action execution feedback information according to a preset feedback cycle (e.g., every 100 milliseconds) and transmits it back to the edge gateway node through the collaborative command interaction channel. The feedback information includes device identifier, timestamp, and real-time status data (action execution progress, action execution accuracy, resource consumption, etc.). The edge gateway node summarizes the received action execution feedback from each device, records the data in the database in timestamp order, and generates a collaborative execution status report in real time. The report includes the current action execution status of each device, whether there are any abnormalities (such as delayed action execution progress, accuracy errors, abnormal resource consumption, etc.), and the overall collaborative operation progress, providing monitoring personnel with a real-time visual display of the operation execution status.

[0186] Through the above steps, collaborative operation of mining equipment based on the Internet of Things is realized. By mapping the relationship between work tasks and equipment capabilities, efficient and safe collaborative operation of mining equipment in complex scenarios is ensured.

[0187] In one exemplary embodiment, an IoT-based collaborative operation system for mining construction equipment is provided. This IoT-based collaborative operation system can be a terminal, server, etc., and its internal structure diagram can be as follows: Figure 2As shown, this IoT-based collaborative operation system for mining construction equipment includes a processor, memory, input / output interfaces, a communication interface, a display unit, and input devices. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input devices are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, near-field communication, or other technologies. When the computer program is executed by the processor, it implements an IoT-based collaborative operation method for mining construction equipment. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the shell of the mining construction process equipment collaborative operation system based on the Internet of Things, or an external keyboard, touchpad, or mouse, etc.

[0188] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for collaborative operation of mining construction process equipment based on the Internet of Things, characterized in that, The method comprises: obtaining a set of operation task scene descriptions of mining construction and a list of collaborative capabilities of each mining device, the set of operation task scene descriptions comprising a scene type of mining operation, an operation target in the scene, and a special constraint of the scene, and the list of collaborative capabilities comprising a type of collaborative operation, a collaborative response mode, and a collaborative resource supply range of each mining device; establishing a scene capability association mapping of the set of operation task scene descriptions and the list of collaborative capabilities, adjusting a matching rule of the association mapping through real-time synchronization of device collaborative history records and scene execution feedback of the Internet of Things, and obtaining a dynamic association result of the scene and the device; generating a device collaborative operation planning scheme based on the dynamic association result of the scene and the device, and identifying a collaborative breakpoint and generating a breakpoint completion scheme through pre-playing of each device action sequence of the Internet of Things, wherein the device collaborative operation planning scheme comprises a collaborative role positioning of each mining device, a cross-device action connection process, and a collaborative resource allocation path; in the process of executing the device collaborative operation planning scheme by each mining device, collecting an actual connection state of collaborative actions between devices and scene constraint change information through the Internet of Things, adjusting the cross-device action connection process and the collaborative resource allocation path according to the actual connection state and the scene constraint change information, and obtaining an optimized device collaborative operation planning scheme; generating a mining construction device collaborative execution instruction according to the optimized device collaborative operation planning scheme, building a collaborative instruction interaction channel of each device control module through the Internet of Things, distributing the mining construction device collaborative execution instruction to the control module of each mining device, and driving each device to complete operation actions according to the collaborative execution instruction and return action execution feedback. 2.The Internet-of-Things-based collaborative operation method of mining construction process equipment according to claim 1, characterized in that, The method comprises: extracting scene key elements of each operation scene from the set of operation task scene descriptions, wherein the scene key elements comprise operation object characteristics in the scene, scene space layout features, and operation sequence constraints in the scene; extracting device collaborative key items of each mining device from the list of collaborative capabilities, wherein the device collaborative key items comprise a type of operation object that can be handled by the device, a device operation space adaptation range, and an operation sequence that can be cooperated by the device; preliminarily establishing a correspondence between the scene key elements and the device collaborative key items, corresponding the operation object characteristics in the scene to the type of operation object that can be handled by the device, corresponding the scene space layout features to the device operation space adaptation range, and corresponding the operation sequence constraints in the scene to the operation sequence that can be cooperated by the device; obtaining a device scene combination that meets a preset standard in collaborative action completion and a standard in operation target achievement from the device collaborative history records, and extracting a corresponding rule between the scene key elements and the device collaborative key items in the obtained device scene combination; obtaining a device scene combination that meets a preset standard in collaborative action completion and a standard in operation target achievement from the device collaborative history records, and extracting a corresponding rule between the scene key elements and the device collaborative key items in the obtained device scene combination; Collect scene execution feedback through the Internet of Things, which includes connection problems of device coordination actions in past scene execution and coordination adjustment requirements caused by changes in scene constraints; Adjust the corresponding priority of scene key elements and device coordination key items according to the connection problems recorded in the scene execution feedback. If the corresponding adaptation range of device work space and scene space layout characteristics in any type of scene often causes connection problems, then the corresponding priority of other device coordination key items in this type of scene is increased. According to the coordination adjustment requirements recorded in the scene execution feedback, supplement the matching rules of the scene capability association mapping. If changes in any type of scene constraint often cause insufficient supply of device coordination resources, then add resource supply range adaptation requirements under scene constraint changes to the matching rules. Based on the adjusted corresponding priority and supplemented matching rules, reconstruct the corresponding relationship between scene key elements and device coordination key items to form a preliminary version of the scene capability association mapping. Verify the preliminary version by selecting past device scene combinations that have not participated in coordination through the Internet of Things. If the device coordination actions are smooth and meet the scene constraints during verification, then determine the preliminary version as the scene device dynamic association result. If there are connection problems or do not meet the constraints, then repeat the matching rule adjustment step until verification is passed. Store the scene device dynamic association result by scene type, and associate the corresponding matching rule adjustment record and verification result. 3.The Internet-of-Things based collaborative operation method of mining construction process equipment according to claim 1, characterized in that, Generate a device coordination work planning scheme based on the scene device dynamic association result, and pre-act each device action sequence through the Internet of Things to identify coordination breakpoints and generate breakpoint completion schemes, including: Extract the adapted device list and scene adaptation basis of each device from the scene device dynamic association result. The scene adaptation basis includes the matching points of device coordination key items and scene key elements. According to the importance of the matching points of each device in the scene adaptation basis, assign each device a coordination role positioning. If the matching points of a device are concentrated in the correspondence between the work object characteristics in the scene and the types of work objects that the device can handle, then the device is positioned as a work execution dominant role. If the matching points are concentrated in the correspondence between the scene space layout characteristics and the device work space adaptation range, then the device is positioned as a work space coordination role. Collect the coordination role positioning information of all devices, analyze the action dependency relationship between different roles, and trigger the space adjustment action of the work space coordination role after the action of the work execution dominant role is completed. Then, use the action completion signal of the work execution dominant role as the starting condition for the action of the work space coordination role to construct a preliminary link of the cross-device action connection process. Optimize the preliminary link of the cross-device action connection process by combining the work sequence constraints in the scene and the coordination response modes of each device. Set an action confirmation node at the connection between adjacent device actions. The previous device needs to send action completion confirmation information to the subsequent device after completing the action, and the subsequent device starts its own action after receiving the confirmation information, forming a cross-device action connection process. According to the cooperative resource supply range and the in-scene operation target of each device, a cooperative resource allocation path is planned. If the cooperative resource supply range of the device covers multiple operation areas, the resource output port closest to the operation execution point is selected as the allocation starting point, the operation execution point is selected as the allocation ending point, obstacles in the scene space layout features are avoided, and a basic resource allocation path is determined; Resource transfer nodes are set on the basic path. If the resource allocation distance exceeds the single-device resource delivery capacity, multi-device resource relay delivery is realized through the transfer nodes, the resource temporary storage capacity of the transfer nodes and the transfer time requirements are supplemented, and the cooperative resource allocation path is perfected; The cooperative role positioning, cross-device action connection process, and cooperative resource allocation path of each device are converted into a digital action sequence through the Internet of Things. The devices simulate the execution of operations according to the digital action sequence, and the action execution time, action connection interval, and resource allocation time consumption are recorded in real time; The recorded data are analyzed to identify cooperative breakpoints. If the action connection interval of any two devices exceeds the preset connection time, it is determined to be an action connection breakpoint. If the resource allocation time consumption exceeds the preset allocation time and the resource demand point has not been reached, it is determined to be a resource allocation breakpoint. For the action connection breakpoint, the corresponding action confirmation node in the cross-device action connection process is queried. The waiting time for sending confirmation information after the completion of the previous device action is extended to the preset sending waiting threshold, or the action start preparation time after receiving the confirmation information by the subsequent device is reduced to the preset preparation threshold, and a breakpoint completion scheme is generated. For the resource allocation breakpoint, the number of transfer nodes in the cooperative resource allocation path is optimized, or the resource delivery rate is adjusted to the preset rate range, so that the resource allocation time consumption meets the preset requirements, and a breakpoint completion scheme is generated. The cooperative role positioning, cross-device action connection process, and cooperative resource allocation path are integrated with the breakpoint completion scheme to form a device cooperative operation planning scheme. 4.The method of claim 1, wherein, During the execution of the device cooperative operation planning scheme by each mining device, the actual connection state of the cooperative action between the devices and the scene constraint change information are collected through the Internet of Things. The cross-device action connection process and the cooperative resource allocation path are adjusted according to the actual connection state and the scene constraint change information, and an optimized device cooperative operation planning scheme is obtained, including: The actual start time, actual completion time, and action execution accuracy of the device action are collected, as well as the time of receiving the action start signal and the time of sending the action completion signal. The actual interval time of action connection between adjacent devices is calculated, and whether the action execution accuracy meets the in-scene operation target requirements is recorded to form the actual connection state data of the cooperative action between the devices. The actual interval time is the difference between the time of receiving the action start signal by the subsequent device and the time of sending the action completion signal by the previous device. The collected scene geological condition change data, spatial obstacle position change data, and operation area environment parameter change data constitute the scene constraint change information. The actual connection state data and the scene constraint change information are transmitted to the data processing node in real time for classification and arrangement, the data correlation is established according to the equipment identifier and the time sequence, and the actual connection state data at each time point corresponds to unique scene constraint change information; The actual connection state data and the preset requirements of the cross-equipment action connection process are analyzed, if the actual interval time exceeds the preset connection time length, and there is no scene factor causing the interval extension in the scene constraint change information, it is determined that there is an optimization space for the cross-equipment action connection process; For the optimization space of the cross-equipment action connection process, the information transmission mode of the action confirmation node is adjusted, so that the information transmission delay is reduced to the preset delay threshold, and the signal processing process of the equipment control module is optimized, so that the processing time after signal reception is reduced to the preset processing threshold, and the cross-equipment action connection process is adjusted; The influence of the scene constraint change information on the cooperative resource allocation path is analyzed, if the scene constraint change information shows that a new obstacle region appears on the original path, the cooperative resource allocation path is re-planned to avoid the new obstacle region, and a new resource output port and a transfer node are selected; If the scene constraint change information shows that the change of the working area environment parameter causes the resource transportation efficiency to decrease, the resource transportation rate parameter in the cooperative resource allocation path is adjusted to the rate value corresponding to the preset efficiency, or the resource temporary storage capacity of the resource transfer node is increased to the preset capacity value; The adjusted cross-equipment action connection process and the cooperative resource allocation path are substituted into the equipment cooperative work planning scheme to replace the original corresponding content, and an adjusted equipment cooperative work planning scheme is formed; According to the adjusted equipment cooperative work planning scheme, part of the equipment is selected through the Internet of Things to perform local cooperative work, actual connection state data and resource allocation data of the local cooperative work are collected, and the adjustment effect is verified; If the actual connection state data of the local cooperative work meets the preset requirements and the resource allocation is smooth, the adjusted planning scheme is determined as the optimized equipment cooperative work planning scheme; if it does not meet the preset requirements, the actual connection state data and the scene constraint change information are re-analyzed, and the adjustment steps are repeated until the preset requirements are met. 5.The Internet-of-Things based collaborative operation method of mining construction process equipment according to claim 1, characterized in that, The mining construction equipment cooperative execution instruction is generated according to the optimized equipment cooperative work planning scheme, the cooperative instruction interaction channel of each equipment control module is built through the Internet of Things, the mining construction equipment cooperative execution instruction is distributed to the control module of each mining equipment, each equipment is driven to complete the work action according to the cooperative execution instruction, and the action execution feedback is returned, including: The optimized equipment cooperative work planning scheme is analyzed, the cooperative role positioning of each equipment, the action parameters in the cross-equipment action connection process, and the resource parameters in the cooperative resource allocation path are extracted, the action parameters include action type, action execution time length, and action accuracy requirement, and the resource parameters include resource type, resource transportation amount, and resource transportation time; The action parameters and the resource parameters are classified according to the equipment identifier, and the structured instruction content is generated for each equipment, the instruction content includes equipment identifier, instruction type, instruction execution parameter, and instruction execution sequence. The unified format of the mining construction equipment cooperative execution instruction is constructed based on instruction content, and the format includes an instruction identification section, an instruction content section, and an instruction verification section. The instruction identification section marks instruction generation time and instruction priority. The instruction content section carries structured instruction content. The instruction verification section includes integrity verification information of the instruction content. The mining construction equipment cooperative execution instructions of each device are sorted in time sequence according to the instruction execution sequence of each device to form a set of mining construction equipment cooperative execution instructions. A cooperative instruction interaction channel is built through an edge gateway node of the Internet of Things. The edge gateway node and each device control module establish a bidirectional communication connection. A time division multiplexing communication mode is used to allocate a communication frequency band. An instruction cache queue is set in the edge gateway node. Instructions in the set of mining construction equipment cooperative execution instructions are stored in the cache queue according to instruction priority and execution sequence. The edge gateway node distributes the mining construction equipment cooperative execution instructions to each device control module in the cache queue sequence. After sending each mining construction equipment cooperative execution instruction, the edge gateway node waits for instruction receiving confirmation information from the device control module. After receiving the confirmation information, the next mining construction equipment cooperative execution instruction is sent. If the edge gateway node does not receive confirmation information from any device control module within a preset waiting time, the communication frequency band is switched to resend the mining construction equipment cooperative execution instruction. The number of resending times is recorded. If the number of resending times reaches a preset upper limit and the confirmation information is still not received, an instruction receiving channel of a backup device control module is started. The instruction is sent through the backup channel. After each device control module receives the mining construction equipment cooperative execution instruction, the instruction content is analyzed, and the execution component of the device is driven to complete the work action according to the instruction execution parameter. At the same time, real-time state data in the action execution process is collected, including action execution progress, action execution accuracy, and resource consumption. The device control module transmits the real-time state data as action execution feedback to the edge gateway node through the cooperative instruction interaction channel. The edge gateway node collects the action execution feedback of all devices and generates a cooperative execution status report in time sequence. 6.The method of claim 2, wherein, The device scene combinations whose cooperative action completion meets the preset standard and whose work target achievement meets the standard are filtered from the device cooperative history record. The corresponding rules of scene key elements and device cooperative key items in the filtered device scene combinations are extracted, including: Cooperative action completion data and work target achievement data of all device scene combinations are extracted from the device cooperative history record. The cooperative action completion data includes action completion rate, action connection qualified rate, and action execution error rate. The work target achievement data includes target completion rate and target completion quality score. The preset standard of cooperative action completion is set. The action completion rate is not less than the preset completion proportion, the action connection qualified rate is not less than the preset connection proportion, and the action execution error rate is not higher than the preset error proportion. It is determined that the cooperative action completion meets the preset standard. The standard requirement of work target achievement is set. The target completion rate is not less than the preset target proportion, and the target completion quality score is not less than the preset quality score. It is determined that the work target achievement meets the standard. The cooperative action completion data is compared with the preset standard, the operation target achievement data is compared with the standard requirement, and the device scene combination that meets the preset standard and the operation target achievement requirement is screened out, and an effective device scene combination list is formed. The device scene combinations under the same scene type are selected from the effective device scene combination list, and are classified into the same scene combination group. For each scene combination group, the scene key elements and corresponding device cooperation key items of each device scene combination in the group are extracted, and the corresponding relationship and corresponding frequency of each scene key element and device cooperation key item are recorded. The corresponding frequency of the same scene key element and different device cooperation key items is counted, and if the corresponding frequency of any device cooperation key item and scene key element accounts for more than a preset proportion of the total corresponding frequency of the scene key element, the device cooperation key item is determined as the main matching item of the scene key element. The change rule of the main matching item of the scene key element and the device cooperation key item under different scene types is analyzed, and the change content and change reason of the main matching item when the scene type changes are recorded. The main matching items and change rules of the scene key element and the device cooperation key item under all scene types are summarized to form a corresponding rule document of the scene key element and the device cooperation key item, and the corresponding rule document is stored in association with the effective device scene combination list. 7.The method of claim 3, wherein the method further comprises: The action connection breakpoint is queried, the corresponding action confirmation node in the cross-device action connection process is queried, the waiting time for sending confirmation information after the completion of the previous device action is extended to a preset sending waiting threshold, or the action start preparation time after the subsequent device receives the confirmation information is reduced to a preset preparation threshold, and a breakpoint completion scheme is generated, including: The action connection breakpoint is filtered from the identified cooperative breakpoint, and the previous device identifier, subsequent device identifier and corresponding action confirmation node position involved in each action connection breakpoint are recorded. The preset information transmission process of the action confirmation node in the cross-device action connection process is queried, and the trigger condition, information transmission path of the previous device sending confirmation information, and processing steps after the subsequent device receiving the confirmation information are obtained. The detailed data of the action connection breakpoint in the rehearsal process is retrieved through the Internet of Things, including the actual completion time of the previous device action, the time of the previous device sending confirmation information, the time of the confirmation information transmission to the subsequent device, the preparation time of the subsequent device after receiving the confirmation information, and the actual start time of the action of the subsequent device. The time consumption of each link is calculated, and the time consumption from the completion of the previous device action to the sending of the confirmation information is the sending waiting time, and the time consumption from the receiving of the confirmation information by the subsequent device to the start of the action is the start preparation time. If the proportion of the sending waiting time in the total interfacing interval time exceeds the preset sending proportion, it is determined that the sending waiting time after the pre-device action is completed needs to be extended, the reason why the sending waiting time is less than the preset sending waiting threshold is analyzed, if the sending waiting time is insufficient due to the pre-device action completion detection delay, the sensitivity of the action completion detection is adjusted, the detection time is extended to ensure that the action is completely completed before the confirmation information is sent, and the minimum threshold of the sending waiting time is set to ensure that the action is stably completed before the confirmation information is sent; If the proportion of the starting preparation time in the total interfacing interval time exceeds the preset preparation proportion, it is determined that the action starting preparation time after the subsequent device receives the confirmation information needs to be reduced, the reason why the starting preparation time exceeds the preset preparation threshold is analyzed, if the parameter self-checking process before the subsequent device starts consumes time exceeding the preset self-checking time length, the parameter self-checking process is optimized, the self-checking items of non-key parameters are reduced, or part of the self-checking items and the confirmation information receiving process are executed in parallel, so that the starting preparation time is reduced to be within the preset preparation threshold; Verification standards for the adjustment scheme are generated, if the sending waiting time is extended, it is verified whether the pre-device action is stably completed within the adjusted sending waiting time; if the starting preparation time is reduced, it is verified whether the subsequent device can normally start the action within the adjusted starting preparation time and whether the action accuracy meets the requirements; The adjustment scheme and the verification standards are integrated to form a breakpoint completion scheme for the action interfacing breakpoint; The above steps are repeated to generate a corresponding breakpoint completion scheme for each action interfacing breakpoint, all action interfacing breakpoint completion schemes are collected to form an action interfacing breakpoint completion scheme set; The action interfacing breakpoint completion scheme set is associated with the cross-device action interfacing process, and the action confirmation node and the device identifier corresponding to each completion scheme are marked. 8.The method of claim 4, wherein, If the scene constraint change information shows that a new obstacle region appears on the original path, the collaborative resource allocation path is replanned to avoid the new obstacle region, and a new resource output port and a transfer node are selected, including: Extracting the position information, region range size, and obstacle type of the new obstacle region from the scene constraint change information to determine the specific coordinate range of the new obstacle region in the scene space layout; Retrieving detailed data of the original collaborative resource allocation path, including resource output port coordinates, transfer node coordinates, resource demand point coordinates, lengths and directions of each segment on the path; Comparing the coordinate range of the new obstacle region with the coordinates of the original collaborative resource allocation path to determine the overlapping segment of the original path and the new obstacle region, and marking the start and end coordinates of the overlapping segment; Analyzing the scene space layout features around the new obstacle region to find a candidate path region that can bypass the obstacle, which needs to meet the device operation space adaptation range requirement and have no other potential obstacles; In the candidate path region, a resource output port is selected again, and a resource output port closest to the resource demand point and not affected by the new obstacle region is preferentially selected, if the original resource output port is not affected, the original resource output port is continued to be used, and if the original resource output port is affected, a new resource output port is selected from the collaborative resource supply range of other devices; According to the location of the newly selected resource output port and the resource demand point, a new transfer node is set in the alternative path area. If the resource transportation distance is within the single-device resource transportation capacity range, no transfer node needs to be set. If it exceeds the capacity range, the transfer node is set according to the resource transportation capacity, so that the transportation distance of each segment is within the device transportation capacity. A new collaborative resource allocation path is planned, starting from the new resource output port, passing through the new transfer node, and reaching the resource demand point. The new collaborative resource allocation path avoids the coordinate range of the new obstacle area, and the total length of the new collaborative resource allocation path is reduced to within the preset length threshold. The total transportation time of the new collaborative resource allocation path is calculated. If the total transportation time of the new path does not exceed the preset upper limit of the transportation time, the new path is determined as a candidate path, and the total transportation time is the sum of the transportation time of each segment and the transfer time of the transfer node. If the total transportation time of the new path exceeds the preset upper limit, the number of transfer nodes or the path direction is adjusted to reduce the transportation time to within the preset upper limit of the transportation time, until the total transportation time meets the requirements. The detailed data of the new collaborative resource allocation path, including the new resource output port information, the new transfer node information, the coordinates and lengths of each segment of the path, and the total transportation time, are sorted to form a new path scheme. The new path scheme is input into the resource allocation simulation program through the Internet of Things to simulate the process of resource transportation according to the new path, and to verify whether the resource can reach the demand point smoothly and without interruption. If the simulation result shows that the resource can be transported smoothly, the new path scheme is determined as the adjusted collaborative resource allocation path. If there is a problem in the simulation, the alternative path area and the transfer node setting are reanalyzed, and the planning steps are repeated until the simulation is passed.

9. An Internet of Things based collaborative operation system for mining construction process equipment, characterized in that, It comprises: a processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the machine-executable instructions to perform the method of claim 1 to 8.

10. A computer program product, characterised in that, The computer program product comprises machine-executable instructions stored in a computer-readable storage medium, and the processor of the Internet of Things-based mining construction process equipment collaborative operation system reads the machine-executable instructions from the computer-readable storage medium. The processor executes the machine-executable instructions, so that the Internet of Things-based mining construction process equipment collaborative operation system executes the method of claim 1 to 8.

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