Method and system for scheduling of equipment systems based on unmanned coal mining

Abstract

This invention provides a method and system for scheduling equipment systems based on unmanned coal mining, relating to the field of equipment scheduling technology. By integrating safety level classification, multi-source data-driven conflict identification, and dynamic priority scheduling mechanisms, this invention achieves an organic unity of safety, intelligence, and efficiency. On one hand, it establishes an evaluation matrix based on historical risk cases, classifies equipment into safety levels according to the severity of failure consequences, and presets conflict coefficients. When a conflict occurs, it dynamically increases the conflict coefficient of high-risk equipment based on real-time scenarios, significantly improving the inherent safety level of the system. On the other hand, it relies on an industrial cloud platform to clean and analyze the location, status, and environmental data collected by sensors, accurately identifying logical conflicts where multiple devices compete for exclusive resources. It then uses a rolling, updated priority sequence to guide path planning, task timing, and energy allocation, achieving distributed collaborative control of "cloud decision-making—side execution."

Description

Technical Field

[0001] This invention relates to the field of equipment scheduling technology, and in particular to a method and system for scheduling equipment systems based on unmanned coal mining. Background Technology

[0002] In the process of transitioning from traditional coal mining operations to unmanned coal mining, many technical challenges urgently need to be addressed. These problems severely restrict the safety, intelligence, and efficiency of unmanned coal mining systems, specifically in the following aspects: In traditional coal mining operations, the lack of a scientific mechanism for classifying equipment safety levels makes it difficult to systematically assess the operational risks and the severity of consequences of equipment malfunctions. In actual operational scenarios, the consequences of malfunctions in different equipment vary greatly. For example, malfunctions in some critical equipment can lead to large-scale collapses, gas leaks, and other serious accidents, while malfunctions in some auxiliary equipment may only affect the progress of localized operations. However, traditional methods fail to differentiate equipment management based on these differences. In the face of emergencies, they cannot prioritize the safe operation of high-risk equipment, leaving the entire coal mining system exposed to higher safety risks.

[0003] In unmanned coal mining environments, competition for exclusive resources frequently occurs when multiple pieces of equipment work collaboratively. Traditional coal mining systems lack effective multi-source data acquisition and analysis methods, making it difficult to obtain real-time multi-dimensional data such as equipment location, operating status, and environmental parameters. This results in the inability to promptly and accurately identify logical conflict scenarios when multiple pieces of equipment simultaneously request or occupy the same exclusive resource. For example, in narrow tunnels, multiple pieces of coal mining equipment may simultaneously attempt to enter the same area because they cannot accurately perceive each other's positions and task requirements, leading to equipment collisions, deadlocks, and other problems. This not only damages the equipment but also causes operational interruptions, severely impacting production efficiency.

[0004] Therefore, it is necessary to provide a system scheduling method for unmanned coal mining equipment and a system solution to the above-mentioned technical problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method and system for scheduling equipment systems based on unmanned coal mining, which solves the problems of insufficient security and inadequate conflict identification and handling capabilities in the prior art.

[0006] The equipment system scheduling method based on unmanned coal mining provided by this invention includes the following steps: S1. Classify safety levels based on the operational risks and severity of failure consequences of unmanned coal mining equipment, and preset conflict coefficients for each safety level; S2. Collect multi-source data in real time through sensors installed on the equipment, including location information, operating status and environmental parameters, and obtain multi-source datasets by uploading the data to the industrial cloud platform for cleaning. S3. Identify device conflict scenarios based on multi-source datasets and dynamically adjust the conflict coefficient in conjunction with preset security levels; S4. Based on the adjusted conflict coefficient, prioritize the items according to the sequence from high to low, and recalculate the conflict coefficient and update the priority according to the preset period. S5. Based on the industrial cloud platform, scheduling instructions are generated according to the updated priorities to perform distributed scheduling of equipment.

[0007] Preferably, the specific steps of S1 are as follows: S101. Based on historical safety cases, establish a risk consequence assessment matrix according to the operational risks that the equipment may cause during operation and the severity of its failure consequences; S102. Based on the risk consequence assessment matrix, divide the equipment into several safety levels and preset an initial conflict coefficient for each safety level.

[0008] Preferably, the specific steps of S2 are as follows: S201. Each device continuously collects raw multi-source data through sensors at a preset sampling frequency and uploads it to the industrial cloud platform in real time through the underground industrial communication network. S202. Perform data cleaning operations on the received raw multi-source data through the industrial cloud platform, including removing outliers and aligning timestamps, to form a structured multi-source dataset.

[0009] Preferably, the specific steps of S3 are as follows: S301. Extract the task type currently being executed by each device and the key shared resource information bound to it from the multi-source dataset; S302. Detect whether there is a situation where multiple devices simultaneously request or occupy the same exclusive resource, and obtain the detection result. If the detection result indicates that such a situation exists, it is determined to be a logical conflict scenario. S303. Based on the identified logical conflict scenarios, dynamically adjust the conflict coefficient of each device and its corresponding security level to obtain the adjusted conflict coefficient.

[0010] Preferably, the specific steps of S4 are as follows: S401. Summarize the conflict coefficients of all devices after adjustment in the current scheduling cycle, sort the devices from high to low according to the conflict coefficient values, and generate a priority sequence. S402. Mark the priority sequence with a timestamp and archive it. Repeat the priority sequence generation step according to the preset scheduling cycle to obtain the updated priority.

[0011] Preferably, the specific steps of S5 are as follows: S501. Read the updated priority through the industrial cloud platform, and plan the operation path, task sequence or resource allocation scheme for each device according to the priority level, and obtain the planning results, including the priority order of operation path passage, the order of task sequence operation or the priority order of energy supply allocation according to the priority from high to low. S502. Transform the planning results into specific scheduling instructions and send them to the local controller of the corresponding equipment through the industrial communication network; S503: After receiving the scheduling instructions, each device executes the control logic locally to perform distributed scheduling.

[0012] A scheduling system for equipment systems based on unmanned coal mining, the scheduling system comprising: The safety rating module is used to classify safety levels based on the operational risks and severity of failure consequences of unmanned coal mining equipment, and to preset conflict coefficients for each safety level; The data acquisition module is used to collect multi-source data in real time through sensors installed on the equipment, including location information, operating status and environmental parameters, and to obtain a multi-source dataset by uploading the data to the industrial cloud platform for cleaning. The conflict identification module is used to identify device conflict scenarios based on multi-source datasets and dynamically adjust the conflict coefficient in combination with preset security levels. The priority scheduling module is used to divide priorities in descending order according to the adjusted conflict coefficient, and to recalculate the conflict coefficient and update the priorities according to a preset period. The instruction issuance module is used to generate scheduling instructions based on the updated priority of the industrial cloud platform and to perform distributed scheduling of equipment.

[0013] Compared with related technologies, the equipment system scheduling method and system based on unmanned coal mining provided by this invention have the following beneficial effects: This invention achieves a unified approach to security, intelligence, and efficiency by integrating security level classification, multi-source data-driven conflict identification, and dynamic priority scheduling mechanisms. On one hand, an evaluation matrix is ​​established based on historical risk cases, classifying equipment into security levels according to the severity of failure consequences and pre-setting conflict coefficients. When a conflict occurs, the conflict coefficient of high-risk equipment is dynamically increased based on the real-time scenario, significantly improving the inherent safety level of the system. On the other hand, relying on an industrial cloud platform, the location, status, and environmental data collected by sensors are cleaned and analyzed to accurately identify logical conflicts arising from multiple devices vying for exclusive resources. A rolling priority sequence guides path planning, task timing, and energy allocation, generating optimized scheduling instructions that are sent to the local controller, achieving distributed collaborative control through "cloud decision-making and edge execution." This method and system effectively reduce equipment collisions, deadlocks, and invalid waiting, significantly reducing manual intervention and downtime. While ensuring controllability of high-risk operations, it significantly improves the operating efficiency, resource utilization, and automated collaborative capabilities of unmanned coal mining systems. Attached Figure Description

[0014] Figure 1 This is a flowchart of the equipment system scheduling method based on unmanned coal mining according to the present invention; Figure 2 This is a system module diagram of the equipment system scheduling system based on unmanned coal mining according to the present invention. Detailed Implementation

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] Example 1 like Figure 1 As shown, the equipment system scheduling method based on unmanned coal mining includes the following steps: S1. Classify safety levels based on the operational risks and severity of failure consequences of unmanned coal mining equipment, and preset conflict coefficients for each safety level; S2. Collect multi-source data in real time through sensors installed on the equipment, including location information, operating status and environmental parameters, and obtain multi-source datasets by uploading the data to the industrial cloud platform for cleaning. S3. Identify device conflict scenarios based on multi-source datasets and dynamically adjust the conflict coefficient in conjunction with preset security levels; S4. Based on the adjusted conflict coefficient, prioritize the items according to the sequence from high to low, and recalculate the conflict coefficient and update the priority according to the preset period. S5. Based on the industrial cloud platform, scheduling instructions are generated according to the updated priorities to perform distributed scheduling of equipment.

[0017] In the specific implementation process, the specific steps of step S1 are as follows: S101. Based on historical safety cases, establish a risk consequence assessment matrix according to the operational risks that the equipment may cause during operation and the severity of its failure consequences.

[0018] Specifically, a large number of historical safety cases involving unmanned coal mining equipment were collected. These cases covered problems encountered by the equipment in various operating scenarios, including but not limited to equipment failures and accidents caused by operational errors. For example, in an unmanned coal mining operation at a certain coal mine, a coal mining machine suffered losses due to an electrical system failure, resulting in a several-day shutdown of the entire coal face for repairs and safety inspections. A detailed analysis of the collected historical cases was conducted, identifying the types of operational risks caused by equipment operation in each case, such as mechanical injury risks and electrical safety risks. The severity was assessed, categorizing risks from minor impacts (such as short-term equipment shutdowns for maintenance that do not affect overall production progress) to serious consequences (such as major equipment damage or prolonged shutdowns). A two-dimensional risk consequence assessment matrix was constructed, where the horizontal axis represents the type of operational risk, and the vertical axis represents the severity level of the failure consequence. Each cell in the matrix records relevant equipment information for the corresponding risk type and consequence severity level, such as the frequency of equipment failures and the potential scope of losses, providing a quantitative basis for subsequent safety level classification.

[0019] S102. Based on the risk consequence assessment matrix, divide the equipment into several safety levels and preset an initial conflict coefficient for each safety level.

[0020] Specifically, based on the risk consequence assessment matrix, and considering the type of operational risk and the severity of failure consequences, a standard for classifying equipment safety levels is established. In this embodiment, equipment is divided into three safety levels: high, medium, and low. Equipment that frequently experiences high-risk operations with extremely severe failure consequences is classified as high-safety level; equipment that occasionally experiences medium-risk operations with severe failure consequences is classified as medium-safety level; and equipment that rarely experiences low-risk operations with minor failure consequences is classified as low-safety level. An initial conflict coefficient is preset for each safety level. It should be noted that the initial conflict coefficient measures the importance and potential risk of the equipment when a conflict may occur. High-safety level equipment has a higher preset initial conflict coefficient, medium-safety level equipment has a medium preset initial conflict coefficient, and low-safety level equipment has a lower preset initial conflict coefficient. In this embodiment, the initial conflict coefficient for high-safety level equipment is set to 3, for medium-safety level equipment to 2, and for low-safety level equipment to 1.

[0021] In the specific implementation process, the specific steps of step S2 are as follows: S201. Each device continuously collects raw multi-source data through sensors at a preset sampling frequency and uploads it to the industrial cloud platform in real time through the underground industrial communication network.

[0022] Specifically, the various sensors installed on the equipment operate continuously at preset sampling frequencies. Position sensors can acquire the equipment's precise location information in the mine in real time, for example, by determining the equipment's coordinates in three-dimensional space using technologies such as laser positioning and inertial navigation. Operational status sensors monitor various operating parameters of the equipment, such as the rotational speed, power, and temperature of the coal mining machine, and the conveying speed and load of the scraper conveyor. Environmental parameter sensors are responsible for collecting environmental data within the mine, such as methane concentration, oxygen content, dust concentration, humidity, and temperature. The collected data is output as electrical signals through multiple sensors. The equipment uploads the raw, multi-source data in real time via an underground industrial communication network. This network can employ a combination of wired and wireless methods. For example, wired communication methods such as fiber optics are used between fixed devices to ensure stable and high-speed data transmission, while wireless communication technologies such as Wi-Fi 6 and 5G are used for mobile devices to ensure timely and accurate data transmission to the industrial cloud platform.

[0023] S202. Perform data cleaning operations on the received raw multi-source data through the industrial cloud platform, including removing outliers and aligning timestamps, to form a structured multi-source dataset.

[0024] Specifically, data analysis and statistical methods are used to detect and remove outliers. For numerical data, such as equipment operating parameters and environmental parameters, a standard deviation-based method is used. A threshold is set, and when the deviation of a data point from the average exceeds this threshold, it is identified as an outlier and removed. For example, for temperature data from a coal mining machine, if most temperature values ​​are between 50-70℃, the average is 60℃, and the standard deviation is 5℃, then a temperature value exceeding 75℃ or falling below 45℃ is considered an outlier. For some categorical or status data, reasonable rules and ranges are set to determine whether a value is an outlier. Through an industrial cloud platform, all data is unified onto a precise timeline based on the data acquisition and transmission times. Interpolation methods are used; for data points with large time intervals, appropriate interpolation is performed based on the values ​​of preceding and following data points to ensure temporal continuity and alignment. For example, if a coal mining machine acquires data 10 times per second, while a scraper conveyor acquires data once per second, the coal mining machine's data is appropriately aggregated or interpolated according to the scraper conveyor's data time points to ensure temporal correspondence between the two. After removing outliers and aligning timestamps, the industrial cloud platform stores and organizes the cleaned data according to a certain structure and format, forming a structured multi-source dataset.

[0025] In the specific implementation process, the specific steps of step S3 are as follows: S301. Extract the current task type and the key shared resource information bound to each device from the multi-source dataset.

[0026] Specifically, after receiving the cleaned multi-source dataset, the industrial cloud platform first categorizes the data according to device identifiers, grouping data from the same device together. Then, for each device's data, it parses it according to preset data formats and field meanings. For example, the dataset may contain a device status field; parsing this field determines whether the device is currently running, stopped, or faulty. Simultaneously, the dataset may also contain a task type identifier field; parsing this field clarifies the specific task the device is currently performing, such as coal mining, transportation, or support tasks. Based on determining the device's task type, it further analyzes the key shared resources bound to that task, such as power resources and transportation interface resources.

[0027] S302. Detect whether there is a situation where multiple devices simultaneously request or occupy the same exclusive resource, and obtain the detection result. If the detection result indicates that such a situation exists, it is determined to be a logical conflict scenario.

[0028] Specifically, a status table is established for each critical shared resource, recording information such as the device identifier currently occupying the resource and the occupation time. For example, for a specific area of ​​space resources in a coal mining face, the status table records which device (such as a coal mining machine) started occupying that area for coal mining operations and when. The status tables of all critical shared resources are traversed to check if multiple devices simultaneously request or occupy the same resource. If, at a certain moment, the status table shows that the coal mining machine and another auxiliary device simultaneously request to occupy the same specific area of ​​space resources in the coal mining face, or if a scraper conveyor and a transfer conveyor simultaneously attempt to occupy the same section of transport channel resources, a conflict is determined to exist. Based on the conflict detection results, if multiple devices are found to simultaneously request or occupy the same exclusive resource, this situation is determined to be a logical conflict scenario, and information such as the device identifier, resource type, and time of conflict are recorded for subsequent processing.

[0029] S303. Based on the identified logical conflict scenarios, dynamically adjust the conflict coefficient of each device and its corresponding security level to obtain the adjusted conflict coefficient.

[0030] Specifically, based on the equipment safety levels defined in step S1, the safety level information corresponding to each device involved in the logical conflict scenario is retrieved from a preset database or configuration file. For example, a coal mining machine may be classified as a high-safety-level device because its failure could lead to serious coal mining operation interruptions and safety accidents; while auxiliary equipment for cleaning loose coal may be classified as a low-safety-level device. The higher the safety level of the device, the greater the increase in its conflict coefficient when a conflict occurs. In this embodiment, for high-safety-level devices, when a conflict occurs, its conflict coefficient is increased by 50% from its original value; for medium-safety-level devices, it is increased by 30%; and for low-safety-level devices, it is increased by 10%.

[0031] In this embodiment, after identifying the logical conflict scenario between the coal mining machine and auxiliary equipment, the industrial cloud platform retrieves from the database that the coal mining machine has a high security level and the auxiliary equipment has a low security level. Based on the conflict coefficient adjustment, the conflict coefficient for high-security-level equipment is increased by 50%, and the conflict coefficient for low-security-level equipment is increased by 10%. The original conflict coefficient for the coal mining machine was 0.8, which is increased to 0.8 × (1 + 50%) = 1.2; the original conflict coefficient for the auxiliary equipment was 0.4, which is increased to 0.4 × (1 + 10%) = 0.44.

[0032] In the specific implementation process, step S4 consists of the following steps: S401. Summarize the conflict coefficients of all devices after adjustment in the current scheduling cycle, sort the devices from high to low according to the conflict coefficient values, and generate a priority sequence.

[0033] Specifically, through the industrial cloud platform, at the end of each scheduling cycle, the conflict coefficients of all devices after dynamic adjustment in step S3 are collected from the database or cache storing the conflict coefficient data of the storage devices. The conflict coefficient data is scattered and stored in the records corresponding to different devices. These data are then gathered together to form a list containing all devices and their conflict coefficients.

[0034] In this embodiment, an unmanned coal mining face has three pieces of equipment: a coal mining machine, a scraper conveyor, and a transfer conveyor. During the current scheduling cycle, after the dynamic adjustment in step S3, the conflict coefficient of the coal mining machine is 0.8, the conflict coefficient of the scraper conveyor is 0.6, and the conflict coefficient of the transfer conveyor is 0.4. The industrial cloud platform aggregates the conflict coefficients of these three pieces of equipment and sorts them in descending order, resulting in the following priority sequence: coal mining machine (conflict coefficient 0.8), scraper conveyor (conflict coefficient 0.6), and transfer conveyor (conflict coefficient 0.4).

[0035] S402. Mark the priority sequence with a timestamp and archive it. Repeat the priority sequence generation step according to the preset scheduling cycle to obtain the updated priority.

[0036] Specifically, after generating the priority sequence, a timestamp is added to the sequence via the industrial cloud platform. The timestamp records the exact moment the priority sequence was generated, typically accurate to the second or even smaller. Adding timestamps facilitates accurate tracking of the generation time of each priority sequence, aiding subsequent data management and analysis, and allowing for backtracking to a specific time point when needed. The timestamped priority sequences are stored in the industrial cloud platform's database or file system. The archiving method can be chosen based on actual needs, such as storing in a relational database for efficient querying and management, or storing as files for easy backup and migration. The purpose of archiving is to preserve priority sequence data long-term, providing historical data support for system operation monitoring, troubleshooting, and performance optimization. According to a preset scheduling cycle, the industrial cloud platform will periodically repeat steps S401 and S402. The scheduling cycle can be set according to the actual operating conditions and needs of the unmanned coal mining system, for example, every minute, every 5 minutes, or every 10 minutes. At the start of each new scheduling cycle, the cloud platform collects the conflict coefficients of the devices again, regenerates the priority sequence, and marks it with a new timestamp for archiving. Through this periodic repetition, the device priorities are updated in a timely manner, ensuring that scheduling decisions are always based on the latest device status and conflict situation. After the periodic repetition of generation and archiving, each newly generated priority sequence is the updated priority relative to the previous sequence. It should be noted that the updated priority reflects the relative importance and scheduling priority of the devices at different points in time, so as to better adapt to the dynamically changing operating environment of the unmanned coal mining system and improve the scheduling flexibility and accuracy of the system.

[0037] In this embodiment, the preset scheduling period is 5 minutes. At the initial moment, the first priority sequence is generated and timestamped at 10:00:00, and archived in the database. After 5 minutes, at 10:05:00, the industrial cloud platform executes step S401 again, collecting the conflict coefficients of the three devices. The conflict coefficient of the coal mining machine becomes 0.7, the scraper conveyor becomes 0.5, and the transfer machine becomes 0.3. The priority sequence is regenerated as follows: coal mining machine (conflict coefficient 0.7), scraper conveyor (conflict coefficient 0.5), transfer machine (conflict coefficient 0.3), and timestamped at 10:05:00 before being archived. This newly generated priority sequence is the updated priority relative to the time of 10:00:00. After another 5 minutes, at 10:10:00, the industrial cloud platform will repeat the above operation to generate a new updated priority sequence, and so on.

[0038] In the specific implementation process, the specific steps of step S5 are as follows: S501: Read the updated priority through the industrial cloud platform, and plan the operation path, task sequence or resource allocation scheme for each device according to the priority level, and obtain the planning results, including the priority order of operation path passage, the order of task sequence operation or the priority order of energy supply allocation according to the priority from high to low.

[0039] Specifically, the industrial cloud platform retrieves the priority information of all devices from a database or cache that stores updated priorities. Based on the device's job type and priority, it determines the order in which each device begins task execution. Higher-priority devices are assigned tasks first to avoid conflicts in task execution times between different devices. For example, in a coal mining operation, the highest-priority coal mining machine is assigned to mine coal first. After it completes a certain workload, subsequent transportation equipment is assigned to transport the coal. Resources required for equipment operation, such as electricity and water, are allocated according to priority. This ensures that higher-priority devices receive sufficient resource support to guarantee their normal operation. For instance, in situations with limited electricity resources, higher-priority devices are prioritized for power supply to avoid downtime due to insufficient resources. Finally, information such as the priority of each device's work path, the order of task execution, and the priority of energy supply allocation are integrated to form a complete planning result.

[0040] In this embodiment, an unmanned coal mining face includes three pieces of equipment: a coal mining machine, a scraper conveyor, and a crusher. After calculation in step S4, the updated priority order is: coal mining machine (priority 1), scraper conveyor (priority 2), and crusher (priority 3). After reading this priority order through the industrial cloud platform, planning begins. For the coal mining machine, it is planned to mine coal from its current position along an unobstructed path closest to the coal seam. For the scraper conveyor, it is scheduled to start 5 minutes after the coal mining machine begins mining, transporting the mined coal to a designated location. For the crusher, it is scheduled to start 3 minutes after the scraper conveyor transports the coal, crushing it. Simultaneously, in terms of power resource allocation, priority is given to ensuring the coal mining machine's power needs, guaranteeing its continuous and stable operation.

[0041] S502. The planning results are converted into specific scheduling instructions and sent to the local controller of the corresponding equipment through the industrial communication network.

[0042] Specifically, the industrial cloud platform transforms the planning results generated in step S501 into a format that the local controller of the equipment can recognize and execute. For example, the work path planning is transformed into instructions for the direction and speed of equipment movement, the task timing planning is transformed into instructions for the start and stop of equipment, and the resource allocation planning is transformed into instructions for limiting the use of equipment resources. The transformed instructions are then encapsulated, and equipment identification information is added to ensure that each instruction can be accurately sent to the local controller of the corresponding equipment. Then, the encapsulated scheduling instructions are sent to the local controller of the corresponding equipment through an underground industrial communication network, such as Ethernet or a wireless communication network.

[0043] In this embodiment, the coal mining machine's operating path planning is converted into an instruction of "move forward at a speed of 2 meters per minute" and the start-up time planning is converted into an instruction of "start at 10:00:00" via an industrial cloud platform; the scraper conveyor's transport instruction is converted into an instruction of "start, transport speed of 10 tons per hour" and the start-up time instruction is "start at 10:05:00"; the crusher's crushing instruction is converted into an instruction of "start, crushing capacity of 5 tons per hour" and the start-up time instruction is "start at 10:08:00". Then, the cloud platform encapsulates these instructions, adds equipment identifiers, and sends them to the local controllers of the coal mining machine, scraper conveyor, and crusher via an underground industrial Ethernet network.

[0044] S503: After receiving the scheduling instructions, each device executes the control logic locally to perform distributed scheduling.

[0045] Specifically, the local controllers of each device receive scheduling instructions from the industrial cloud platform via a communication interface, decrypt and parse the instructions to obtain specific control information, and then execute corresponding operations based on the parsed instructions and the device's own control algorithms and logic. For example, the local controller of a coal mining machine controls the machine's traveling mechanism according to movement instructions, causing it to move along a planned path; the local controller of a scraper conveyor controls the conveyor belt's operating speed according to transportation instructions to transport coal; and the local controller of a crusher controls the working status of the crushing device according to crushing instructions to complete the coal crushing task.

[0046] Example 2 like Figure 2 As shown, the equipment system scheduling system based on unmanned coal mining includes: The safety rating module is used to classify safety levels based on the operational risks and severity of failure consequences of unmanned coal mining equipment, and to preset conflict coefficients for each safety level; The data acquisition module is used to collect multi-source data in real time through sensors installed on the equipment, including location information, operating status and environmental parameters, and to obtain a multi-source dataset by uploading the data to the industrial cloud platform for cleaning. The conflict identification module is used to identify device conflict scenarios based on multi-source datasets and dynamically adjust the conflict coefficient in combination with preset security levels. The priority scheduling module is used to divide priorities in descending order according to the adjusted conflict coefficient, and to recalculate the conflict coefficient and update the priorities according to a preset period. The instruction issuance module is used to generate scheduling instructions based on the updated priority of the industrial cloud platform and to perform distributed scheduling of equipment.

[0047] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0048] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0049] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for scheduling equipment systems based on unmanned coal mining, characterized in that, Includes the following steps: S1. Classify safety levels based on the operational risks and severity of failure consequences of unmanned coal mining equipment, and preset conflict coefficients for each safety level; S2. Collect multi-source data in real time through sensors installed on the equipment, including location information, operating status and environmental parameters, and obtain multi-source datasets by uploading the data to the industrial cloud platform for cleaning. S3. Identify device conflict scenarios based on multi-source datasets and dynamically adjust the conflict coefficient in conjunction with preset security levels; S4. Based on the adjusted conflict coefficient, prioritize the items according to the sequence from high to low, and recalculate the conflict coefficient and update the priority according to the preset period. S5. Based on the industrial cloud platform, scheduling instructions are generated according to the updated priorities to perform distributed scheduling of equipment.

2. The equipment system scheduling method based on unmanned coal mining according to claim 1, characterized in that, The specific steps of S1 are as follows: S101. Based on historical safety cases, establish a risk consequence assessment matrix according to the operational risks that the equipment may cause during operation and the severity of its failure consequences; S102. Based on the risk consequence assessment matrix, divide the equipment into several safety levels and preset an initial conflict coefficient for each safety level.

3. The equipment system scheduling method based on unmanned coal mining according to claim 1, characterized in that, The specific steps of S2 are as follows: S201. Each device continuously collects raw multi-source data through sensors at a preset sampling frequency and uploads it to the industrial cloud platform in real time through the underground industrial communication network. S202. Perform data cleaning operations on the received raw multi-source data through the industrial cloud platform, including removing outliers and aligning timestamps, to form a structured multi-source dataset.

4. The equipment system scheduling method based on unmanned coal mining according to claim 1, characterized in that, The specific steps for S3 are as follows: S301. Extract the task type currently being executed by each device and the key shared resource information bound to it from the multi-source dataset; S302. Detect whether there is a situation where multiple devices simultaneously request or occupy the same exclusive resource, and obtain the detection result. If the detection result indicates that such a situation exists, it is determined to be a logical conflict scenario. S303. Based on the identified logical conflict scenarios, dynamically adjust the conflict coefficient of each device and its corresponding security level to obtain the adjusted conflict coefficient.

5. The equipment system scheduling method based on unmanned coal mining according to claim 1, characterized in that, The specific steps of S4 are as follows: S401. Summarize the conflict coefficients of all devices after adjustment in the current scheduling cycle, sort the devices from high to low according to the conflict coefficient values, and generate a priority sequence. S402. Mark the priority sequence with a timestamp and archive it. Repeat the priority sequence generation step according to the preset scheduling cycle to obtain the updated priority.

6. The equipment system scheduling method based on unmanned coal mining according to claim 1, characterized in that, The specific steps of S5 are as follows: S501. Read the updated priority through the industrial cloud platform, and plan the operation path, task sequence or resource allocation scheme for each device according to the priority level, and obtain the planning results, including the priority order of operation path passage, the order of task sequence operation or the priority order of energy supply allocation according to the priority from high to low. S502. Transform the planning results into specific scheduling instructions and send them to the local controller of the corresponding equipment through the industrial communication network; S503: After receiving the scheduling instructions, each device executes the control logic locally to perform distributed scheduling.

7. A system for scheduling equipment in unmanned coal mining, employing the system scheduling method for scheduling equipment in unmanned coal mining as described in any one of claims 1-6, characterized in that, The scheduling system includes: The safety rating module is used to classify safety levels based on the operational risks and severity of failure consequences of unmanned coal mining equipment, and to preset conflict coefficients for each safety level. The data acquisition module is used to collect multi-source data in real time through sensors installed on the equipment, including location information, operating status and environmental parameters, and to obtain a multi-source dataset by uploading the data to the industrial cloud platform for cleaning. The conflict identification module is used to identify device conflict scenarios based on multi-source datasets and dynamically adjust the conflict coefficient in combination with preset security levels. The priority scheduling module is used to divide priorities in descending order according to the adjusted conflict coefficient, and to recalculate the conflict coefficient and update the priorities according to a preset period. The instruction issuance module is used to generate scheduling instructions based on the updated priority of the industrial cloud platform and to perform distributed scheduling of equipment.

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