Coal mine-oriented computing task scheduling method, device, system and electronic equipment

Abstract

The present disclosure provides a coal mine-oriented computing task scheduling method and device, system and electronic equipment, relating to the technical field of edge computing, according to the real-time requirement of coal mine production business for computing tasks, multiple task categories including super real-time key tasks are divided, based on the task category, real-time collected network state information and computing node load information, the computing task is dynamically distributed to the terminal device, edge computing node or cloud server for execution, and the super real-time key task is preferentially distributed to the terminal device or the preferred edge computing node connected with it and its data transmission priority is guaranteed, at the same time, a redundant execution instance is established on at least one other computing node for the executing super real-time key task, and a preconfigured fault-tolerant processing mechanism is enabled when a communication interruption or node failure of the task execution environment is monitored, thus, the problem of key safety task response delay caused by not combining the real-time demand of coal mine business for task classification can be solved.

Description

Technical Field

[0001] This disclosure relates to the field of edge computing technology, and in particular to a computing task scheduling method, apparatus, system, and electronic device for coal mines. Background Technology

[0002] With the advancement of intelligent coal mine construction, remote control, unmanned driving, and intelligent inspection equipment are widely used in underground and open-pit mines. These devices rely on advanced technologies such as computer vision and AI algorithms, generating massive real-time data processing demands. However, the coal mine environment presents significant limitations: network bandwidth is limited and unstable, computing resources are unevenly distributed, and the real-time requirements of different tasks vary greatly (e.g., collision avoidance requires millisecond-level response, while geological modeling can tolerate longer latency), and the requirements for safety and reliability are extremely high.

[0003] Existing technologies have significant shortcomings: traditional centralized architectures exhibit lag and vulnerability in the complex environment of coal mines; while edge computing architectures offload some computing to local environments, their scheduling strategies do not deeply integrate with coal mine business logic, safety levels, and extreme environmental constraints, lacking targeted task classification and hierarchical scheduling mechanisms. General load balancing or proximity computing strategies cannot guarantee millisecond-level responses for safety-critical tasks such as gas identification and equipment collision avoidance, and critical tasks are prone to being blocked. At the same time, traditional solutions lack awareness and coordination of production processes and effective network outage fault tolerance mechanisms. Tasks will be suspended when the network is interrupted or a node fails, seriously affecting operational continuity and safe production. Summary of the Invention

[0004] This disclosure provides a method, apparatus, system, and electronic device for scheduling computational tasks in coal mines. Its main objective is to at least partially address one of the technical problems in related technologies.

[0005] According to a first aspect of this disclosure, a computational task scheduling method for coal mines is provided, comprising:

[0006] Based on the real-time requirements of coal mine production operations for computing tasks, the computing tasks to be scheduled are divided into multiple task categories, including ultra-real-time critical tasks. Based on the task category, real-time collected network status information, and computing node load information, the computing task is dynamically allocated to at least one of the terminal device, edge computing node, or cloud server for execution. For the ultra-real-time critical task, it is preferentially allocated to the terminal device or the preferred edge computing node with which it is connected for execution, and the priority of ultra-real-time critical task data transmission is guaranteed. For the aforementioned real-time critical task in execution, redundant execution instances are established on at least one other computing node; When a communication interruption or node failure is detected in the task execution environment, a pre-configured fault tolerance mechanism is activated to maintain the computing task.

[0007] According to a second aspect of this disclosure, a computing task scheduling device for coal mines is provided, comprising: The classification unit is used to establish a multi-level task classification model based on the different timeliness requirements of computing tasks in coal mine safety monitoring, equipment control and production management, so as to classify the computing tasks to be scheduled, including at least one type of safety-critical task with an upper limit requirement for delay. The allocation unit is used to dynamically allocate various computing tasks to matching computing nodes in the hierarchical computing system based on the task classification results and in combination with the real-time network topology status, link quality and computing node load information. Among them, for the safety-critical tasks, priority is given to allocating them to the task-generating source device or its nearby edge computing nodes, and communication resources with quality of service assurance are allocated to the safety-critical tasks. A synchronization unit is used to establish synchronous or asynchronous redundant computing copies on at least one other independent computing node for the safety-critical tasks assigned to edge computing nodes for execution. The startup unit is used to initiate corresponding fault recovery or task succession mechanisms according to predefined strategies when a network connection anomaly or computing node failure is detected during task execution, so as to ensure the continuous or degraded reliable execution of computing tasks, especially the safety-critical tasks.

[0008] According to a third aspect of this disclosure, a computing task scheduling system for coal mines is provided, comprising: the apparatus as described in the second aspect above.

[0009] According to a fourth aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0010] According to a fifth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0011] According to a sixth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0012] The computing task scheduling method, apparatus, system, and electronic equipment disclosed herein for coal mines categorize computing tasks into multiple categories, including ultra-real-time critical tasks, based on the real-time requirements of coal mine production operations. Based on this task category, real-time collected network status information, and computing node load information, computing tasks are dynamically allocated to terminal devices, edge computing nodes, or cloud servers for execution. Ultra-real-time critical tasks are preferentially allocated to terminal devices or their preferred edge computing nodes with communication connections, ensuring their data transmission priority. Simultaneously, redundant execution instances are established on at least one other computing node for ultra-real-time critical tasks in progress. Furthermore, if a communication interruption or node failure is detected in the task execution environment, the system will respond accordingly. By enabling a pre-configured fault-tolerant processing mechanism, this technology can solve the problems in existing technologies caused by the lack of targeted task classification based on the real-time needs of coal mine operations, the lack of dynamic adaptability in resource allocation, the lack of priority guarantees for critical tasks, and the lack of effective redundancy and fault tolerance mechanisms. These problems result in delayed response to critical safety tasks, susceptibility to being blocked by low-priority tasks, difficulty in coping with network fluctuations or node failures, and inefficient utilization of computing resources. The technology achieves the following effects: accurately matching the differentiated computing task requirements of coal mine production operations, ensuring rapid response and execution stability of ultra-real-time critical tasks, improving the rationality of computing resource allocation, enhancing the system's anti-interference and fault tolerance capabilities, and ensuring the continuous and reliable execution of computing tasks related to coal mine production.

[0013] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0014] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A flowchart illustrating a computational task scheduling method for coal mines provided in this embodiment of the present disclosure; Figure 2 A schematic diagram of a computing task scheduling device for coal mines provided in an embodiment of this disclosure; Figure 3 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0015] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0016] The following describes, with reference to the accompanying drawings, a method, apparatus, system, and electronic device for scheduling computational tasks in coal mines according to embodiments of the present disclosure.

[0017] Figure 1 This is a flowchart illustrating a computational task scheduling method for coal mines provided in an embodiment of this disclosure.

[0018] like Figure 1 As shown, the method includes the following steps: Step 101: Based on the different timeliness requirements of computational tasks in coal mine safety monitoring, equipment control and production management, establish a multi-level task classification model to classify the computational tasks to be scheduled, including at least one type of safety-critical task with an upper limit on delay.

[0019] In the embodiments of this disclosure, a multi-level task classification model is constructed for various computing tasks related to safety monitoring, equipment control, and production management in coal mine scenarios, based on their differentiated requirements for execution timeliness. This model systematically classifies the computing tasks to be scheduled, including at least one type of safety-critical task with a clearly defined upper limit on latency (i.e., the ultra-real-time critical task ensuring safe production in coal mines in the patented technology). The core of this classification model lies in dividing the computing tasks into levels based on the rigid constraints on execution timeliness and business importance, so that different types of computing tasks obtain scheduling priorities and resource guarantee foundations that match their characteristics, providing a classification basis for subsequent accurate scheduling. As one implementation method, computing tasks can be divided into four levels according to real-time requirements, where the upper limit of latency for safety-critical tasks is set to a specific millisecond value, corresponding to task types directly related to safe production, such as equipment anti-collision calculation and gas identification.

[0020] By establishing a multi-level task classification model that aligns with the operational needs of coal mines, the problem of insufficient timeliness for safety-critical tasks due to the lack of targeted task classification in existing technologies is effectively solved. This model provides differentiated classification criteria for computing tasks with different timeliness requirements, ensuring that the execution timeliness of safety-critical tasks is given priority, laying the foundation for the accurate implementation of subsequent scheduling strategies, and improving the targeting and security of distributed computing task scheduling in coal mines.

[0021] Step 102: Based on the task classification results and combined with the real-time network topology status, link quality and computing node load information, dynamically allocate various computing tasks to matching computing nodes in the hierarchical computing system for execution. Among them, for the safety-critical tasks, priority is given to allocating them to the task-generating source device or its nearby edge computing nodes, and communication resources with quality of service assurance are allocated to the safety-critical tasks.

[0022] In the embodiments of this disclosure, after classifying computing tasks, based on the obtained task classification results and combined with real-time collected network topology status, link quality, and computing node load information, various computing tasks are dynamically allocated to suitable computing nodes in the hierarchical computing system for execution. The core lies in achieving precise resource matching based on task characteristics and real-time environment status. For clearly defined safety-critical tasks (i.e., ultra-real-time critical tasks), they are preferentially allocated to the task-generating device or its nearby edge computing nodes to minimize data transmission paths and reduce latency. Communication resources with Quality of Service (QoS) guarantees are specifically allocated to these tasks to prevent interference from other network traffic. As one implementation method, the real-time acquired network status information may include link bandwidth utilization, round-trip latency, packet loss rate, etc., and the computing node load information may cover CPU utilization, memory availability, I / O throughput, etc. QoS guarantees can be achieved through reserving dedicated virtual channels, using VPN+QoS rate limiting, or the TSN protocol stack.

[0023] By combining real-time environmental conditions with task classification results for dynamic task allocation, and providing priority execution nodes and dedicated communication resources for safety-critical tasks, this technology effectively solves the problems of lack of adaptability in resource allocation and susceptibility of critical tasks to network congestion or low-priority tasks in existing technologies. It can significantly reduce the execution latency of safety-critical tasks, improve the overall utilization efficiency of computing resources, and ensure the operational stability and security of the coal mine distributed computing system.

[0024] Step 103: For the security-critical tasks assigned to edge computing nodes for execution, establish synchronous or asynchronous redundant computing copies on at least one other independent computing node.

[0025] In the embodiments of this disclosure, after a safety-critical task (i.e., a real-time critical task) is assigned to an edge computing node for execution, to mitigate the impact of a single computing node failure on the continuity and reliability of task execution, this technical solution constructs a redundant computing copy in synchronous or asynchronous mode on at least one other independent computing node. This redundant deployment provides dual protection for task execution, ensuring that the safety-critical task can continue even in the event of a failure or overload of the primary execution node. The core function of this redundant copy is to provide an alternative execution path for the safety-critical task. Its deployment mode can be flexibly selected as synchronous or asynchronous based on task characteristics and system resource conditions, ensuring both consistency in task execution and efficient resource utilization. As one implementation method, synchronous redundant copies of the safety-critical task can be established on two independent edge computing nodes, allowing the copies to synchronize their execution status with the primary task in real time, ensuring that the backup node can quickly take over in the event of a primary node failure.

[0026] By configuring redundant computing copies for safety-critical tasks executed by edge computing nodes, the problem of lacking targeted fault tolerance guarantees and the easy interruption of safety-critical tasks due to single node failures in existing technologies is effectively solved. This significantly improves the anti-interference capability and continuity of safety-critical task execution, providing reliable support for the stable advancement of computing tasks related to coal mine safety production.

[0027] Step 104: When a network connection anomaly or computing node failure is detected during task execution, the corresponding fault recovery or task continuation mechanism is initiated according to a predefined strategy to ensure the continuous or degraded reliable execution of computing tasks, especially the safety-critical tasks.

[0028] In the embodiments of this disclosure, the network connection status and the operating status of computing nodes are continuously monitored throughout the entire execution of the computing task. When network connection anomalies (such as interruptions or congestion) or computing node failures (such as overload or failure) are detected, the corresponding fault recovery mechanism or task succession mechanism is automatically activated according to the predefined fault handling strategy. The core is to avoid task execution interruption through preset response logic, ensuring that various computing tasks, especially safety-critical tasks (i.e., ultra-real-time critical tasks), can continue to execute, or maintain reliable operation in a degraded manner if the original execution accuracy cannot be maintained. As one implementation method, when the network connection is abnormal, the embedded cache of the front-end device and the lightweight inference engine can be enabled to output approximate results. When the computing node fails, a rapid switch of redundant replicas can be triggered. After the anomaly is resolved, the data is synchronized to complete high-precision calculations, achieving seamless task succession.

[0029] By pre-setting fault handling strategies and corresponding recovery and continuation mechanisms, the system effectively solves the problems of lack of effective fault tolerance and network interruption recovery capabilities in existing technologies, and the easy interruption of tasks under abnormal conditions. It significantly improves the system's anti-interference ability and robustness in complex environments, ensures the continuous and reliable execution of safety-critical tasks, and provides a solid technical guarantee for safe production in coal mines.

[0030] The computing task scheduling method for coal mines disclosed herein categorizes computing tasks into multiple categories, including ultra-real-time critical tasks, based on the real-time requirements of coal mine production operations. Based on this task category, real-time collected network status information, and computing node load information, computing tasks are dynamically allocated to terminal devices, edge computing nodes, or cloud servers for execution. Ultra-real-time critical tasks are preferentially allocated to terminal devices or their preferred edge computing nodes with communication connections, ensuring their data transmission priority. Simultaneously, redundant execution instances are established on at least one other computing node for ultra-real-time critical tasks in progress. Furthermore, pre-configuration is activated when communication interruptions or node failures are detected in the task execution environment. The fault-tolerant processing mechanism can solve the problems in existing technologies caused by the lack of targeted task classification based on the real-time needs of coal mine operations, the lack of dynamic adaptability in resource allocation, the lack of priority guarantees for critical tasks, and the lack of effective redundancy and fault tolerance mechanisms, which lead to delayed response to critical safety tasks, susceptibility to being blocked by low-priority tasks, difficulty in coping with network fluctuations or node failures, and inefficient utilization of computing resources. It achieves the technical effects of accurately matching the differentiated computing task requirements of coal mine production operations, ensuring the rapid response and execution stability of ultra-real-time critical tasks, improving the rationality of computing resource allocation, enhancing the system's anti-interference and fault tolerance capabilities, and ensuring the continuous and reliable execution of computing tasks related to coal mine production.

[0031] As a specific implementation of this disclosure, based on the basic scheme, the establishment of the multi-level task classification model is further defined as follows: according to the maximum allowed end-to-end processing latency of the task, the task is divided into multiple levels, wherein the first level task corresponds to safety-critical applications with processing latency requirements of less than 10 milliseconds, the second level task corresponds to real-time interactive applications with processing latency requirements of 10 milliseconds to 500 milliseconds, the third level task corresponds to near real-time analysis applications with processing latency requirements of 500 milliseconds to 5 seconds, and the fourth level task corresponds to non-real-time computing applications with processing latency requirements of more than 5 seconds.

[0032] Specifically, the implementation of the multi-level task classification model is as follows: using the maximum allowable end-to-end processing latency as the core criterion, all scheduled computational tasks in the coal mine production scenario are clearly divided into four levels. The first level tasks have a maximum end-to-end processing latency of less than 10 milliseconds, corresponding to safety-critical applications directly related to coal mine safety production, such as equipment collision avoidance calculation and emergency stop signal processing. The second level tasks have a maximum end-to-end processing latency between 10 and 500 milliseconds, corresponding to real-time interactive applications requiring real-time feedback, such as personnel / vehicle recognition, AR projection, and gas detection. The third level tasks have a maximum end-to-end processing latency between 500 milliseconds and 5 seconds, corresponding to near-real-time analytical applications that do not require immediate response but need to complete analysis quickly, such as path planning and data statistics. The fourth level tasks have a maximum end-to-end processing latency greater than 5 seconds, corresponding to non-real-time computational applications that do not have strict real-time requirements, such as 3D map reconstruction and predictive maintenance training. Each level of task corresponds to a clear latency threshold standard, forming a standardized Service Level Agreement (SLA), providing a clear and quantifiable classification basis for the subsequent formulation of scheduling strategies.

[0033] By defining four levels of task classification with clear latency thresholds, the system accurately matches the real-time requirements of different business operations in coal mine production. This solves the problems of vague task classification and lack of quantitative standards in existing technologies, and rigidly defines the millisecond-level latency requirements of safety-critical applications. This lays a clear foundation for prioritizing their execution resources and provides clear guidance for the reasonable scheduling of other types of tasks, thereby improving the classification accuracy and execution order of the entire distributed computing task scheduling system.

[0034] As a specific implementation of this disclosure, based on the basic scheme, the method further defines the dynamic allocation of various computing tasks to matching computing nodes in the hierarchical computing system based on task classification results and combined with real-time acquired network topology status, link quality, and computing node load information. This includes: continuously monitoring the available bandwidth, transmission latency, and data packet loss rate of each communication link in the hierarchical computing system, as well as the CPU utilization, memory usage, and input / output throughput of each computing node; selecting computing nodes that can meet the latency requirements of the safety-critical tasks based on the latency requirements and monitoring information, and dynamically establishing or reserving an end-to-end communication path with priority transmission guarantees.

[0035] Specifically, the implementation process of dynamically allocating computing tasks based on task classification results is as follows: Through the integrated network status monitor and load monitor, key operating parameters of the hierarchical computing system are continuously collected. Communication link-related monitoring information includes available bandwidth, transmission latency, and data packet loss rate for each link. Computing node-related monitoring information covers the CPU utilization, memory usage, and I / O throughput of each terminal device, edge computing node, and cloud server, ensuring real-time monitoring of network and resource dynamics. For safety-critical tasks (i.e., ultra-real-time critical tasks), a preset latency limit (e.g., less than 10 milliseconds) is used as the core constraint. Node selection is performed based on the aforementioned real-time monitoring data, prioritizing the task's source device or nearby edge computing nodes with reasonable CPU utilization and memory usage that meet the latency requirements. Simultaneously, an end-to-end communication path is dynamically established or reserved at the network layer for this safety-critical task. This path is given transmission priority by enabling VPN+QoS rate limiting or TSN protocol stacks to prevent network traffic from other non-critical tasks from occupying its bandwidth resources, ensuring the stability and timeliness of data transmission.

[0036] By accurately monitoring the core operating parameters of network links and computing nodes, a quantitative basis is provided for node selection for safety-critical tasks. At the same time, by establishing a communication path with priority transmission guarantee, the problems of lack of dynamic adaptability in resource allocation and susceptibility of critical tasks to network congestion in existing technologies are effectively solved. This ensures that safety-critical tasks can obtain stable execution resources and transmission channels, strictly meet their millisecond-level latency requirements, and significantly improve the determinism and reliability of safety-critical task execution in coal mine distributed computing systems.

[0037] As a specific implementation of this disclosure, based on the basic scheme, the allocation of communication resources with quality of service assurance for the safety-critical task is further defined as follows: when it is detected that the transmission quality of the current communication path serving the safety-critical task is lower than a preset threshold, the control plane function quickly reroutes the data stream of the task to a pre-planned or dynamically calculated backup communication path.

[0038] Specifically, when allocating communication resources with quality of service guarantees for safety-critical tasks (i.e., ultra-real-time critical tasks), the core system ensures transmission reliability through a dynamic path switching mechanism. The system first presets a transmission quality assessment threshold, which is set based on the latency requirements of the safety-critical task (e.g., less than 10 milliseconds). This threshold specifically covers key indicators such as round-trip time (RTT), packet loss rate, and bandwidth utilization of the communication path. For example, the preset RTT threshold is 100ms, and the packet loss rate threshold is 5%. The control plane function works in real time with the network status monitor to continuously collect and evaluate the transmission quality data of the communication path currently serving the safety-critical task. When the round-trip latency of the path exceeds 100ms, the packet loss rate is higher than 5%, or the bandwidth utilization rate reaches 90% or more, indicating that the transmission quality is below the preset threshold, the control plane function immediately initiates a fast rerouting process. Without interrupting task execution, the data stream of the safety-critical task is quickly switched to a pre-planned backup communication path (such as a redundant link within the same local area network) or an idle link dynamically determined through real-time calculation. The entire rerouting process is controlled within milliseconds, ensuring that the task data stream transmission does not experience significant interruption or a spike in latency.

[0039] By monitoring the transmission quality of the communication path in real time and triggering a fast rerouting mechanism, the problem of single communication path for critical tasks and transmission delay or interruption due to link quality degradation in existing technologies is effectively solved. This ensures that safety-critical tasks can still obtain a stable transmission channel when the main communication path is abnormal, strictly meeting their millisecond-level latency requirements, and further improving the anti-interference capability and reliability of safety-critical task transmission in coal mine distributed computing systems.

[0040] As a specific implementation of this disclosure, based on the basic scheme, the method of initiating the corresponding fault recovery or task succession mechanism according to the predefined strategy is further defined as follows: when a fault is detected in the main edge computing node executing the safety-critical task, the processing state of the task is seamlessly switched to the backup computing node containing the redundant computing copy within less than 50 milliseconds, and the backup computing node takes over the execution.

[0041] Specifically, the fault recovery mechanism for primary edge computing node failures is implemented as follows: The system configures a continuous heartbeat detection mechanism for the primary edge computing node executing safety-critical tasks (i.e., ultra-real-time critical tasks) to monitor its operating status in real time, including the stability of the central processing unit (CPU), the connectivity of communication links, and the task execution process status. Simultaneously, a backup computing node containing a redundant computational copy of the safety-critical task maintains real-time synchronization with the primary edge computing node's task processing status. The backup node is pre-loaded with the complete operating environment, algorithm model, and real-time data required for task execution, ensuring that the copy's execution progress is consistent with the primary task. When the heartbeat detection mechanism detects a failure in the primary edge computing node (such as a crash, overload failure, or communication interruption), the system immediately triggers a primary-backup switchover process. Without manual intervention, the transfer of task processing control is completed in less than 50 milliseconds, seamlessly switching the current processing status of the task to the backup computing node. The backup computing node directly continues the unfinished computation process from the primary node, continuously outputting task results. The entire switchover process has no impact on the continuity of task execution.

[0042] By implementing a real-time status synchronization and millisecond-level fast switching mechanism between primary and backup nodes, the problem of primary node failure leading to interruption of safety-critical tasks in existing technologies is effectively solved. This ensures that safety-critical tasks can continue to execute even when the primary node fails, strictly meeting its millisecond-level latency requirements. It significantly improves the reliability and fault tolerance of safety-critical task execution in coal mine distributed computing systems, providing solid technical support for safe coal mine production.

[0043] As a specific implementation of this disclosure, based on the basic scheme, the method of initiating the corresponding fault recovery or task continuation mechanism according to the predefined strategy is further defined as follows: when an interruption is detected in the network connection between the terminal device and the computing node allocated to it, the terminal device is controlled to call its local pre-built lightweight computing model to continuously calculate the safety-critical task and output the results based on simplified algorithms or historical data; after the network connection is restored, the computing logs and intermediate results generated locally during the interruption are uploaded to the backend node of the hierarchical computing system for complete data synchronization and high-precision result calibration.

[0044] Specifically, the task continuation mechanism for network connection interruptions is implemented as follows: The terminal device has a pre-built embedded cache and a lightweight inference engine, and a locally pre-configured lightweight computing model adapted for safety-critical tasks (i.e., ultra-real-time critical tasks). This model is a simplified version of the corresponding high-precision algorithm and can run quickly on terminal devices with limited resources. The system continuously monitors the network connection status between the terminal device and the assigned computing nodes. When a network connection interruption is detected (such as loss of underground wireless signal), the terminal device immediately triggers a local computing switchover process, automatically calling the pre-configured lightweight computing model. Based on historical data stored in the embedded cache and real-time collected basic data, it continuously calculates the safety-critical task and outputs approximate results derived from simplified algorithms or historical data (e.g., inferring the current position based on the historical trajectory of obstacles), ensuring uninterrupted task execution. Once the network connection is restored, the terminal device automatically initiates the data synchronization process, uploading the complete computing logs, intermediate results, and output data generated locally during the network interruption to the backend nodes (such as edge cloud or central cloud) in the hierarchical computing system. The backend nodes then use high-precision algorithms to process the uploaded data, complete the result calibration and data completion, and achieve a seamless connection between "offline operation and online synchronization".

[0045] By pre-installing a lightweight computing model and embedded cache on the terminal device, and combining a mechanism of continuous local computing during network outages and data synchronization calibration after network reconnection, the problem of task termination due to network interruption in existing technologies is effectively solved. This ensures that safety-critical tasks in complex coal mine environments can continue to output effective results even when the network is unstable, and high-precision calculations can be resumed after network reconnection. This not only ensures the continuity of operations but also takes into account the accuracy of calculation results, significantly improving the system's adaptability and reliability in scenarios with intermittent network outages underground.

[0046] As a specific implementation of this disclosure, based on the basic solution, the embodiments of this disclosure further include: responding to a preset event related to the process flow that occurs in the coal mine production system, identifying multiple logically related computing tasks triggered by the event; and coordinating the multiple logically related computing tasks to be executed on the same edge computing node or a cluster of multiple edge computing nodes located in the same local area network.

[0047] Specifically, the collaborative scheduling mechanism based on the coal mine production process is implemented as follows: The system pre-integrates the coal mine standard operating procedure (SOP) and links in real time with the monitoring module and sensor network of the coal mine production system to continuously sense various preset events in the production process. These preset events include scenarios directly related to the process, such as electric shovels loading materials onto designated mining trucks, intelligent inspection robots entering specific work areas, and the initiation of collaborative operations between equipment. When such specific events are detected, the system automatically identifies multiple computing tasks triggered by the event that have data interaction or collaborative dependencies in business logic through a task association mapping algorithm. For example, AR display tasks, visual recognition tasks, and material loading status monitoring tasks corresponding to the collaborative operation of electric shovels and mining trucks. Subsequently, the scheduling engine, based on the real-time edge computing node distribution and local area network topology information, collaboratively schedules these logically related computing tasks to the same edge computing node or to a cluster of multiple edge computing nodes located in the same local area network for execution. This ensures that related tasks complete data interaction in a close-range network environment, minimizing cross-node and cross-regional communication overhead and improving the collaborative response speed between tasks.

[0048] By deeply integrating with the coal mine production process, collaborative scheduling of logically related tasks is realized, effectively solving the problems of scheduling strategies being divorced from production reality and large communication overhead across equipment tasks in existing technologies. It significantly reduces data transmission latency and interaction loss between related tasks, improves the smoothness of human-computer interaction and the overall collaborative efficiency of production operations, and makes the scheduling strategy more in line with the actual production needs of coal mines, further enhancing the practicality and adaptability of the distributed computing task scheduling system.

[0049] It should be noted that the embodiments of this disclosure may include multiple steps. For ease of description, these steps are numbered, but these numbers are not a limitation on the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of this disclosure do not limit this.

[0050] Corresponding to the aforementioned computational task scheduling method for coal mines, this disclosure also proposes a computational task scheduling device for coal mines. Since the device embodiments of this disclosure correspond to the aforementioned method embodiments, details not disclosed in the device embodiments can be referred to the aforementioned method embodiments, and will not be repeated here.

[0051] Figure 2 This is a schematic diagram of the structure of a computing task scheduling device for coal mines provided in an embodiment of this disclosure, as shown below. Figure 2 As shown, it includes: Classification unit 21 is used to establish a multi-level task classification model based on the different timeliness requirements of computing tasks in coal mine safety monitoring, equipment control and production management, so as to classify the computing tasks to be scheduled, including at least one type of safety-critical task with an upper limit requirement for delay. The allocation unit 22 is used to dynamically allocate various computing tasks to matching computing nodes in the hierarchical computing system based on the task classification results and in combination with the real-time network topology status, link quality and computing node load information. For the safety-critical tasks, priority is given to allocating them to the task-generating source device or its nearby edge computing nodes, and communication resources with quality of service assurance are allocated to the safety-critical tasks. Synchronization unit 23 is used to establish synchronous or asynchronous redundant computing copies on at least one other independent computing node for the safety-critical tasks assigned to edge computing nodes for execution. The startup unit 24 is used to start the corresponding fault recovery or task continuation mechanism according to the predefined strategy when a network connection abnormality or computing node failure is detected during task execution, so as to ensure the continuous or degraded reliable execution of computing tasks, especially the safety-critical tasks.

[0052] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0053] According to the embodiments of this disclosure, this disclosure also provides a computing task scheduling system for coal mines, corresponding to the computing task scheduling method and apparatus for coal mines described above. For details not disclosed in the system embodiments, please refer to the above-described method and apparatus embodiments, which will not be repeated in this disclosure.

[0054] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0055] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0056] like Figure 3As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 302 or a computer program loaded from storage unit 308 into RAM (Random Access Memory) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0057] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0058] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as a coal mine-oriented computing task scheduling method. For example, in some embodiments, the coal mine-oriented computing task scheduling method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, computing unit 301 may be configured to perform the aforementioned coal mine-oriented computing task scheduling method by any other suitable means (e.g., by means of firmware).

[0059] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0060] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0061] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0062] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0063] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0064] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0065] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0066] The various numerical designations such as "first," "second," etc., used in this disclosure are merely for ease of description and are not intended to limit the scope of the embodiments of this disclosure, nor do they indicate a sequential order.

[0067] At least one of the features described in this disclosure can also be described as one or more, and multiple features can be two, three, four or more, and this disclosure does not impose any limitations. In the embodiments of this disclosure, for a technical feature, the technical features in that technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no sequential order or size order among the technical features described by "first", "second", "third", "A", "B", "C" and "D".

[0068] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0069] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A coal mine-oriented computing task scheduling method, characterized in that, include: Based on the different timeliness requirements of computational tasks in coal mine safety monitoring, equipment control and production management, a multi-level task classification model is established to classify the computational tasks to be scheduled, including at least one type of safety-critical task with an upper limit on delay. Based on the task classification results, and combined with the real-time network topology status, link quality and computing node load information, various computing tasks are dynamically allocated to matching computing nodes in the hierarchical computing system for execution. Among them, for the safety-critical tasks, priority is given to allocating them to the task-generating source device or its nearby edge computing nodes, and communication resources with quality of service assurance are allocated to the safety-critical tasks. For the safety-critical tasks assigned to edge computing nodes for execution, synchronous or asynchronous redundant computing copies are established on at least one other independent computing node; When network connectivity issues or computing node failures are detected during task execution, corresponding fault recovery or task continuation mechanisms are initiated according to predefined strategies to ensure the continuous or degraded reliable execution of computing tasks, especially the safety-critical tasks.

2. The method of claim 1, wherein, The establishment of the multi-level task classification model includes: Based on the maximum allowed end-to-end processing latency, tasks are divided into multiple levels. Level 1 tasks correspond to safety-critical applications with processing latency requirements of less than 10 milliseconds. Level 2 tasks correspond to real-time interactive applications with processing latency requirements of 10 to 500 milliseconds. Level 3 tasks correspond to near real-time analytical applications with processing latency requirements of 500 milliseconds to 5 seconds. Level 4 tasks correspond to non-real-time computing applications with processing latency requirements of more than 5 seconds.

3. The method of claim 1, wherein, The process of dynamically allocating various computing tasks to matching computing nodes in the hierarchical computing system based on task classification results and combined with real-time network topology status, link quality, and computing node load information includes: Continuously monitor the available bandwidth, transmission latency, and data packet loss rate of each communication link in the hierarchical computing system, as well as the CPU utilization, memory usage, and input / output throughput of each computing node; Based on the latency requirements and monitoring information of the safety-critical task, a computing node that can meet the latency requirements is selected, and an end-to-end communication path with priority transmission guarantee is dynamically established or reserved.

4. The method according to claim 3, characterized in that, The allocation of communication resources with quality of service assurance for the safety-critical task includes: When the transmission quality of the current communication path serving the safety-critical task is detected to be lower than a preset threshold, the control plane function will quickly reroute the data stream of the task to a pre-planned or dynamically calculated backup communication path.

5. The method according to claim 1, characterized in that, The step of initiating the corresponding fault recovery or task continuation mechanism according to the predefined strategy includes: When a failure is detected in the primary edge computing node executing the safety-critical task, the task processing state is seamlessly switched to a backup computing node containing the redundant computing copy within less than 50 milliseconds, and the backup computing node takes over the execution.

6. The method according to claim 1, characterized in that, The step of initiating the corresponding fault recovery or task continuation mechanism according to the predefined strategy also includes: When an interruption is detected in the network connection between the terminal device and the computing node assigned to it, the terminal device is controlled to call its local preset lightweight computing model to continuously compute the safety-critical task and output the results based on simplified algorithms or historical data. After the network connection is restored, the computation logs and intermediate results generated locally during the interruption are uploaded to the backend node of the hierarchical computing system for complete data synchronization and high-precision result calibration.

7. The method according to claim 1, characterized in that, Also includes: In response to pre-defined events related to the process flow that occur in the coal mine production system, identify multiple logically related computational tasks triggered by the event; Multiple logically related computing tasks are collaboratively scheduled to be executed on the same edge computing node or a cluster of multiple edge computing nodes located in the same local area network.

8. A computational task scheduling device for coal mines, characterized in that, include: The classification unit is used to establish a multi-level task classification model based on the different timeliness requirements of computing tasks in coal mine safety monitoring, equipment control and production management, so as to classify the computing tasks to be scheduled, including at least one type of safety-critical task with an upper limit requirement for delay. The allocation unit is used to dynamically allocate various computing tasks to matching computing nodes in the hierarchical computing system based on the task classification results and in combination with the real-time network topology status, link quality and computing node load information. Among them, for the safety-critical tasks, priority is given to allocating them to the task-generating source device or its nearby edge computing nodes, and communication resources with quality of service assurance are allocated to the safety-critical tasks. A synchronization unit is used to establish synchronous or asynchronous redundant computing copies on at least one other independent computing node for the safety-critical tasks assigned to edge computing nodes for execution. The startup unit is used to initiate corresponding fault recovery or task succession mechanisms according to predefined strategies when a network connection anomaly or computing node failure is detected during task execution, so as to ensure the continuous or degraded reliable execution of computing tasks, especially the safety-critical tasks.

9. A computational task scheduling system for coal mines, characterized in that, The system includes: a computing task scheduling device for coal mines as described in claim 8.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

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