An intelligent scheduling method for explosion-proof fusion communication data for a complex mine environment

Abstract

The present application relates to cloud network fusion technical field, specifically for a kind of complex environment of mine-oriented explosion-proof fusion communication data intelligent scheduling method, including the following steps: obtaining the multi-source communication data message uploaded by underground explosion-proof terminal via explosion-proof base station in mine explosion-proof fusion communication network and the operating state of edge gateway, communication link and cloud server;Extract service type, node identification, timestamp, data length and underground area identification and form data stream scheduling feature set;Generate data stream scheduling factor, determine queue attribution, forwarding order, bandwidth occupation proportion and cloud edge processing location;According to link feedback information, update link state table, and switch transmission path when abnormal condition is satisfied.The present application is associated with scheduling by message feature, link state and cloud edge resource state, so that multi-source communication data forms queue allocation, path adjustment and fallback path when concurrent and link fluctuation.

Description

Technical Field

[0001] This invention relates to the field of cloud-network convergence technology, and in particular to an intelligent scheduling method for explosion-proof converged communication data in complex mining environments. Background Technology

[0002] The field of cloud-network convergence technology involves the collaborative bearing relationship between cloud computing resources, edge nodes, network transmission links, communication protocols, and business data streams. Communication processing typically revolves around terminal access, message encapsulation, link selection, queue scheduling, data forwarding, and platform storage. Among these, the traditional intelligent scheduling method for explosion-proof converged communication data in complex mine environments refers to the data communication process between underground explosion-proof terminals, explosion-proof base stations, edge gateways, switching equipment, cloud servers, and ground-based dispatch platforms. The underground terminal collects voice, video, environmental sensor data, and control command data to generate communication messages, which are then connected to the mine communication network via the explosion-proof base station. The edge gateway classifies, queues, allocates bandwidth, and forwards messages according to message type, source address, destination node, link occupancy status, and buffer queue length, and then transmits the corresponding data to the cloud server or ground-based dispatch platform.

[0003] Traditional mine integrated communication scheduling mainly relies on underground terminal message access and edge gateway forwarding. Queue division is mostly based on message type and buffer length, and bandwidth allocation is mostly based on link occupancy status. There is a lack of unified correlation between cloud server status and edge gateway load and underground area data. When voice and video environmental sensing and control commands are concurrent, queue backlog and link fluctuations on the same path will affect the queuing order of subsequent messages. Forwarding paths and cloud-edge processing positions lack a basis for linkage adjustment with feedback status. In abnormal states, the data flow processing path and the correspondence with real-time status are insufficient. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent scheduling method for explosion-proof converged communication data in complex mine environments. This method uses multi-source communication data, communication link status, edge gateway load, and cloud server status in the mine explosion-proof converged communication network as unified scheduling criteria. It establishes a mechanism for queue allocation, cloud-edge processing location selection, link feedback correction, and anomaly rollback handling for voice, video, environmental sensing, and control command data streams. This enables the converged communication data scheduling process in complex mine environments to correspond to business attributes, network status, and cloud-edge resource status.

[0005] To achieve the above objectives, this invention provides an intelligent scheduling method for explosion-proof integrated communication data in complex mine environments, comprising the following steps: acquiring multi-source communication data packets uploaded by underground explosion-proof terminals via explosion-proof base stations in a mine explosion-proof integrated communication network, and acquiring the operational status information of edge gateways, communication links, and cloud servers; extracting service type, source node identifier, target node identifier, timestamp, data length, and underground area identifier from the multi-source communication data packets, and associating the extraction results with the operational status information to form a data flow scheduling feature set; determining service urgency factor, link congestion factor, edge bearer factor, and cloud availability factor according to the data flow scheduling feature set, and combining them to generate data flow scheduling factors; and based on the data flow scheduling factors... The system determines the queue affiliation, forwarding order, bandwidth utilization ratio, and cloud-edge processing location for the corresponding data stream, including local forwarding, cloud upload for processing, or cloud upload after edge preprocessing. It receives link feedback information from explosion-proof base stations, switching equipment, or edge gateways and updates the link status table. Based on the updated link status table, it adjusts the queue level, bandwidth utilization ratio, and forwarding path of subsequent data streams. When the link feedback information meets any of the following conditions: link interruption, queue backlog exceeding preset backlog conditions, or cloud service unreachable, the corresponding data stream is switched to edge local caching, backup link forwarding, or a low-bitrate transmission path. When the link feedback information does not meet any of these conditions, the corresponding data stream is forwarded according to the adjusted queue level, bandwidth utilization ratio, and forwarding path.

[0006] Furthermore, when acquiring multi-source communication data packets, separate receiving buffers are established according to the source types of voice data packets, video data packets, environmental sensor data packets, and control command data packets. Within each receiving buffer, the reception time of the multi-source communication data packet, the identification of the access explosion-proof base station, and the packet integrity verification result are recorded. When the packet integrity verification result meets the preset integrity conditions, the corresponding multi-source communication data packet is written into the message set to be scheduled; when the packet integrity verification result does not meet the preset integrity conditions, the corresponding multi-source communication data packet is not written into the message set to be scheduled.

[0007] Furthermore, when acquiring operational status information, the edge gateway processing load, communication link occupancy rate, communication link latency, communication link packet loss rate, and cloud server response status are read respectively, and the collection time stamp is set according to the same time window as the multi-source communication data packets. When the collection time stamp of the operational status information falls within the reception time window of the multi-source communication data packets, the operational status information is associated with the set of packets to be scheduled; when the collection time stamp of the operational status information does not fall within the reception time window, the operational status information is reread.

[0008] Furthermore, when extracting the service type, source node identifier, target node identifier, timestamp, data length, and underground area identifier, the message header fields are first read to determine the source node identifier and target node identifier. Then, the message payload fields are read to determine the data length and service type. Finally, the underground area identifier and timestamp are determined based on the access explosion-proof base station identifier and the message timestamp. When all extraction results exist, the extraction results are written into the data flow scheduling feature set; when any extraction result is missing, the missing fields are supplemented according to the message header fields, the access explosion-proof base station identifier, and the preset node area mapping table.

[0009] Furthermore, when forming the data flow scheduling feature set, the corresponding time window is determined based on the timestamp, the corresponding communication link is determined based on the source node identifier and the target node identifier, and the corresponding edge gateway is determined based on the underground area identifier. When the collection time tag of the operation status information matches the corresponding time window, the edge gateway processing load, communication link occupancy rate, communication link latency, communication link packet loss rate, and cloud server response status are written into the data flow scheduling feature set; when the collection time tag of the operation status information does not match the corresponding time window, the corresponding extraction result is retained and the system waits for the next operation status information.

[0010] Furthermore, when determining the service urgency factor, link congestion factor, edge bearer factor, and cloud availability factor, the service urgency factor is determined based on the service type, control command identifier, and message latency requirements; the link congestion factor is determined based on the communication link occupancy rate, communication link latency, and communication link packet loss rate; the edge bearer factor is determined based on the edge gateway's processing load and the number of pending messages; and the cloud availability factor is determined based on the cloud server's response status and data reception confirmation results. When all four factors are present, the process proceeds to the data flow scheduling factor combination step; if any factor is missing, the corresponding data flow scheduling feature set is reread.

[0011] Furthermore, when generating data stream scheduling factors, the basic scheduling level of the data stream is first determined according to the business urgency factor. Then, the basic scheduling level is corrected on the transmission side according to the link congestion factor. Subsequently, the basic scheduling level corrected on the transmission side is corrected on the cloud-edge side according to the edge bearer factor and the cloud availability factor. When the basic scheduling level corrected on the cloud-edge side meets the queue switching conditions, the data stream scheduling factor of the corresponding data stream is output; when the basic scheduling level corrected on the cloud-edge side does not meet the queue switching conditions, the basic scheduling level corrected on the transmission side is retained as the data stream scheduling factor of the corresponding data stream.

[0012] Furthermore, when determining queue affiliation, control command data streams are written to the control command queue, voice data streams to the voice communication queue, environmental sensor data streams to the environmental monitoring queue, and video data streams to the video transmission queue. When the service urgency factor corresponding to the data stream scheduling factor is higher than the link congestion factor and the edge bearer factor, the forwarding order of the corresponding data stream in its respective queue is increased; when the link congestion factor or the edge bearer factor corresponding to the data stream scheduling factor meets the congestion condition, the bandwidth occupancy ratio of the corresponding data stream in its respective queue is reduced.

[0013] Furthermore, when determining the cloud-edge processing location, the selection is based on the target node identifier, the edge gateway processing load, and the cloud server response status. When the target node identifier corresponds to an underground node and the edge gateway processing load does not meet the load over-limit condition, local forwarding is selected; when the cloud server response status meets the reachability condition and the data stream needs to be stored on the platform, uploading to the cloud for processing is selected; when the edge gateway processing load does not meet the load over-limit condition and the cloud server response status meets the reachability condition, edge preprocessing followed by uploading to the cloud is selected.

[0014] Furthermore, upon receiving link feedback information, the system reads the link interruption status, queue backlog, and cloud service availability status, and updates the link status table according to the link feedback time. When the link feedback information meets the link interruption condition, the corresponding data stream is switched to the backup link for forwarding; when the link feedback information meets the queue backlog exceeding the preset backlog condition, the corresponding data stream is switched to the edge local cache; when the link feedback information meets the cloud service unreachable condition, the corresponding data stream is switched to a low bitrate transmission path.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by acquiring multi-source communication data packets and the operational status information of edge gateways, communication links, and cloud servers, and associating service type, node identifier, timestamp, data length, and underground area identifier as a data flow scheduling feature set, the scheduling basis simultaneously covers service attributes, network status, and cloud-edge resource status. By generating service urgency factors, link congestion factors, edge bearer factors, and cloud availability factors from the data flow scheduling feature set, and determining queue affiliation, forwarding order, bandwidth occupancy ratio, and cloud-edge processing location accordingly, the mine communication data flow has corresponding forwarding adjustment paths under link feedback and abnormal conditions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is the main flowchart of the intelligent scheduling of mine explosion-proof integrated communication data according to the present invention; Figure 2 This is a schematic diagram illustrating the multi-source communication data packet reception and integrity verification of the present invention; Figure 3 This is a schematic diagram illustrating the association of time windows for the operating status information of this invention; Figure 4 This is a schematic diagram of the data flow scheduling factor generation and queue mapping of the present invention; Figure 5 This is a schematic diagram of the link feedback correction and abnormal fallback path of the present invention. Detailed Implementation

[0018] The technical solutions described in this invention are explained below with reference to the accompanying drawings. Subsequent embodiments use the data scheduling process in a mine explosion-proof integrated communication network as an example. The underground explosion-proof terminals, explosion-proof base stations, edge gateways, communication links, switching equipment, cloud servers, and ground scheduling platforms involved are all used to illustrate the relationships of data acquisition, access, transmission, forwarding, feedback, and rollback processing in the complex environment of a mine. Unclaimed equipment topics are not extended to independent protection topics. Data objects appearing in subsequent embodiments include multi-source communication data packets, operating status information, data flow scheduling feature sets, data flow scheduling factors, link feedback information, and link status tables. The aforementioned names maintain a consistent meaning throughout the text. Component names with numerical labels have a consistent meaning with the corresponding components in the protection scope text, accompanying descriptions, and original drawings; component labels are not enclosed in parentheses, and the labels are only used to identify components and do not constitute quantity, parameter, order, or range limitations. In this embodiment, component labels are not set for abstract data, parameters, states, queues, steps, or processing results. S-class step numbers are only used to describe the method execution order.

[0019] Please see Figures 1 to 5This embodiment provides an intelligent scheduling method for explosion-proof fusion communication data in complex mine environments. This method is applicable to cloud-network convergence scenarios where underground explosion-proof terminals access the mine communication network via explosion-proof base stations, and data transmission is jointly participated in by edge gateways, communication links, cloud servers, and ground scheduling platforms. The method processes voice data packets, video data packets, environmental sensor data packets, and control command data packets. It incorporates packet fields, underground region origin, communication link status, edge gateway status, and cloud server status into the same scheduling process. Through feature construction, factor generation, queue mapping, cloud-edge processing location determination, feedback correction, and anomaly rollback, the corresponding data streams form executable scheduling paths.

[0020] S1: Acquire multi-source communication data packets uploaded by underground explosion-proof terminals via explosion-proof base stations in the mine explosion-proof integrated communication network. These multi-source communication data packets originate from voice data, video data, environmental sensor data, and control command data collected or generated by the underground explosion-proof terminals. The underground explosion-proof terminals encapsulate the corresponding data into packets, which are then accessed through the explosion-proof base station to form the mine communication network. When receiving multi-source communication data packets, the edge gateway establishes separate receiving buffers according to the source type of the voice data packets, video data packets, environmental sensor data packets, and control command data packets, ensuring that packets of different source types have corresponding buffer locations before entering the scheduling processing stage.

[0021] In S1, the receive buffer records the reception time, the access explosion-proof base station identifier, and the message integrity verification result of multi-source communication data packets. The reception time identifies the time and location when the multi-source communication data packet enters the edge gateway; the access explosion-proof base station identifier indicates the underground access location corresponding to the multi-source communication data packet; and the message integrity verification result indicates whether the message header and payload fields meet the integrity conditions for subsequent scheduling processing. When the message integrity verification result meets the preset integrity conditions, the edge gateway writes the corresponding multi-source communication data packet into the message set to be scheduled; when the message integrity verification result does not meet the preset integrity conditions, the corresponding multi-source communication data packet is not written into the message set to be scheduled. Therefore, the source of the message set to be scheduled for subsequent processing is clear, and the reception time and access location are retained.

[0022] S2 acquires the operational status information of the edge gateway, communication links, and cloud server. This operational status information includes edge gateway processing load, communication link occupancy, communication link latency, communication link packet loss rate, and cloud server response status. Edge gateway processing load characterizes the current carrying status of the set of messages to be received, buffered, or processed by the edge gateway; communication link occupancy, communication link latency, and communication link packet loss rate characterize the transmission status of the corresponding communication links in the mine communication network; and cloud server response status characterizes the reachability of the cloud server for data reception or service requests. The operational status information is collected by the edge gateway using the same time window as the multi-source communication data packets, with the time stamp set accordingly.

[0023] In S2, the edge gateway compares the collection timestamp of the operation status information with the reception time window of the multi-source communication data packets. When the collection timestamp of the operation status information falls within the reception time window of the multi-source communication data packets, the operation status information is associated with the set of packets to be scheduled; when the collection timestamp of the operation status information does not fall within the reception time window, the operation status information is reread. Through this time window constraint, multi-source communication data packets and operation status information form a correspondence within the same scheduling cycle, preventing different time states from being directly mixed into the same batch of scheduling criteria.

[0024] S3 extracts the service type, source node identifier, destination node identifier, timestamp, data length, and underground area identifier from the multi-source communication data packet. The edge gateway first reads the packet header fields to determine the source node identifier and destination node identifier, then reads the packet payload fields to determine the data length and service type, and finally determines the underground area identifier and timestamp based on the access explosion-proof base station identifier and the packet timestamp. The source node identifier is used to identify the sending node of the multi-source communication data packet, the destination node identifier is used to identify the receiving node of the multi-source communication data packet, the data length is used to identify the packet payload size, the service type is used to identify which category the corresponding data stream belongs to (voice, video, environmental sensing, or control commands), and the underground area identifier is used to identify the mine area source corresponding to the multi-source communication data packet.

[0025] In S3, when the service type, source node identifier, target node identifier, timestamp, data length, and underground area identifier are all present, the extraction results are written into the data flow scheduling feature set. When any extraction result is missing, the edge gateway supplements the missing fields according to the packet header fields, the access explosion-proof base station identifier, and the preset node area mapping table. The preset node area mapping table is used to map the access explosion-proof base station identifier and node identifier to the underground area identifier. The supplemented fields are still included in the data flow scheduling feature set and correspond to the reception time of the multi-source communication data packets. This process ensures that packets with missing fields can still form the basic fields required for scheduling within the existing traceable source range.

[0026] S4. The extracted results are correlated with the operational status information to form a data flow scheduling feature set. The edge gateway determines the corresponding time window based on the timestamp, the corresponding communication link based on the source node identifier and the target node identifier, and the corresponding edge gateway based on the underground area identifier. The corresponding time window is used to limit the time correspondence between the message and the operational status information, the corresponding communication link is used to limit the reference object of the link status information, and the corresponding edge gateway is used to limit the source object of the edge processing load. The data flow scheduling feature set is composed of message fields, time windows, communication links, edge gateway processing load, communication link occupancy rate, communication link latency, communication link packet loss rate, and cloud server response status.

[0027] In S4, when the collection timestamp of the operational status information matches the corresponding time window, the edge gateway's processing load, communication link occupancy rate, communication link latency, communication link packet loss rate, and cloud server response status are written into the data flow scheduling feature set. When the collection timestamp of the operational status information does not match the corresponding time window, the edge gateway retains the corresponding extraction results and waits for the next operational status information. Through this association processing, the data flow scheduling feature set can simultaneously reflect the message service attributes, message source location, transmission link status, edge bearer status, and cloud response status, so subsequent scheduling is no longer based solely on a single message type or a single buffer length for forwarding arrangements.

[0028] S5 determines the service urgency factor, link congestion factor, edge bearer factor, and cloud availability factor based on the data flow scheduling feature set. The service urgency factor is determined based on the service type, control command identifier, and message latency requirements. When multi-source communication data messages contain control command identifiers or the corresponding service type has timing processing requirements, the service urgency factor is used to record the basic scheduling level of the corresponding data flow in the queue. The link congestion factor is determined based on the communication link occupancy rate, communication link latency, and communication link packet loss rate. When the occupancy status, latency status, or packet loss status of the corresponding communication link reaches the congestion condition, the link congestion factor is used to perform transmission-side correction of the basic scheduling level.

[0029] In S5, the edge bearer factor is determined based on the edge gateway's processing load and the number of pending packets, characterizing the edge gateway's local bearer status for data flows within the current scheduling period. The cloud availability factor is determined based on the cloud server's response status and data reception confirmation results, characterizing whether the data flow is suitable for uploading to the cloud for processing. The data reception confirmation result can be returned by the cloud server regarding the data reception status, and the edge gateway determines the cloud availability factor based on the returned status. When the service urgency factor, link congestion factor, edge bearer factor, and cloud availability factor are all present, the data flow scheduling factor combination step is initiated; when the service urgency factor, link congestion factor, edge bearer factor, or cloud availability factor is missing, the corresponding data flow scheduling feature set is reread to avoid missing factors directly participating in the combination judgment.

[0030] S6, combining and generating data flow scheduling factors. The edge gateway first determines the basic scheduling level of the data flow according to the business urgency factor, then corrects the basic scheduling level on the transmission side according to the link congestion factor, and finally corrects the basic scheduling level on the cloud side according to the edge bearer factor and the cloud availability factor. The business urgency factor determines the initial scheduling direction of the data flow in the business dimension, the link congestion factor determines whether the basic scheduling level needs to be adjusted due to changes in transmission status, and the edge bearer factor and the cloud availability factor determine whether the data flow is suitable for processing on the edge side, uploading to the cloud for processing, or using edge preprocessing before uploading to the cloud.

[0031] In S6, when the cloud-edge corrected basic scheduling level meets the queue switching conditions, the corresponding data flow scheduling factor is output; when the cloud-edge corrected basic scheduling level does not meet the queue switching conditions, the transmission-side corrected basic scheduling level is retained as the corresponding data flow scheduling factor. The queue switching conditions are jointly limited by the combined state of the service urgency factor, link congestion factor, edge bearer factor, and cloud availability factor. The combined data flow scheduling factor serves as the input result for subsequent queue affiliation, forwarding order, bandwidth utilization ratio, and cloud-edge processing location selection.

[0032] S7. Based on the data flow scheduling factor, the queue affiliation, forwarding order, and bandwidth utilization ratio of the corresponding data flow are determined. The edge gateway writes control command data flows into the control command queue, voice data flows into the voice communication queue, environmental sensor data flows into the environmental monitoring queue, and video data flows into the video transmission queue. Different queues correspond to data flows of different source types, but the forwarding order and bandwidth utilization ratio within the queue are further determined by the data flow scheduling factor. When the service urgency factor corresponding to the data flow scheduling factor is higher than the link congestion factor and the edge bearer factor, the forwarding order of the corresponding data flow in its respective queue is increased.

[0033] In S7, when the link congestion factor or edge bearer factor corresponding to the data flow scheduling factor meets the congestion condition, the bandwidth occupancy ratio of the corresponding data flow in its respective queue is reduced. This bandwidth occupancy ratio adjustment does not change the source type of the data flow itself, but rather changes the proportion of communication resources occupied by the corresponding data flow within the same scheduling cycle, ensuring that queue resources correspond to link status and edge bearer status. Queue affiliation determines which queue a data flow enters, forwarding order determines the sequential position of different data flows within the same queue, and bandwidth occupancy ratio determines the resource occupancy status of the data flow in the transmission link.

[0034] S8 determines the cloud-edge processing location—local forwarding, uploading to the cloud for processing, or pre-processing at the edge before uploading—based on the data flow scheduling factor. The edge gateway selects the appropriate location based on the target node identifier, the edge gateway's processing load, and the cloud server's response status. When the target node identifier corresponds to an underground node and the edge gateway's processing load does not meet the load over-limit condition, local forwarding is selected, allowing the corresponding data flow to be forwarded at the edge. When the cloud server's response status meets the reachability condition and the data flow requires platform storage, uploading to the cloud for processing is selected, allowing the corresponding data flow to be received or stored on the cloud server.

[0035] In S8, when the edge gateway's processing load does not meet the load over-limit condition and the cloud server's response status meets the reachability condition, edge preprocessing is selected before uploading to the cloud. Edge preprocessing is used to complete packet organization, queue mapping, or pre-transmission processing related to data flow scheduling at the edge, and then upload the corresponding data flow to the cloud server. The above three cloud-edge processing locations belong to the processing branches under the same data flow scheduling factor. The distinction between local forwarding, cloud upload processing, and edge preprocessing followed by cloud upload is jointly determined by the target node identifier, edge gateway processing load, and cloud server response status, avoiding unconditionally selecting different processing locations in parallel.

[0036] S9 receives link feedback information from explosion-proof base stations, switching equipment, or edge gateways and updates the link status table. Link feedback information includes link interruption status, queue backlog, and cloud service availability status. The edge gateway updates the link status table according to the link feedback time. The link status table records the availability, congestion, and feedback status of communication links in the current or subsequent scheduling cycles. The updated link status table participates in the adjustment of queue levels, bandwidth utilization ratios, and forwarding paths for subsequent data flows, ensuring that the scheduling of subsequent data flows is based on feedback results from explosion-proof base stations, switching equipment, or edge gateways.

[0037] In S9, when link feedback information indicates that the communication link can continue to transmit and does not meet abnormal conditions, the edge gateway forwards the corresponding data stream according to the adjusted queue level, bandwidth utilization ratio, and forwarding path. When the link feedback information changes, the edge gateway does not directly use the forwarding path of the previous cycle. Instead, it uses the link feedback time as the update node to write the link interruption status, queue backlog, and cloud service availability status into the link status table. The updated link status table then participates in subsequent data stream scheduling. This feedback correction process creates a closed data flow relationship between the data stream scheduling factor, queue status, and forwarding path.

[0038] S10: When the link feedback information meets any of the following conditions: link interruption, queue backlog exceeding the preset backlog condition, or cloud service unreachable, the corresponding data stream will be switched to edge local caching, backup link forwarding, or a low bitrate transmission path. Specifically, when the link feedback information meets the link interruption condition, the corresponding data stream will be switched to backup link forwarding; when the link feedback information meets the queue backlog exceeding the preset backlog condition, the corresponding data stream will be switched to edge local caching; when the link feedback information meets the cloud service unreachable condition, the corresponding data stream will be switched to a low bitrate transmission path.

[0039] In S10, the edge local cache is used to temporarily store the corresponding data stream when there is queue backlog, the backup link forwarding is used to provide an alternative transmission path for the corresponding data stream when the link is interrupted, and the low bitrate transmission path is used to adjust the transmission mode of the corresponding data stream when the cloud service is unavailable. These fallback paths are correlated with the abnormal states in the link feedback information, with different abnormal states corresponding to different handling paths. When the link feedback information does not meet any of the conditions of link interruption, queue backlog exceeding the preset backlog condition, or cloud service unavailable, the edge gateway forwards the corresponding data stream according to the adjusted queue level, bandwidth utilization ratio, and forwarding path. Therefore, both normal scheduling paths and abnormal fallback paths are triggered or excluded by the link feedback information.

[0040] In this embodiment, S1 to S10 constitute a continuous scheduling processing chain. Multi-source communication data packets first undergo integrity verification and enter the set of packets to be scheduled. Then, they are associated with the operational status information within the same time window to form a data flow scheduling feature set. Subsequently, service urgency factors, link congestion factors, edge bearer factors, and cloud availability factors are generated and combined into a data flow scheduling factor. The data flow scheduling factor is used to determine queue affiliation, forwarding order, bandwidth utilization ratio, and cloud-edge processing location. Link feedback information is used to update the link status table and trigger subsequent path adjustments. This chain creates a continuous data closed loop between packet input, status association, scheduling output, and feedback correction.

[0041] In this embodiment, the service type, source node identifier, target node identifier, timestamp, data length, and underground area identifier are all derived from multi-source communication data packets, access explosion-proof base station identifiers, and pre-set node area mapping tables; the edge gateway processing load, communication link occupancy rate, communication link latency, communication link packet loss rate, and cloud server response status are all derived from operational status information; the link interruption status, queue backlog, and cloud service reachability status are all derived from link feedback information. The aforementioned data sources correspond one-to-one with the subsequent processing objects, avoiding situations with no source or no receiver. The data flow scheduling factor is not an independently existing abstract result, but rather is formed layer by layer from traceable fields and statuses in the data flow scheduling feature set, and continues to serve as input for queue mapping, forwarding control, and cloud-edge processing location selection.

[0042] In this embodiment, if a complete historical state has not yet been formed within the first scheduling cycle, the edge gateway performs scheduling based on the multi-source communication data packets, operational status information, and link feedback information already obtained in the current scheduling cycle. In subsequent scheduling cycles, the edge gateway adjusts the queue level, bandwidth occupancy ratio, and forwarding path of subsequent data streams according to the updated link status table. If the collection time stamp of the operational status information does not match the reception time window, the edge gateway retains the corresponding extraction result and waits for the next operational status information. If any factor is missing, the edge gateway rereads the corresponding data stream scheduling feature set. The aforementioned boundary processing is all performed within the scheduling process supported by the original disclosure, without introducing additional calculation results or parameters without a source.

[0043] In this embodiment, the preset integrity condition, congestion condition, queue switching condition, load overload condition, reachability condition, link interruption condition, and preset backlog condition are all used as state determination boundaries in the scheduling judgment. These conditions are used to limit whether multi-source communication data packets enter the set of packets to be scheduled, whether operational status information participates in association, whether data streams undergo queue switching, whether cloud-edge processing locations change, and whether corresponding data streams enter the fallback path. The specific values ​​or engineering configurations of the conditions can be determined within the scope of the original disclosure; this embodiment does not extend the undisclosed specific values. The results generated after each condition is triggered enter the subsequent scheduling process; if no condition is triggered, the existing processing path is maintained or the corresponding information is reread.

[0044] In this embodiment, voice data streams, video data streams, environmental sensing data streams, and control command data streams are carried through voice communication queues, video transmission queues, environmental monitoring queues, and control command queues, respectively. The queues are not independent devices or structures, but rather a resource organization method used by the edge gateway to queue data streams of different source types during scheduling. The forwarding order and bandwidth occupancy ratio of different queues are constrained by data stream scheduling factors. When the service urgency factor dominates the scheduling level, the forwarding order of the corresponding data stream in its respective queue is adjusted; when the link congestion factor or edge bearer factor meets the congestion condition, the bandwidth occupancy ratio of the corresponding data stream in its respective queue is adjusted. This process simultaneously reflects service attributes and communication resource status in the queue scheduling results.

[0045] In this embodiment, after the link feedback information is transmitted back, the edge gateway writes it into the link state table using the link feedback time as the update node. The updated result of the link state table is used for subsequent data flows, rather than just recording data flows that have already been forwarded. If the link feedback information corresponds to a link interruption condition, the backup link forwarding becomes the transmission path for the corresponding subsequent data flow; if the link feedback information corresponds to a queue backlog exceeding a preset backlog condition, the edge local cache becomes the temporary handling path for the corresponding subsequent data flow; if the link feedback information corresponds to a cloud service unreachable condition, the low bitrate transmission path becomes the adjustment path for the corresponding subsequent data flow. Through the above path correspondence, the feedback data can form subsequent scheduling actions, rather than remaining at the state recording level.

[0046] The specific processes, field states, processing order, judgment conditions, data sources, scheduling factors, queue relationships, cloud-edge processing locations, link status table update methods, and anomaly fallback paths described in the above embodiments are used to explain the feasible implementation methods of the technical solutions recorded in this invention, and should not limit this invention to the specific embodiments listed. Without departing from the technical solutions recorded in this invention and the scope supported by the original disclosure, any equivalent substitutions, equivalent modifications, equivalent combinations, order adjustments, equivalent transformations of field names, equivalent inheritance of the execution subject, or equivalent changes in the carrier form that can be conceived by those skilled in the art should fall within the scope of protection of this patent; however, they should not be extended to unclaimed topics, nor should the substantive correspondence of technical objects be changed through changes in reference numerals. Equivalent changes should not replace, delete, or weaken the method features, control relationships, data relationships, temporal relationships, parameter relationships, or physical action relationships necessary to achieve the technical effect.

[0047] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the technical solution.

Claims

1. A method for intelligent scheduling of explosion-proof fusion communication data in complex mine environments, characterized in that, Acquire multi-source communication data packets uploaded by underground explosion-proof terminals via explosion-proof base stations in the mine explosion-proof integrated communication network, and obtain the operating status information of edge gateways, communication links and cloud servers; The service type, source node identifier, target node identifier, timestamp, data length, and downhole area identifier are extracted from the multi-source communication data packets. The extraction results are then correlated with the operation status information to form a data flow scheduling feature set. Based on the data flow scheduling feature set, the service urgency factor, link congestion factor, edge bearer factor, and cloud availability factor are determined respectively, and combined to generate the data flow scheduling factor; The queue affiliation, forwarding order, bandwidth occupancy ratio, and cloud-edge processing location of the corresponding data stream are determined based on the data stream scheduling factor. Receive link feedback information from explosion-proof base stations, switching equipment or edge gateways and update the link status table. Adjust the queue level, bandwidth usage ratio and forwarding path of the data flow according to the updated link status table. When the link feedback information meets any of the following conditions: link interruption, queue backlog exceeding the preset backlog condition, or cloud service unreachable, the corresponding data stream will be switched to edge local cache, backup link forwarding, or low bit rate transmission path. When the link feedback information does not meet any of the conditions, the corresponding data stream will be forwarded according to the adjusted queue level, bandwidth occupancy ratio, and forwarding path.

2. The intelligent scheduling method for explosion-proof fusion communication data in complex mine environments according to claim 1, characterized in that, When acquiring multi-source communication data packets uploaded by underground explosion-proof terminals via explosion-proof base stations in the mine explosion-proof integrated communication network, receiving buffers are established according to the source types of voice data packets, video data packets, environmental sensor data packets, and control command data packets; The receiving buffer records the reception time of multi-source communication data packets, the identification of the access explosion-proof base station, and the packet integrity verification result. When the message integrity verification result meets the preset integrity conditions, the corresponding multi-source communication data message will be written into the message set to be scheduled. When the message integrity verification result does not meet the preset integrity conditions, the corresponding multi-source communication data message will not be written into the message set to be scheduled.

3. The intelligent scheduling method for explosion-proof fusion communication data in complex mine environments according to claim 2, characterized in that, When obtaining the operating status information of the edge gateway, communication link and cloud server, read the edge gateway processing load, communication link utilization, communication link latency, communication link packet loss rate and cloud server response status respectively; Set the collection time tag for the running status information according to the same time window as the multi-source communication data message; When the collection time tag of the running status information falls within the reception time window of the multi-source communication data packet, the running status information is associated with the set of packets to be scheduled. If the time stamp of the collected running status information does not fall within the receiving time window, the running status information is reread.

4. The intelligent scheduling method for explosion-proof fusion communication data in complex mine environments according to claim 1, characterized in that, When extracting the service type, source node identifier, target node identifier, timestamp, data length, and underground area identifier from the multi-source communication data packets, the packet header field is read first to determine the source node identifier and target node identifier, then the packet payload field is read to determine the data length and service type, and finally the underground area identifier and timestamp are determined based on the access explosion-proof base station identifier and the packet timestamp. When the business type, source node identifier, target node identifier, timestamp, data length, and downhole area identifier all exist, the extraction results will be written into the data flow scheduling feature set. If any extraction result is missing, the missing field is supplemented according to the message header field, the access explosion-proof base station identifier, and the preset node area mapping table.

5. The intelligent scheduling method for explosion-proof fusion communication data in complex mine environments according to claim 4, characterized in that, When the extraction results are associated with the running status information to form the data flow scheduling feature set, the corresponding time window is determined based on the timestamp, the corresponding communication link is determined based on the source node identifier and the target node identifier, and the corresponding edge gateway is determined based on the downhole area identifier. When the collection time tag of the operation status information matches the corresponding time window, the edge gateway processing load, communication link occupancy rate, communication link latency, communication link packet loss rate and cloud server response status are written into the data flow scheduling feature set. When the time stamp of the collected running status information does not match the corresponding time window, the corresponding extraction result is retained and the system waits for the next set of running status information.

6. The intelligent scheduling method for explosion-proof fusion communication data in complex mine environments according to claim 1, characterized in that, When determining the service urgency factor, link congestion factor, edge bearer factor, and cloud availability factor based on the data flow scheduling feature set, the service urgency factor is determined based on the service type, control command identifier, and message latency requirements; the link congestion factor is determined based on the communication link occupancy rate, communication link latency, and communication link packet loss rate; the edge bearer factor is determined based on the edge gateway processing load and the number of pending messages; and the cloud availability factor is determined based on the cloud server response status and data reception confirmation results. When the business urgency factor, the link congestion factor, the edge bearer factor, and the cloud availability factor all exist, proceed to the data flow scheduling factor combination step; When the business urgency factor, the link congestion factor, the edge bearer factor, or the cloud availability factor is missing, the corresponding data flow scheduling feature set is reread.

7. The intelligent scheduling method for explosion-proof fusion communication data in complex mine environments according to claim 6, characterized in that, When generating the data flow scheduling factors, the basic scheduling level of the data flow is first determined according to the business urgency factor, then the basic scheduling level is corrected on the transmission side according to the link congestion factor, and then the basic scheduling level after the transmission side correction is corrected on the cloud-edge side according to the edge bearer factor and the cloud availability factor. When the basic scheduling level corrected by the cloud edge meets the queue switching conditions, the data flow scheduling factor of the corresponding data flow is output. When the basic scheduling level corrected by the cloud-edge side does not meet the queue switching conditions, the basic scheduling level corrected by the transmission side is retained as the data flow scheduling factor for the corresponding data flow.

8. The intelligent scheduling method for explosion-proof fusion communication data in complex mine environments according to claim 1, characterized in that, When determining the queue affiliation of the corresponding data stream based on the data stream scheduling factor, the control instruction data stream is written to the control instruction queue, the voice data stream is written to the voice communication queue, the environmental sensing data stream is written to the environmental monitoring queue, and the video data stream is written to the video transmission queue. When the service urgency factor corresponding to the data flow scheduling factor is higher than the link congestion factor and the edge bearer factor, the forwarding order of the corresponding data flow in its queue is increased. When the link congestion factor or edge bearer factor corresponding to the data flow scheduling factor meets the congestion condition, the bandwidth occupancy ratio of the corresponding data flow in its queue is reduced.

9. The intelligent scheduling method for explosion-proof fusion communication data in complex mine environments according to claim 8, characterized in that, When determining the cloud-edge processing location—whether to forward locally, upload to the cloud for processing, or upload to the cloud after edge preprocessing—the selection is based on the target node identifier, the edge gateway processing load, and the cloud server response status. When the target node identifier corresponds to the underground node and the edge gateway's processing load does not meet the load over-limit condition, local forwarding is selected; When the cloud server response status meets the reachability conditions and the data stream needs to be stored on the platform, choose to upload to the cloud for processing; When the edge gateway's processing load does not meet the load over-limit condition and the cloud server's response status meets the reachability condition, select edge preprocessing before uploading to the cloud.

10. The intelligent scheduling method for explosion-proof fusion communication data in complex mine environments according to claim 1, characterized in that, When receiving link feedback information from explosion-proof base stations, switching equipment, or edge gateways, read the link interruption status, queue backlog, and cloud service availability status, and update the link status table according to the link feedback time. When the link feedback information meets the link interruption condition, the corresponding data stream will be switched to the backup link for forwarding; When the link feedback information satisfies the condition that the queue backlog exceeds the preset backlog condition, the corresponding data stream will be switched to the edge local cache; When the link feedback information meets the condition that the cloud service is unreachable, the corresponding data stream will be switched to a low bit rate transmission path.

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