Coal flow windscreen wiper multi-device cooperative control method and system

Abstract

The invention relates to the technical field of coal mine automation control, and discloses a coal flow windscreen wiper multi-device cooperative control method and system. The method comprises the following steps: obtaining an original environment data set of each sensor node in an underground environment, processing the original environment data set to obtain a communication quality index, calculating bandwidth demand change of each node in combination with historical transmission records of the nodes, and predicting a resource allocation proportion; when the current available network resources cannot meet the requirements, sorting the key data packets to obtain a priority transmission sequence; extracting high-priority data from the sequence, temporarily storing the high-priority data to a local cache unit, and determining the state of a buffer area; dynamically adjusting bandwidth allocation by adopting an intelligent optimization model, and updating communication resource configuration; and judging whether the resource configuration meets a load balance requirement, if so, sending a data packet through a wireless channel, determining a transmission performance index, and generating a resource management scheme of a next period. The cooperative work efficiency of multiple devices of the coal flow windscreen wiper in the high-dust and high-humidity environment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal mine automation control technology, and in particular to a coal flow wiper multi-device collaborative control method and system. BACKGROUND

[0002] In underground mine operations, the environmental conditions are extremely harsh, especially the high-dust and high-humidity environment poses great challenges to the operation and communication of mine equipment. The traditional communication system often faces problems such as wireless signal attenuation, insufficient bandwidth, high data loss rate, etc. in such an environment. These problems not only affect the normal collaborative operation of the equipment, but also may cause the equipment to respond in a timely manner, affecting the efficiency and safety of mine operations. Especially in the application scenario of coal flow wipers, the wiper equipment needs to obtain real-time information such as coal dust and humidity through sensors and perform automatic control based on these data. However, the quality of wireless communication in the underground environment is easily affected by factors such as dust concentration and humidity, leading to poor communication between devices, and thus affecting the collaborative work of the devices and real-time data transmission. Therefore, how to design an efficient, stable and intelligent communication resource management scheme in a high-dust and high-humidity environment to ensure the timely transmission of critical data has become a key to improving the efficiency of coal mine operations.

[0003] The existing communication system often relies on fixed bandwidth allocation and cannot dynamically adjust according to real-time network status, resulting in some devices being unable to communicate smoothly due to insufficient bandwidth. Moreover, during data transmission, the system often fails to give sufficient priority to critical data packets, which may lead to data loss or delay, affecting the timely response of the equipment.

[0004] Therefore, the multi-device collaborative control method of the present application solves the problems of insufficient resource allocation and priority management of traditional communication systems in high-dust and high-humidity environments by introducing intelligent optimization algorithms, dynamic bandwidth adjustment, and priority data packet transmission, thereby improving the efficiency of device collaborative work and the stability of communication, and has significant technical advantages and practical value. SUMMARY

[0005] To solve the above technical problems, the present application provides a coal flow wiper multi-device collaborative control method and system for improving the efficiency of coal flow wiper multi-device collaborative work in a high-dust and high-humidity environment.

[0006] In a first aspect, the present application provides a coal flow wiper multi-device collaborative control method, which comprises: Step S1: Obtain the original environmental data set of each sensor node deployed in the underground environment, process the original environmental data set to obtain the current communication quality index; Step S2: Based on the communication quality index, in combination with the historical transmission records of the nodes, a dynamic adjustment algorithm is used to calculate the bandwidth demand changes of each node, and the resource allocation ratio between nodes is predicted; Step S3: If the current available network resources cannot meet the resource allocation ratio, then the key data packets in the real-time monitoring information are sorted by priority queue to obtain a priority transmission sequence; Step S4: High-priority data is extracted from the priority transmission sequence, the high-priority data is temporarily stored by a local cache unit, and the transmission buffer state is determined; Step S5: According to the transmission buffer state, a smart optimization model is used to dynamically adjust the bandwidth allocation to obtain an optimized communication resource configuration; Step S6: Determine whether the communication resource configuration meets the inter-node load balancing requirement, if yes, send the data packets in the priority transmission sequence through the wireless channel, determine the transmission performance index by judging the transmission success rate of the data packets; Step S7: According to the transmission performance index, a resource scheduling scheme is generated, feedback information is obtained, parameters of the dynamic adjustment algorithm are updated, and a resource management scheme for the next period is generated.

[0007] In a second aspect, the application provides a coal flow wiper multi-device cooperative control system, which comprises: A monitoring module is configured to obtain an original environment data set of each sensor node deployed in a downhole environment, process the original environment data set, and obtain a current communication quality index; An allocation module is configured to calculate the bandwidth demand changes of each node based on the communication quality index, in combination with the historical transmission records of the nodes, and predict the resource allocation ratio between nodes by using a dynamic adjustment algorithm; A scheduling module is configured to sort the key data packets in the real-time monitoring information by priority queue to obtain a priority transmission sequence if the current available network resources cannot meet the resource allocation ratio; A cache module is configured to extract high-priority data from the priority transmission sequence, temporarily store the high-priority data by a local cache unit, and determine the transmission buffer state; An optimization module is configured to dynamically adjust the bandwidth allocation according to the transmission buffer state by using a smart optimization model to obtain an optimized communication resource configuration; An evaluation module is configured to determine whether the communication resource configuration meets the inter-node load balancing requirement, if yes, send the data packets in the priority transmission sequence through the wireless channel, determine the transmission performance index by judging the transmission success rate of the data packets; A feedback module is configured to generate a resource scheduling scheme according to the transmission performance index, acquire feedback information, update parameters of the dynamic adjustment algorithm, and generate a resource management scheme for a next period.

[0008] Compared with the prior art, the application has at least the following advantages: The application provides a coal flow wiper multi-device collaborative control method and system, which introduces multi-level data acquisition and intelligent analysis, and realizes efficient collaborative control of coal flow wiper devices in complex underground environments. First, through the real-time acquisition of temperature, humidity, dust concentration and other environmental data by the Internet of Things sensor array, combined with the high-dust and high-humidity environmental parameters obtained by infrared spectrum analysis, the environmental changes in the coal mine underground can be fully mastered; on this basis, through data preprocessing and noise suppression algorithm, the accuracy and reliability of the data are ensured; through the support vector machine algorithm, the environmental parameters are converted into communication quality grades, intelligent classification of communication quality is realized, and the state index of the communication system is calculated by a weighted summation algorithm. Based on the communication quality index, combined with the historical transmission records of the nodes, a dynamic adjustment algorithm is used to predict the bandwidth demand and adjust the resource allocation proportion of each node to optimize the utilization efficiency of network resources. When the network resources cannot meet the preset proportion, the system sorts the key data packets through a priority queue to ensure that important data can be transmitted preferentially. Through this mechanism, the transmission of key task data is effectively guaranteed under the condition of limited bandwidth, thereby improving the response capability of the system.

[0009] In addition, the application also dynamically adjusts the resource allocation proportion by real-time monitoring of network traffic fluctuations to ensure that the system can adapt to different working conditions and environmental changes. When the traffic fluctuation exceeds the preset range, the system can quickly adjust the bandwidth resource allocation proportion to avoid communication interruption caused by network instability and ensure the stability of data transmission. Finally, the application analyzes the communication performance index comprehensively, feeds back and optimizes the bandwidth allocation strategy, and forms an adaptive resource management mechanism; the system dynamically adjusts the resource configuration in each period to ensure efficient and stable device collaborative work in various complex environments, providing higher automation level and safety guarantee for coal mine operations. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings; Figure 1 The flowchart of the coal flow wiper multi-device collaborative control method in the embodiment of the application; Figure 2 A transmission delay test result schematic diagram of an embodiment of the present application; Figure 3 A structure schematic diagram of a coal flow wiper multi-device cooperative control system of an embodiment of the present application. DETAILED DESCRIPTION

[0011] The embodiment of the present application provides a coal flow wiper multi-device cooperative control method and system. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0012] For ease of understanding, the specific flow of the embodiment of the present application is described below. Please refer to Figure 1 A flow chart of a coal flow wiper multi-device cooperative control method in the embodiment of the present application, the method comprises: Step S1: Obtain the original environment data set of each sensor node deployed in the downhole environment, process the original environment data set, and obtain the current communication quality index.

[0013] In the step S1, the original environment data set includes the environment monitoring data and high-dust high-humidity environment parameters of each sensor node, the original environment data set is processed by smoothing and denoising to obtain a standard environment data set, the standard environment data set is processed by feature extraction and fusion to calculate the environment parameter characteristic value, and the interaction between the high-dust parameter and the high-humidity parameter is analyzed to obtain a feature data set; if the high-dust parameter or the high-humidity parameter in the feature data set exceeds a preset threshold, an abnormal detection mechanism is triggered to determine an abnormal environment state, the communication quality is classified by using a support vector machine algorithm according to the abnormal environment state to obtain a communication quality level; and the communication quality index of the communication system state is comprehensively calculated based on the communication quality level and the feature data set.

[0014] Specifically, the coal flow wiper device is a cleaning device used in coal mining operations, mainly for removing coal dust and other debris attached to the surface of coal mine equipment, machinery or transportation tools. In the coal mine operation environment, especially in underground operation, the dust concentration is extremely high, and the humidity is relatively high, which will affect the normal operation of the equipment. Therefore, the coal flow wiper device uses mechanical brushes or scrapers to clean the surface of the equipment in an automated manner, ensuring the effective operation and operating efficiency of the equipment. However, in the coal mine underground operation, due to the high dust and high humidity environment, the coal flow wiper device often faces problems such as unstable communication, insufficient bandwidth and data transmission delay, which will affect the cooperation between devices and cleaning efficiency. In order to ensure that the device can work efficiently and stably, the application first collects the original environmental data set through the Internet of Things sensor array deployed on the wall of the underground roadway and the surface of the equipment, including sensor data from multiple sources, as follows: temperature data: collected in real time by temperature sensors, used to monitor the temperature change of the underground environment, the temperature data can affect the operating efficiency of the equipment and the working state of the communication equipment; humidity data: collected in real time by humidity sensors, the humidity data reflects the humidity of the environment, higher humidity may cause signal attenuation of the equipment and communication equipment; dust concentration data: collected by dust concentration sensors (such as PM2.5 sensors), the dust concentration data reflects the pollution level of the underground, high dust concentration will cause equipment pollution, affect cleaning efficiency, and cause interference to communication link. In addition, the infrared spectrum analyzer is used to scan the underground air sample to obtain high dust and high humidity environment parameters, specifically dust particle deposition rate, which can directly reflect the potential impact of dust attachment on the communication antenna, the higher the deposition rate, the more dust on the surface of the communication equipment may cause signal attenuation and performance degradation. Then, the original data set containing noise is preprocessed to eliminate sensor instantaneous false alarms and environmental sudden disturbances, specifically using Kalman filter algorithm, which effectively suppresses data fluctuations through the iterative process of state prediction and measurement correction, for example, for temperature data, the algorithm can smooth the abnormal fluctuations caused by equipment heating or air flow disturbance, and output reliable standard environmental data set. Subsequently, the feature extraction and fusion stage is entered, and the key indicators that significantly affect the communication quality, i.e. environmental parameter characteristic values, are extracted from the purified data, and the sliding average values of temperature and humidity are calculated, and combined with the peak characteristics of dust concentration, to form the initial feature vector. To quantify the coupling effect of high dust and high humidity environment, the Pearson correlation coefficient is used to calculate the correlation between dust concentration and humidity, when a strong positive correlation is calculated, it indicates that the high humidity environment aggravates the attachment of dust, and this correlation coefficient is used as a new fusion feature to form the final feature data set.

[0015] If the high dust parameter or the high humidity parameter in the feature data set exceeds the preset threshold value, an abnormal detection mechanism is triggered immediately to determine an abnormal environment state. The core of the mechanism is a pre-trained support vector machine classification model. The model is based on a large amount of historical environment feature data and corresponding communication quality level labels (excellent, good, medium, and poor) marked by actual network performance indicators (such as throughput and bit error rate) for supervised training. During the training process, a sequence minimum optimization algorithm is used to solve a convex quadratic programming problem to find an optimal hyperplane that can maximize the classification interval. The model takes the real-time feature data set as input, maps the data to a high-dimensional feature space through its radial basis kernel function, and finds the optimal classification hyperplane. Finally, the communication quality level is output. In this way, complex environmental parameters are converted into explicit communication quality classification, providing a clear basis for subsequent decision-making, thereby optimizing the adaptability and reliability of the downhole communication system. Based on the classification result, the communication quality indicator of the communication system state, i.e., the network performance evaluation result, is calculated comprehensively. Specifically, the communication quality indicator is realized through a weighted summation algorithm: communication quality indicator = a x (1-temperature penalty) + b x (1-humidity attenuation) + g x signal-to-noise ratio normalized value + d x deposition compensation factor. The weight coefficients (a, b, g, d) are determined by principal component analysis of historical data to maximize the correlation between each environmental parameter and the measured communication performance. For example, a set of typical weight values is (0.3, 0.3, 0.25, 0.15). The algorithm finally obtains a quantitative communication quality indicator value. The communication quality indicator value takes into account temperature influence, humidity attenuation, signal-to-noise ratio indicator, and deposition compensation, reflecting the degree of environmental impact on the communication link. This step precisely quantifies the impact of harsh physical environments on communication quality levels and indicator values through multi-source environmental data fusion and intelligent analysis, realizes early perception and hierarchical warning of environmental interference, and provides hierarchical decision-making basis for the entire control system, ensuring the adaptability and reliability of the downhole communication system under different working conditions from the source.

[0016] Step S2: Based on the communication quality indicator, the historical transmission records of the nodes are combined, and a dynamic adjustment algorithm is used to calculate the bandwidth demand changes of each node to predict the resource allocation ratio between nodes.

[0017] The step S2 comprises: based on the historical transmission records of each node, extracting the bandwidth demand change trend through time series analysis to obtain a bandwidth demand sequence; using a moving average algorithm to smooth the bandwidth demand sequence to obtain an optimized bandwidth demand sequence; if the bandwidth demand of a node in the optimized bandwidth demand sequence exceeds a preset threshold, predicting the bandwidth demand of a future time window through a linear regression algorithm to obtain a predicted bandwidth demand, calculating the resource allocation ratio of each node, adjusting the network resource allocation strategy to obtain a resource allocation scheme; adjusting the resource allocation scheme in combination with the communication quality index, updating the bandwidth configuration parameters of the communication system to obtain optimized transmission parameters, and monitoring real-time network traffic, if the traffic fluctuation exceeds a preset range, dynamically adjusting the resource allocation ratio to obtain the final resource allocation ratio.

[0018] Specifically, to accurately predict the bandwidth demand of the nodes, first, the complete transmission records of each node in the past several operation cycles are called from the historical database, especially the key business data such as the wiper camera video stream data, the lens cleaning state instruction and the coal flow blockage alarm information, and the transmission records contain the measured peak and average throughput, communication delay distribution characteristics, data packet loss rate and other key performance indicators in each communication cycle. By performing time series analysis on these historical data with time stamps, a self-regressive integral moving average model is used for trend decomposition, which specifically includes: identifying the bandwidth usage peak mode of each node at the same time period every day, focusing on analyzing the bandwidth demand characteristics caused by the coordinated operation of the wipers during the coal flow peak period, detecting the load fluctuation law caused by the weekly work cycle, and extracting the burst traffic characteristics caused by the coordinated operation of the devices. This analysis process first eliminates data non-stationarity through difference operation, then determines the model order using the partial autocorrelation function, and finally establishes a bandwidth demand prediction model for each node. Based on this model, the historical data is fitted and reconstructed to generate an initial bandwidth demand sequence that conforms to the actual working law of the node, which fully reflects the bandwidth demand change trajectory that may occur in the same period in the future. To improve the reliability of the sequence data, the initial bandwidth demand sequence is subjected to smoothing and denoising processing, a moving average algorithm based on a sliding window is used, a time window covering multiple sampling periods is set, the periodic influence of the wiper start-stop cycle on the bandwidth demand is particularly considered, the arithmetic mean value of all bandwidth sampling values in the window is calculated, and the mean value is used to replace the original data at the center point of the window. This processing method can effectively filter out abnormal peaks caused by sensor false alarms and transient interference, while retaining the true change trend of the bandwidth demand. After iterative calculation, an optimized bandwidth demand sequence that accurately reflects the continuous load capacity of the node is output.

[0019] When the bandwidth demand of a specific node in the optimized sequence is detected to continuously exceed the safety threshold set according to its device type, especially when a certain wiper node needs to be frequently started for cleaning due to coal flow blockage, a prediction enhancement mechanism is immediately started. A linear regression model is constructed using the least squares method, with time as the independent variable and bandwidth demand as the dependent variable. The smoothed data of the last few cycles are used as the training set for model fitting. Through the calculated regression coefficient, the bandwidth demand extreme value of the node in the future specific time window is extrapolated and predicted, and this predicted bandwidth demand is used as a key input parameter for resource allocation. Based on the predicted demand results of all nodes, preliminary resource planning is performed, a linear programming model is established with the maximization of system throughput as the objective function and the total bandwidth not exceeding the physical upper limit as the core constraint condition, and weight coefficients reflecting the priority of node business are introduced, among which the transmission priority of coal flow blockage alarm data is the highest, followed by the wiper control instruction, and the conventional monitoring video stream is the last. The simplex method is used to solve the theoretical optimal resource allocation ratio of each node, and this process generates an initial resource allocation scheme that takes into account efficiency and fairness. In specific implementation, assuming that there are three nodes A, B, and C in the network, their predicted bandwidth demands are 8 Mbps, 12 Mbps, and 10 Mbps respectively, and the physical upper limit of the total network bandwidth is 25 Mbps. Among them, node A is responsible for transmitting coal flow blockage alarms in critical areas, node B transmits wiper control instructions, and node C transmits conventional monitoring videos. Different priority weight coefficients are allocated to them: the weight of node A carrying real-time monitoring video is 0.4, the weight of node B transmitting sensor data is 0.35, and the weight of node C responsible for device state monitoring is 0.25. The linear programming model is established as follows: objective function: max (0.4 × A + 0.35 × B + 0.25 × C), constraint condition: A + B + C ≤ 25, boundary condition: A ≤ 8, B ≤ 12, C ≤ 10. The optimal solution is obtained by solving this optimization problem by the simplex method: node A is allocated 8 Mbps, node B is allocated 10.2 Mbps, and node C is allocated 6.8 Mbps. This allocation scheme not only ensures that high-priority businesses obtain sufficient bandwidth, but also ensures that the total bandwidth utilization rate reaches 100%, while the allocation amount of each node does not exceed its predicted demand, forming a theoretical optimal resource allocation ratio. This allocation scheme serves as the initial resource allocation scheme, laying the foundation for subsequent dynamic adjustment based on real-time communication quality.

[0020] After obtaining the initial resource allocation scheme, dynamic calibration needs to be combined with the communication quality index. When the index shows that the channel is deteriorating, especially when the communication quality decreases in a high dust environment, the allocation amount is isometrically compressed according to the preset mapping relationship, for example, when the index is lower than 0.8, a compression coefficient of 0.9 is enabled, to ensure that the total bandwidth matches the current channel capacity. Based on the calibration result, the transmission parameters such as the physical layer modulation scheme and the data frame format are updated synchronously. During the execution process, the actual flow of each node is monitored through a sliding window. When it is detected that the node flow continuously deviates from the allocated value by more than 20%, redundant bandwidth is immediately allocated from the light load node to the overload node to realize real-time rebalancing of resources. The final allocation scheme not only meets the actual carrying capacity of the channel, but also responds to the dynamic changes of node demand in a timely manner.

[0021] Step S3: If the current available network resources cannot meet the resource allocation ratio, then the key data packets in the real-time monitoring information are sorted through the priority queue to obtain a priority transmission sequence.

[0022] Among them, step S3 includes: obtaining real-time data flow through network flow monitoring, analyzing the data packet identifier to identify key data packets containing real-time monitoring information; establishing a priority queue, inserting the key data packets into the queue according to the transmission priority, forming a priority transmission sequence, and the transmission priority is dynamically calculated and determined based on the real-time requirement and content importance of the data packet.

[0023] Specifically, in the downhole coal flow monitoring system, the current available network resources are monitored in real time through the following mechanism: first, a bandwidth detection agent is deployed at the network layer to send detection data packets to the sink node at a frequency of seconds, and by measuring the round-trip delay and throughput change of the data packets, the effective available bandwidth of the current channel is calculated; at the same time, the physical layer modem continuously reports the signal-to-noise ratio and bit error rate parameters, when the signal-to-noise ratio is lower than the preset threshold value, for example, 15dB, and the bit error rate exceeds the preset threshold value, for example, When the current available network resources cannot meet the resource allocation ratio determined in step S2 through the above monitoring means, the priority transmission guarantee mechanism is immediately started, which first captures real-time data streams through the network traffic monitoring unit, and performs deep analysis on the data packets in transmission; According to the preset data packet identification rule, the data packets containing key task information are accurately identified from the data stream, which specifically includes coal flow blockage alarm data such as identifier 0xA1, windshield wiper emergency control instruction such as identifier 0xB2, lens contamination degree overrun alarm such as identifier 0xC3, and other real-time monitoring information directly affecting safety production. On the basis of identifying the key data packets, a multi-level priority queue management mechanism is established. The calculation of transmission priority adopts a dynamic weight algorithm based on service rules, and its calculation model is: priority score = real-time coefficient × W_t + content importance coefficient × W_c + waiting time × W_d, wherein the real-time coefficient is a normalized value according to the reciprocal of the data packet survival period, the content importance coefficient is a fixed coefficient according to the data packet type (alarm / instruction / status), and the waiting time is an incremental function of the data packet residence time in the queue. The weight coefficients of the real-time coefficient W_t, the content importance coefficient W_c and the waiting time coefficient W_d are set according to the real-time requirement, the content importance and the residence time of the data packet. The real-time coefficient W_t gives priority to guarantee the transmission of emergency data such as alarm data, the content importance coefficient W_c is adjusted according to the data type such as instruction and state monitoring, and the waiting time coefficient W_d is dynamically adjusted according to the residence time of the data packet in the queue. Through system load and historical data feedback, these coefficients are adjusted in real time to ensure optimal allocation of resources and communication efficiency.

[0024] In the queue management process, the identified key data packets are arranged in descending order according to their calculated priority scores and inserted into the transmission queue. For example, when a coal flow blockage alarm (priority score 98) and lens contamination data (priority score 75) are detected at the same time, the blockage alarm data is automatically placed at the front of the queue; at the same time, the queue manager continuously monitors the waiting time of each data packet, and the priority of the data packet whose waiting time exceeds 200 ms is raised, effectively preventing the "starvation" phenomenon of low-priority data packet transmission. To adapt to the dynamic changes of the underground environment, an environment-adaptive priority adjustment mechanism is also established. When the environmental sensor detects that the dust concentration in a specific area exceeds the preset threshold, for example, 200 mg / m³, the transmission priority of all monitoring data in that area is automatically raised; when the coal flow speed exceeds the set safety threshold, the priority level of the speed monitoring data is correspondingly increased. This dynamic priority mechanism ensures that the transmission strategy can be intelligently adjusted under limited bandwidth resources, always prioritizing the transmission of key data for safety production. The final priority transmission sequence serves as the basis for data transmission scheduling, ensuring that critical data such as coal flow blockage alarms can be given priority for transmission in high-load or weak network environments, while recording the queue status and transmission success rate during periods of resource scarcity. These data will be fed back to the dynamic adjustment algorithm in step S2 to optimize future resource allocation prediction models.

[0025] In step S3, the method further comprises: real-time monitoring of network traffic fluctuations, triggering dynamic adjustment of the transmission sequence when traffic fluctuations exceed a preset range; using a weighted moving average algorithm to smooth the transmission demand of key data packets and recalculate the transmission priority of each data packet; based on the recalculated transmission priority, dynamically reordering the priority queue; updating the bandwidth resource allocation ratio based on the new queue structure, and reconfiguring the bandwidth parameters of the network nodes based on the updated resource allocation ratio, to finally generate the priority transmission sequence.

[0026] Specifically, the downhole network environment is complex and variable, and the initial priority setting may not adapt to the dynamic changes of the working conditions. Therefore, this step specially designs a dynamic adjustment mechanism for continuous optimization. When the network traffic fluctuation is monitored to be beyond the preset range, the urgency of each data packet is re-evaluated, and the transmission queue is always ensured to reflect the most real business demand through dynamic reordering. Specifically, during the operation of the priority transmission mechanism, the traffic sensor deployed on the communication link continuously monitors the network traffic fluctuation; specifically, the sliding time window statistical method is adopted to collect the instantaneous throughput data of each node at a period of 500 milliseconds, and when the node traffic fluctuation amplitude is detected to exceed the preset threshold value for three consecutive periods, the dynamic adjustment mechanism of the transmission sequence is triggered immediately. Specifically, first, the weighted moving average algorithm is used to smooth the transmission demand of the key data packet, the algorithm sets a sliding window with a length of 5 sampling periods, and gives higher weight to recent data. Through weighted calculation, the instantaneous peak value caused by sudden traffic is eliminated. For example, when the wiper node generates alarm data surge due to sudden coal flow blockage, this algorithm can effectively smooth its transmission demand curve, avoiding frequent queue rearrangement caused by single peak value. On the basis of completing the demand smoothing, the transmission priority of each data packet is recalculated. The new priority score considers three dimensions of smoothed transmission demand, data packet waiting time and real-time channel condition, and the specific calculation formula is updated as: priority score = basic business weight x 0.5 + waiting time factor x 0.3 + channel quality compensation x 0.2, wherein the waiting time factor is based on the residence time of the data packet in the queue and increases linearly, and the channel quality compensation is dynamically adjusted according to the real-time signal-to-noise ratio.

[0027] Based on the priority score recalculated, the priority queue is dynamically reordered, and the reordering process is realized by using the minimum heap data structure to ensure that the time complexity is maintained at the preset level, and the queue update can be quickly completed even in a high concurrency scenario. For example, when a coal flow blockage alarm data packet has its priority increased due to prolonged waiting time, its position in the queue is adjusted forward to ensure that it gets an earlier transmission opportunity. According to the queue structure after reordering, the bandwidth resources are allocated in turn according to the priority from high to low, and the new bandwidth resource allocation ratio is generated. For example, in a specific implementation, 60% of the total bandwidth is reserved for the top 20% of data packets with the highest priority, 35% of the bandwidth is allocated for the middle 50% of data packets, and the remaining 5% of the bandwidth is used to guarantee the basic transmission demand of the data with the lowest priority. Finally, according to the updated resource allocation ratio, the software-defined network controller issues new flow table rules to each network node to dynamically adjust its bandwidth parameters; at the same time, the relevant parameters of this adjustment, including traffic fluctuation characteristics, queue reordering results, bandwidth allocation efficiency and other data, are recorded to the historical database to provide training samples for subsequent machine learning model optimization, forming a continuous improvement closed-loop control mechanism.

[0028] Step S4: extracting high-priority data from the priority transmission sequence, temporarily storing the high-priority data through a local cache unit, and determining a transmission buffer state.

[0029] In step S4, high-priority data packets are extracted from the priority transmission sequence through packet identification analysis to determine a high-priority data packet set. The high-priority data packets are stored in the local cache unit, and a cache allocation algorithm is used to determine the storage location of the cache unit. The occupancy state of the local cache unit is analyzed to obtain the available capacity of the cache unit and determine the transmission buffer configuration. If the available capacity of the transmission buffer configuration is lower than a preset threshold, a cache cleaning mechanism is used to release low-priority data packets to obtain an updated cache configuration.

[0030] Specifically, after the priority sorting of the transmission sequence is completed, the cache guarantee mechanism of high-priority data is immediately executed. First, the data packet parsing engine performs identifier deep parsing on each data packet in the priority transmission sequence, and according to the preset high-priority identifier rule, including but not limited to: coal flow blockage alarm identifier 0xA1, device emergency stop instruction identifier 0xB2, safety system linkage signal identifier 0xC3, the high-priority data packet set is accurately extracted from the transmission sequence. In the data storage stage, the local cache unit adopts a cache allocation algorithm based on memory partitioning, which divides the cache area into three logical regions: an emergency data area (occupying 40% of the total capacity), a guarantee data area (occupying 40% of the total capacity), and a dynamic adjustment area (occupying 20% of the total capacity). According to the priority score of the data packet, data packets of different levels are stored in the corresponding area. Specifically, emergency alarm data with a priority score greater than 90 is stored in the emergency data area, important control instructions with a priority score between 70 and 90 are stored in the guarantee data area, and the remaining data is flexibly stored in the dynamic adjustment area according to real-time cache pressure. In the cache state monitoring link, the cache management module periodically scans the occupancy of each storage area, and the monitoring indicators include: the current data volume of each partition, the storage fragmentation degree, the data survival time distribution, etc. The transmission buffer configuration state is calculated by integrating these indicators, including real-time available capacity, expected releasable space, storage efficiency score, and other key parameters. When it is detected that the available capacity in the transmission buffer configuration is lower than the preset safety threshold, such as 15% of the total capacity, the hierarchical cache cleaning mechanism is immediately started. This mechanism releases data according to the "least recently used" principle and in combination with data priority: first, it cleans the data packets with the lowest priority and the longest survival time in the dynamic adjustment area; if the capacity requirement is still not met, it continues to clean non-critical data in the guarantee data area; the contents of the emergency data area are only partially cleaned in time order in extreme cases. Through this hierarchical cleaning strategy, the storage space is released while the high-value data is maximally preserved. After the cleaning operation is completed, the capacity allocation of each storage area is recalculated, and the updated cache configuration is generated. The new configuration not only reflects the current storage state, but also contains the capacity demand trend predicted based on historical data, providing decision support for subsequent data scheduling. The entire process forms a complete cache management closed loop from data extraction, intelligent storage to state monitoring, capacity guarantee, ensuring that critical data can be properly stored under high load conditions, providing reliable guarantee for the final data transmission.

[0031] Step S5: According to the state of the transmission buffer, an intelligent optimization model is used to dynamically adjust the bandwidth allocation, and an optimized communication resource configuration is obtained.

[0032] The step S5 comprises: analyzing the transmission buffer state, obtaining the current bandwidth occupation ratio, and determining the initial configuration of bandwidth allocation; extracting the bandwidth usage mode from the historical data according to the initial configuration, training by using the reinforcement learning model, and obtaining the optimized allocation strategy; if the bandwidth occupation ratio of the allocation strategy exceeds the preset threshold, re-allocating the bandwidth resources by the dynamic adjustment mechanism, and generating the updated allocation scheme; analyzing the load state of the communication resources according to the updated allocation scheme, and determining the priority order of the resource configuration; adjusting the bandwidth scheduling parameters of the network nodes by the priority order, and obtaining the optimized scheduling configuration; and updating the resource allocation of the transmission buffer according to the scheduling configuration, and generating the optimized communication resource configuration.

[0033] Specifically, by monitoring the key indicators such as the data backlog rate, cache queue depth and data packet survival time of the transmission buffer in real time, the transmission buffer state is comprehensively analyzed. When it is detected that the backlog rate of the high-priority data area exceeds the preset threshold, for example, 30%, or the average queue depth continues to grow, it is determined that the current bandwidth configuration cannot meet the transmission demand. Based on this state analysis, firstly, the bandwidth occupation ratio of each node is obtained to form the initial configuration benchmark of bandwidth allocation. After obtaining the initial configuration, the bandwidth usage mode under similar working conditions is extracted from the historical operation and maintenance database, including the inter-node traffic distribution characteristics, service load cycle law and channel quality change mode; these historical data are input into the reinforcement learning model based on deep Q network (DQN) as training samples for training. In the model, the state space is defined as the current transmission buffer state (such as backlog rate, queue depth) and the historical bandwidth allocation sequence of each node; the action space is defined as the adjustment vector of the bandwidth allocation ratio of each node; and the reward function is designed by comprehensively considering the total throughput (positive reward) and transmission delay (negative reward) of the system. The model learns by constantly interacting with the environment, and finally outputs the optimal bandwidth allocation strategy for the current buffer state. When the bandwidth occupation ratio of any node in the allocation strategy output by the reinforcement learning model exceeds the preset threshold of the physical link capacity, for example, 85%, the dynamic adjustment mechanism is triggered immediately. The mechanism uses the proportional-integral-derivative control principle to calculate the required bandwidth adjustment amount according to the deviation between the real-time bandwidth utilization rate and the target threshold, and quickly reallocates the bandwidth resources among the nodes to generate an updated allocation scheme. Based on the updated allocation scheme, the real-time load state of the communication resources is analyzed by the load balancer. By calculating the load balancing index of each communication link, the bottleneck nodes with excessive load and the idle nodes with insufficient utilization are identified. The calculation of the load balancing index is based on the Jain's fairness index formula, and the calculation formula is: wherein, is the bandwidth utilization rate of node i, is the total number of nodes, the value range of is [ , 1], the closer the value to 1 indicates the more balanced the load, and the load distribution condition is quantified by the dispersion degree of the bandwidth utilization of all nodes in the statistical period. Specifically, first, the real-time bandwidth utilization, packet discard rate and queue delay of each node are collected in a period of 5 seconds, and then the load balance degree is calculated by substituting the utilization of each node into the formula, and when the index is lower than the preset threshold, for example, 0.8, it is determined that the system is in an unbalanced load state; on this basis, the abnormal nodes are identified through a multi-condition joint judgment mechanism: for the nodes whose bandwidth utilization rate is continuously higher than 85%, and accompanied by a packet discard rate greater than 5% or a queue delay exceeding 100ms, are marked as bottleneck nodes with excessive load; on the contrary, for the nodes whose bandwidth utilization rate is continuously lower than 30%, the packet discard rate is close to zero, and the available cache capacity is more than 70%, they are marked as idle nodes with insufficient utilization.

[0034] Subsequently, according to the comprehensive analysis of the identified node state and service priority, the resource allocation strategy is formulated, and the core principle is to allocate redundant bandwidth from idle nodes or lightly loaded nodes to prioritize the transmission requirements of high-priority services in bottleneck nodes. According to the principle of "guaranteeing key and balancing load", the priority order of resource allocation is determined: the coal flow jam alarm link enjoys the highest priority, the wiper control link is followed, and the state monitoring link follows the principle of balanced allocation. According to this priority order, the bandwidth scheduling parameters of each network node are dynamically adjusted through the software-defined network controller. Specifically, it includes modifying the bandwidth limit parameters of the switch flow table, adjusting the weight configuration of the priority queue, and updating the burst capacity setting of the traffic shaper. These parameter adjustments form an optimized scheduling configuration to ensure that high-priority services obtain sufficient bandwidth protection. Finally, according to the optimized scheduling configuration, the resource allocation strategy of the transmission buffer is updated, including reallocating the cache space proportion of each priority queue, adjusting the out-of-queue rate limit of data packets, and setting differential retransmission mechanism parameters. Through this series of coordinated and consistent optimization measures, a set of optimized communication resource configuration highly matched with the current network state and service demand is finally generated, so as to guarantee the quality of service of key services while improving the overall network resource utilization efficiency.

[0035] Step S6: Determine whether the communication resource configuration meets the requirement of load balance between nodes, if yes, send the data packets in the priority transmission sequence through the wireless channel, and determine the transmission success rate of the data packets to determine the transmission performance index.

[0036] The step S6 comprises: obtaining channel state information through a wireless channel to determine the transmission capacity of the current channel; extracting the transmission priority of the data packet from the priority sequence according to the transmission capacity to generate an initial transmission queue; if the network load of the initial transmission queue exceeds a preset threshold, adjusting the transmission order of the data packet through a resource scheduling mechanism to obtain an optimized transmission queue, and obtaining the transmission success rate and network delay of each data packet according to the optimized transmission queue to determine the transmission performance index.

[0037] Specifically, after obtaining the optimized communication resource configuration, the load balancing verification and data transmission are performed through the following mechanism: first, the wireless communication module periodically obtains channel state information from the physical layer, including signal-to-noise ratio, multipath fading characteristics and interference level, and calculates the theoretical transmission capacity of the current channel based on the Shannon formula; then, the real-time channel capacity calculated is compared with the preset inter-node load balancing requirement, which specifies the matching degree of the bandwidth allocation ratio and the service importance of each node, and the maximum load deviation should not exceed 20% of the average load. When it is confirmed that the current configuration meets the load balancing requirement, the data transmission process is immediately started. The data scheduler dynamically extracts data packets from the priority transmission sequence according to the real-time channel capacity, and generates an initial transmission queue by comprehensively considering the transmission priority, survival time and data size of the data packet. In this process, the weighted round-robin algorithm is used to realize the balance of transmission opportunities by assigning different weight values to data packets of different priorities. Specifically, the highest weight is assigned to the coal flow jam alarm data packet, the medium weight is assigned to the wiper control instruction, and the basic weight is assigned to the state monitoring data. In the round-robin scheduling, high-weight data packets will get more transmission time slots, so as to ensure the full transmission of high-priority services while taking into account the basic communication needs of low-priority services. In the queue execution phase, the actual network load of the initial transmission queue is continuously monitored, and when it is detected that the queue load exceeds a preset threshold, such as 85% of the channel capacity, a resource scheduling mechanism is immediately triggered, which optimizes and recombines the transmission order by dynamically adjusting the transmission timing and burst flow control parameters of the data packet: the large-size data packet is transmitted in fragments, and the emergency small data packet is inserted into the transmission gap, finally forming an optimized transmission queue that can maximize the use of channel resources. Based on the optimized transmission queue, the data packet is sent through the wireless channel, and performance monitoring is simultaneously started. The receiving end calculates the transmission success rate of each priority data packet by calculating the ratio of the number of successfully received data packets to the total number of data packets within a period, and calculates the end-to-end network delay by recording the time stamp, especially focusing on the delay distribution of high-priority data packets. Finally, these indicators are aggregated into a comprehensive transmission performance index, which not only reflects the overall transmission efficiency, but also reflects the quality of service difference of different priority services, providing a quantitative basis for subsequent system optimization. To verify the effectiveness of the method in transmission delay control, a comparative test was conducted in a typical high-dust and high-humidity underground environment, and the test results are as follows:Figure 2 As shown in the figure, the figure shows the transmission delay cumulative probability distribution under the three schemes of the dynamic optimization method, static priority scheduling mechanism and non-priority mechanism of the application. It can be seen from the figure that the transmission delay of the dynamic optimization method proposed in the application is 31.5 ms at 95%, and 52.5 ms at 99%. It is significantly better than the comparative scheme. Especially at high percentile (such as 99%), the delay control of the application is more stable, and the maximum delay is not more than 84.0 ms. This shows that through dynamic resource adjustment and priority queue management, the transmission delay of critical data is effectively reduced, the real-time performance and reliability of the system in harsh environment are improved, and strong communication guarantee is provided for the multi-device cooperative control of the coal flow wiper.

[0038] Step S7: generating a resource scheduling scheme according to the transmission performance index, obtaining feedback information, updating the parameters of the dynamic adjustment algorithm, and generating a resource management scheme for the next period.

[0039] Among them, step S7 includes: based on the transmission performance index, generating a load balanced resource scheduling scheme by analyzing the network load distribution state; adjusting the resource allocation parameters of the wireless channel according to the resource scheduling scheme, obtaining the optimized communication resource configuration, and updating the transmission path of the priority transmission sequence; extracting the transmission success rate and network delay data from the network response to construct a feedback information dataset, using a support vector machine algorithm to optimize the parameters of the dynamic adjustment algorithm, and obtaining an updated parameter set; when the updated parameter set meets the preset load balancing condition, adjusting the priority order of the transmission queue according to the parameter set, and generating a new transmission queue; based on the new transmission queue, obtaining real-time channel state information, determining the corresponding channel resource allocation scheme, and adjusting the sending order of the data packet; generating the resource management configuration for the next period according to the adjusted sending order.

[0040] Specifically, based on the transmission performance index to start the resource scheduling scheme generation process, first of all, by analyzing the distribution state of the network load of each node, the communication link with uneven load is identified, and the resource scheduling scheme with load balancing is generated according to the importance of node business. In specific implementation, according to the priority difference of the alarm link, the wiper control link and the state monitoring link, the weighted minimum connection number algorithm is used to dynamically allocate bandwidth resources, wherein the alarm link obtains a guarantee allocation of not less than 40% of the total bandwidth. According to the generated resource scheduling scheme, the resource allocation parameters of the wireless channel are adjusted through the software defined network controller, including modifying the physical resource block allocation strategy, adjusting the modulation and coding scheme level and updating the power control parameter, so as to obtain the optimized communication resource configuration. At the same time, according to the real-time network topology, the transmission path of the priority transmission sequence is updated, and the multi-path transmission technology is used to disperse the high-priority data packets to multiple available links for transmission, so as to improve the transmission reliability. Key performance data is continuously extracted from the network response, including the transmission success rate, end-to-end delay, jitter and other indicators of each priority data packet, a feedback information data set containing time sequence characteristics is constructed, which is input into the parameter optimization module based on support vector machine. Through the kernel function, the data is mapped to a high-dimensional feature space, and the optimal classification hyperplane is found, according to which the key parameters of the dynamic adjustment algorithm, such as bandwidth prediction coefficient, queue management parameter, etc. are optimized to obtain an updated parameter set.

[0041] When the updated parameter set satisfies the preset load balancing condition, such as the load variance of each node being less than 0.1 and the resource utilization rate being maintained in the interval of 65%-85%, the priority order of the transmission queue is immediately adjusted according to the optimized parameter set. Specifically, it includes recalculating the urgency score of the data packet, dynamically updating the priority weight, and generating a new transmission queue that matches the current network state. Based on the newly generated transmission queue, the real-time channel quality index is obtained through the channel state information reference signal, and the corresponding channel resource allocation scheme is determined by combining the channel capacity prediction model. According to this, the data packet scheduler adjusts the sending order, and adopts the weighted fair queue scheduling algorithm based on priority to ensure high-priority services while avoiding the "starvation" phenomenon of low-priority services. Finally, according to the optimized sending order, the resource management configuration of the next period is generated, which includes the updated bandwidth allocation table, queue scheduling parameters, routing strategy and other key parameters. At the same time, the running data of this period is archived to the historical database for subsequent model training and algorithm optimization, forming a complete self-optimization closed loop. Through continuous learning and adjustment, the coal flow wiper system can maintain high-efficiency collaborative control capability in complex underground environment.

[0042] The above describes a coal flow wiper multi-device collaborative control method in an embodiment of the present application, and the following describes a coal flow wiper multi-device collaborative control system in an embodiment of the present application. Please refer toFigure 3 An embodiment of a coal flow wiper multi-device cooperative control system in the application includes: A monitoring module is configured to acquire an original environment data set of each sensor node deployed in a downhole environment, process the original environment data set, and obtain a current communication quality index.

[0043] A distribution module is configured to calculate a bandwidth demand change of each node by using a dynamic adjustment algorithm based on the communication quality index and in combination with a historical transmission record of the node, and predict a resource allocation ratio between the nodes.

[0044] A scheduling module is configured to sort key data packets in real-time monitoring information by using a priority queue to obtain a priority transmission sequence if the current available network resources cannot meet the resource allocation ratio.

[0045] A cache module is configured to extract high-priority data from the priority transmission sequence, temporarily store the high-priority data by using a local cache unit, and determine a transmission buffer state.

[0046] An optimization module is configured to dynamically adjust bandwidth allocation by using an intelligent optimization model according to the transmission buffer state, and obtain an optimized communication resource configuration.

[0047] An evaluation module is configured to determine whether the communication resource configuration meets a load balance requirement between the nodes, send data packets in the priority transmission sequence through a wireless channel if the communication resource configuration meets the load balance requirement, determine a transmission performance index by judging a transmission success rate of the data packets, and determine a transmission performance index.

[0048] A feedback module is configured to generate a resource scheduling scheme according to the transmission performance index, acquire feedback information, update parameters of the dynamic adjustment algorithm, and generate a resource management scheme for a next period.

[0049] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the system, the system and the unit described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0050] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0051] The above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some of the technical features; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A coal flow wiper multi-device cooperative control method, characterized in that, The method comprises: Step S1: obtaining an original environment data set of each sensor node deployed in a downhole environment, processing the original environment data set to obtain a current communication quality index; Step S2: based on the communication quality index, combining the historical transmission records of the nodes, using a dynamic adjustment algorithm to calculate the bandwidth demand change of each node, and predicting the resource allocation ratio between the nodes; Step S3: if the current available network resources cannot meet the resource allocation ratio, then sorting the key data packets in the real-time monitoring information through a priority queue to obtain a priority transmission sequence; Step S4: extracting high-priority data from the priority transmission sequence, temporarily storing the high-priority data through a local cache unit, and determining a transmission buffer state; Step S5: according to the transmission buffer state, using an intelligent optimization model to dynamically adjust the bandwidth allocation to obtain an optimized communication resource configuration; Step S6: judging whether the communication resource configuration meets the inter-node load balancing requirement, if yes, sending the data packets in the priority transmission sequence through a wireless channel, judging the transmission success rate of the data packets, and determining a transmission performance index; Step S7: according to the transmission performance index, generating a resource scheduling scheme, obtaining feedback information, updating the parameters of the dynamic adjustment algorithm, and generating a resource management scheme for the next period.

2. The method of claim 1, wherein, The step S1 comprises: The original environment data set comprises environment monitoring data and high-dust and high-humidity environment parameters of each sensor node, the original environment data set is subjected to smoothing and denoising processing to obtain a standard environment data set, the standard environment data set is subjected to feature extraction and fusion, environment parameter characteristic values are calculated, and the interaction between high-dust parameters and high-humidity parameters is analyzed to obtain a feature data set; If the high-dust parameters or the high-humidity parameters in the feature data set exceed a preset threshold, an abnormal detection mechanism is triggered to determine an abnormal environment state, a support vector machine algorithm is used to classify the communication quality according to the abnormal environment state to obtain a communication quality level; and based on the communication quality level and the feature data set, the communication quality index of the communication system state is comprehensively calculated.

3. The method of claim 1, wherein, The step S2 comprises: Based on the historical transmission records of each node, the bandwidth demand change trend is extracted through time series analysis to obtain a bandwidth demand sequence; the bandwidth demand sequence is subjected to smoothing processing by using a moving average algorithm to obtain an optimized bandwidth demand sequence; If the bandwidth demand of a node in the optimized bandwidth demand sequence exceeds a preset threshold, the bandwidth demand of a future time window is predicted by using a linear regression algorithm to obtain a predicted bandwidth demand, the resource allocation ratio of each node is calculated, the network resource allocation strategy is adjusted to obtain a resource allocation scheme; The resource allocation scheme is adjusted in combination with the communication quality index, the bandwidth configuration parameters of the communication system are updated to obtain optimized transmission parameters, and real-time network traffic is monitored; if the traffic fluctuation exceeds a preset range, the resource allocation ratio is dynamically adjusted to obtain a final resource allocation ratio.

4. The method of claim 1, wherein, The step S3 comprises: Real-time data stream is obtained through network traffic monitoring, and key data packets containing real-time monitoring information are identified by analyzing packet identification; a priority queue is established, the key data packets are inserted into the queue according to transmission priority to form a priority transmission sequence, and the transmission priority is dynamically calculated and determined based on the real-time requirement and content importance of the data packets.

5. The method of claim 4, wherein, The step S3 further comprises: Real-time network traffic fluctuation is monitored, and when the detected traffic fluctuation exceeds a preset range, dynamic adjustment of the transmission sequence is triggered; the transmission demand of the key data packets is smoothed by using a weighted moving average algorithm, and the transmission priority of each data packet is recalculated; the priority queue is dynamically reordered based on the recalculated transmission priority; the bandwidth resource allocation ratio is updated according to the new queue structure, the bandwidth parameters of the network nodes are reconfigured according to the updated resource allocation ratio, and finally the priority transmission sequence is generated.

6. The method of claim 1, wherein, The step S4 comprises: High-priority data packets are extracted from the priority transmission sequence by packet identification analysis to determine a high-priority data packet set; the high-priority data packets are stored in a local cache unit, and the storage location of the cache unit is determined by using a cache allocation algorithm; The occupancy state of the local cache unit is analyzed to obtain the available capacity of the cache unit and determine the transmission buffer configuration; if the available capacity of the transmission buffer configuration is lower than a preset threshold, low-priority data packets are released by a cache cleaning mechanism to obtain an updated cache configuration.

7. The method of claim 1, wherein, Step S5 comprises: The transmission buffer state is analyzed to obtain the current bandwidth occupation ratio and determine the initial configuration of bandwidth allocation; the bandwidth usage mode is extracted from historical data according to the initial configuration, and an optimized allocation strategy is obtained by using a reinforcement learning model training; if the bandwidth occupation ratio of the allocation strategy exceeds a preset threshold, bandwidth resources are re-allocated by a dynamic adjustment mechanism to generate an updated allocation scheme; According to the updated allocation scheme, the load state of the communication resources is analyzed to determine the priority order of resource configuration; the bandwidth scheduling parameters of the network nodes are adjusted by the priority order to obtain an optimized scheduling configuration; the resource allocation of the transmission buffer is updated according to the scheduling configuration to generate an optimized communication resource configuration.

8. The method of claim 1, wherein, Step S6 comprises: Channel state information is obtained through a wireless channel to determine the transmission capacity of the current channel; the transmission priority of the data packets is extracted from the priority sequence according to the transmission capacity to generate an initial transmission queue; if the network load of the initial transmission queue exceeds a preset threshold, the transmission order of the data packets is adjusted by a resource scheduling mechanism to obtain an optimized transmission queue, and the transmission success rate and network delay of each data packet are obtained according to the optimized transmission queue to determine the transmission performance indicators.

9. The method of claim 1, wherein, Step S7 comprises: Based on the transmission performance index, a load-balanced resource scheduling scheme is generated by analyzing the network load distribution state; the resource allocation parameters of the wireless channel are adjusted according to the resource scheduling scheme, the optimized communication resource configuration is obtained, and the transmission path of the priority transmission sequence is updated; the transmission success rate and network delay data are extracted from the network response to construct a feedback information dataset, and the support vector machine algorithm is used to optimize the parameters of the dynamic adjustment algorithm to obtain an updated parameter set; When the updated parameter set meets the preset load balancing condition, the priority order of the transmission queue is adjusted according to the parameter set, and a new transmission queue is generated; based on the new transmission queue, real-time channel state information is obtained, the corresponding channel resource allocation scheme is determined, and the sending order of the data packet is adjusted; the resource management configuration of the next period is generated according to the adjusted sending order.

10. A coal flow wiper multi-device cooperative control system for implementing a coal flow wiper multi-device cooperative control method according to any one of claims 1-9, characterized in that, The system comprises: A monitoring module is configured to obtain an original environment data set of each sensor node deployed in a downhole environment, process the original environment data set, and obtain a current communication quality index; An allocation module is configured to calculate the bandwidth demand change of each node by using a dynamic adjustment algorithm based on the communication quality index and in combination with the historical transmission records of the nodes, and predict the resource allocation proportion between the nodes; A scheduling module is configured to sort key data packets in real-time monitoring information by using a priority queue to obtain a priority transmission sequence if the current available network resources cannot meet the resource allocation proportion; A cache module is configured to extract high-priority data from the priority transmission sequence, temporarily store the high-priority data by using a local cache unit, and determine a transmission buffer state; An optimization module is configured to dynamically adjust the bandwidth allocation by using an intelligent optimization model according to the transmission buffer state, and obtain an optimized communication resource configuration; An evaluation module is configured to determine whether the communication resource configuration meets the inter-node load balancing requirement, and if so, transmit data packets in the priority transmission sequence by using a wireless channel, determine a transmission success rate of the data packets, and determine a transmission performance index; A feedback module is configured to generate a resource scheduling scheme according to the transmission performance index, obtain feedback information, update parameters of the dynamic adjustment algorithm, and generate a resource management scheme of the next period.

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