Multi-machine collaborative anchoring and drilling machine anchoring operation scheduling method and system

Abstract

The present invention discloses a method and system for scheduling anchoring operations of a multi-machine coordinated anchoring and drilling machine, which relates to the technical field of anchoring and drilling machine control. The method includes: real-time monitoring of the drilling parameters of the anchoring and drilling machine; extracting the gangue load and load fluctuation rate; predicting dust dispersion based on dynamic cutting parameters and the gangue load; performing load response analysis and outputting speed gradient characteristics; compensating and correcting the initial dust concentration prediction value and outputting the corrected dust concentration prediction value; making dust removal linkage decisions and performing multi-machine coordinated control of the onboard wet dust removal blower and belt conveyor. The present invention solves the technical problems in the prior art of low coordinated operation efficiency of anchoring and drilling machines, inability to effectively control dust concentration, poor working environment safety, and low equipment operating efficiency, and achieves the technical effect of realizing efficient coordinated operation of the onboard wet dust removal blower and belt conveyor in the cutting mode of the anchoring and drilling machine, improving working environment safety and equipment operating efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of anchor miner control, and in particular to a method and system for scheduling anchoring operations of a multi-machine coordinated anchor miner. Background Art

[0002] In the coal mining sector, the use of integrated drilling and anchoring machines is becoming increasingly widespread. They combine tunneling and anchor support functions in one machine. However, many problems still exist in the operation of integrated drilling and anchoring machines. On the one hand, the integrated drilling and anchoring machines generate a large amount of dust during cutting operations. Existing dust removal equipment and belt conveyor systems often operate independently and lack an effective coordination mechanism. As a result, dust cannot be promptly and efficiently processed, and the dust concentration in the working environment is too high. On the other hand, the operating parameters of the belt conveyor fail to adjust in real time according to the coal gangue load and load fluctuation rate. Overload or underload conditions often occur, which not only reduces transportation efficiency but also easily causes equipment wear and shortens equipment life, thus affecting the continuity and stability of the entire mining operation.

[0003] The existing technology has technical problems such as low collaborative operation efficiency of the integrated drilling and anchoring machine, inability to effectively control dust concentration, poor working environment safety and low equipment operation efficiency. Summary of the Invention

[0004] The present application provides a method and system for scheduling anchoring operations of a multi-machine coordinated drilling and anchoring machine, which is used to solve the technical problems in the prior art of low collaborative operation efficiency of drilling and anchoring machines, inability to effectively control dust concentration, poor working environment safety, and low equipment operation efficiency.

[0005] In view of the above problems, the present application provides a method and system for scheduling anchoring operations of a multi-machine coordinated drilling and anchoring machine.

[0006] The first aspect of the present application provides a method for scheduling anchoring operations of a multi-machine coordinated anchoring and drilling machine, the method comprising:

[0007] The system monitors the tunneling parameters of the anchoring and boring machine in real time to obtain dynamic cutting parameters. Using the monitoring window of the dynamic cutting parameters, the weighing sensor of the belt conveyor extracts the gangue load and load fluctuation rate. After retrieving tunneling environment information from the environmental sensor network, the system uses the tunneling environment information as interference to predict dust dispersion based on the dynamic cutting parameters and the gangue load, outputting an initial dust concentration prediction value. Load response analysis is performed based on the gangue load and load fluctuation rate, outputting a speed gradient characteristic of the belt conveyor. The speed gradient characteristic and load fluctuation rate are introduced to compensate for and correct the initial dust concentration prediction value, outputting a corrected dust concentration prediction value. A dust removal linkage decision is made based on the corrected dust concentration prediction value, outputting a wet dust removal control characteristic. When the anchoring and boring machine switches to the cutting mode, the onboard wet dust removal blower and the belt conveyor are synchronously activated, and the wet dust removal control characteristic and the speed gradient characteristic are used to perform multi-machine coordinated control of the onboard wet dust removal blower and the belt conveyor.

[0008] The second aspect of the present application provides a multi-machine coordinated anchoring and drilling machine anchoring operation scheduling system, the system comprising:

[0009] The dynamic cutting parameter acquisition module is used to obtain the dynamic cutting parameters by real-time monitoring of the excavation parameters of the anchor-drilling machine; the load data extraction module is used to use the monitoring window of the dynamic cutting parameters to extract the gangue load and load fluctuation rate from the weighing sensor of the belt conveyor; the initial dust concentration prediction value output module is used to call the excavation environment information from the environmental sensor network, use the excavation environment information as the interference quantity, perform dust dispersion prediction based on the dynamic cutting parameters and the gangue load, and output the initial dust concentration prediction value; the speed gradient feature output module is used to perform load response analysis based on the gangue load and load fluctuation rate, and output a speed gradient characteristic of the belt conveyor; a modified dust concentration prediction value output module, used to introduce the speed gradient characteristic and load fluctuation rate, perform compensation correction on the initial dust concentration prediction value, and output a modified dust concentration prediction value; a wet dust removal control characteristic output module, used to make a dust removal linkage decision based on the modified dust concentration prediction value, and output a wet dust removal control characteristic; a multi-machine collaborative control module, used to synchronously start the onboard wet dust removal blower and the belt conveyor when the integrated miner and anchor machine switches to the cutting mode, and adopt the wet dust removal control characteristic and speed gradient characteristic to perform multi-machine collaborative control of the onboard wet dust removal blower and the belt conveyor.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] By monitoring the excavation parameters of the integrated miner and anchor machine in real time, dynamic cutting parameters are obtained; the gangue load and load fluctuation rate are extracted from the belt conveyor's weighing sensor; the excavation environment information is used as interference, and dust dispersion is predicted based on the dynamic cutting parameters and gangue load, outputting an initial dust concentration prediction value; load response analysis is performed, outputting the speed gradient characteristics of the belt conveyor; the speed gradient characteristics and load fluctuation rate are introduced to compensate for the initial dust concentration prediction value, outputting a corrected dust concentration prediction value; a dust removal linkage decision is made based on the corrected dust concentration prediction value, outputting a wet dust removal control characteristic; the onboard wet dust removal duct and the belt conveyor are synchronously started, and the wet dust removal control characteristic and speed gradient characteristics are used to perform multi-machine coordinated control of the onboard wet dust removal duct and the belt conveyor. This achieves the technical effect of achieving efficient coordinated operation of the onboard wet dust removal duct and the belt conveyor in the cutting mode of the integrated miner and anchor machine, improving the safety of the working environment and the efficiency of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A flowchart of a method for scheduling anchoring operations using a multi-machine coordinated anchoring and drilling machine provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of the multi-machine collaborative anchoring and drilling machine anchoring operation scheduling system provided in an embodiment of the present application.

[0015] Explanation of the accompanying drawings: dynamic cutting parameter acquisition module 10, load data extraction module 20, initial dust concentration prediction value output module 30, speed gradient feature output module 40, corrected dust concentration prediction value output module 50, wet dust removal control feature output module 60, multi-machine collaborative control module 70. DETAILED DESCRIPTION

[0016] This application provides a multi-machine collaborative anchoring operation scheduling method and system for an integrated drilling and anchoring machine, which is used to solve the technical problems in the prior art of low collaborative operation efficiency of the integrated drilling and anchoring machine, inability to effectively control dust concentration, poor working environment safety, and low equipment operation efficiency.

[0017] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0018] Example 1, as Figure 1 As shown, the present application provides a method for scheduling anchoring operations of a multi-machine coordinated anchoring machine, the method comprising:

[0019] Step S100: obtaining dynamic cutting parameters by real-time monitoring of the excavation parameters of the integrated excavator and anchoring machine.

[0020] Specifically, during underground coal mine operations, the dynamic cutting parameters are obtained by monitoring the excavation parameters of the anchor and digger in real time. First, the operating mode of the anchor and digger must be determined, and whether it is in cutting mode is determined based on the position of its sliding base. If it is in cutting mode, the anchor and digger's built-in sensing unit is activated, and this built-in sensing unit establishes a communication connection with the speed sensor, hydraulic pressure sensor, and hardness fusion sensor. After activation, the built-in sensing unit begins to work, receiving the real-time cutting head speed returned by the speed sensor. This parameter reflects the rotation speed of the cutting head and can reflect the working efficiency of the anchor and digger when cutting coal and rock; receiving the real-time thrust pressure returned by the hydraulic pressure sensor; and receiving the real-time rock formation hardness returned by the hardness fusion sensor. This parameter reflects the hardness of the rock formation in the current cutting area. The above-mentioned real-time cutting head speed, real-time thrust pressure, and real-time rock formation hardness together constitute the dynamic cutting parameters.

[0021] Step S200: using the monitoring window of the dynamic cutting parameters, extracting the gangue load and load fluctuation rate from the weighing sensor of the belt conveyor.

[0022] Specifically, a matching monitoring window is set based on the obtained dynamic cutting parameters. Based on the cutting rhythm of the anchor-drilling machine and the changing patterns of the cutting parameters, a connection is established with the weighing sensor on the conveyor belt during the time period when this monitoring window is open, collecting data in real time. The weighing sensor continuously monitors the weight of the gangue on the belt, extracting the gangue load from it. This value intuitively reflects the actual weight of the gangue carried by the conveyor belt at the current moment. Simultaneously, the changing data on the gangue load over a period of time is deeply processed to calculate the load fluctuation rate. The load fluctuation rate reflects the severity of the change in the gangue load. For example, when cutting hard rock, the gangue load will suddenly increase, resulting in an increased load fluctuation rate; whereas, when cutting soft rock, the load fluctuation rate is relatively low. Acquiring these two key data sets provides an important basis for subsequent analysis of the conveyor belt's operating status, prediction of dust concentration, and implementation of multi-machine coordinated control.

[0023] Step S300: After the excavation environment information is retrieved from the environmental sensor network, the excavation environment information is used as interference, dust dispersion is predicted based on the dynamic cutting parameters and the gangue load, and an initial dust concentration prediction value is output.

[0024] Specifically, tunneling environmental information is retrieved from a pre-built environmental sensor network, which integrates roof displacement sensors, gas sensors, and dust sensors. The roof displacement sensors monitor roof displacement changes in real time, the gas sensors accurately measure gas concentration within the tunnel, and the dust sensors continuously provide feedback on dust concentration within the tunnel. Together, these data constitute tunneling environmental information. This acquired tunneling environmental information is treated as interference and combined with dynamic cutting parameters (including real-time cutting head speed, real-time thrust pressure, and real-time rock hardness) and gangue loading. Dust dispersion is predicted using a pre-built dust concentration regression function. This function, derived from an analysis of extensive local historical records, uses multiple sampled diffuse dust concentrations as output values ​​and inputs such as sampled cutting speed, sampled thrust pressure, sampled rock hardness, sampled loading, sampled roof displacement, sampled gas concentration, and sampled ambient dust concentration. This function is constructed through a multivariate regression analysis and dynamically updated every M times a preset sliding time window to ensure prediction accuracy. After inputting the current dynamic cutting parameters, gangue loading, and tunneling environment information into the dust concentration regression function, a real-time dust concentration prediction is output. Considering that the anchor drill may be in different operating modes, and that dust generation and dispersion vary depending on the mode, the real-time dust concentration prediction is compensated for based on the drill's real-time mode. For example, in cutting mode, where the machine operates at a high intensity and generates a large amount of dust, the compensation method differs from other modes. After this series of processing, the final output is the initial dust concentration prediction.

[0025] Step S400: performing load response analysis based on the gangue load and load fluctuation rate, and outputting the speed gradient characteristics of the belt conveyor.

[0026] Specifically, the speed gradient characteristics of the belt conveyor are determined based on the gangue load and load fluctuation rate, thereby optimizing the belt conveyor's operational control. After obtaining the gangue load and load fluctuation rate, an in-depth load response analysis is conducted on these data. The gangue load reflects the actual weight of the gangue carried by the belt conveyor, while the load fluctuation rate reflects the degree to which the gangue load varies over time. Together, these two data provide a comprehensive picture of the belt conveyor's load condition. An analytical model is developed that incorporates the belt conveyor's design parameters, motor performance, and historical operating data. During the analysis, the model simulates the belt conveyor's operating conditions under varying load conditions, depending on the gangue load and load fluctuation rate. For example, when the gangue load is large and the load fluctuation rate is high, indicating a heavy and frequently fluctuating belt conveyor workload, the model calculates that the belt conveyor requires a lower operating speed to ensure transport stability and increases the intervals between intermittent starts and stops to prevent motor damage from prolonged high-load operation. This analysis ultimately outputs the speed gradient characteristics of the conveyor belt. A specific example of this speed gradient characteristic is the speed range (1-3 m / s) and intermittent start-stop cycle (≤30 seconds). This speed gradient characteristic provides a key basis for subsequent precise control of the conveyor belt, enabling it to maintain efficient and stable operation under varying gangue loading conditions. This also helps improve the safety and reliability of the entire anchor drilling system.

[0027] Step S500: introducing the speed regulation gradient characteristics and the load fluctuation rate, performing compensation correction on the initial dust concentration prediction value, and outputting a corrected dust concentration prediction value.

[0028] Specifically, multiple sample dust compensation coefficients are interactively obtained for multiple sample speed gradient intervals. These intervals are connected adjacently to cover the various possible speed gradient conditions of the belt conveyor. Simultaneously, multiple sample dust fluctuation intervals are interactively obtained for multiple sample volatility levels, also connected adjacently to fully cover the load fluctuation range. Next, the obtained speed gradient features are used to traverse the multiple sample speed gradient intervals, and the corresponding real-time dust compensation coefficients are found through matching. The load fluctuation rate is used to traverse the multiple sample volatility levels and map the real-time dust fluctuation intervals. The real-time dust compensation coefficients are then used to update the initial dust concentration prediction value to obtain an updated dust concentration prediction value. The updated dust concentration prediction value is then corrected using the real-time dust fluctuation interval to obtain a dust concentration prediction interval. Finally, the maximum value of this dust concentration prediction interval is retrieved and the resulting value is output as the corrected dust concentration prediction value. This output corrected dust concentration prediction value is more consistent with actual conditions, providing a more reliable basis for subsequent dust removal decisions.

[0029] Step S600: making a dust removal linkage decision based on the corrected dust concentration prediction value, and outputting a wet dust removal control feature.

[0030] Specifically, the corrected dust concentration prediction value is used to determine the appropriate dust removal strategy, thereby outputting the wet dust removal control characteristics. First, multiple sample dust removal area limits and multiple sample dust removal air volume limits are interactively obtained for multiple sample ambient dust concentrations. These sample data cover the appropriate dust removal area ranges and required air volume sizes corresponding to different dust concentrations. Next, these sample ambient dust concentrations, sample dust removal area limits, and sample dust removal air volume limits are linearly interpolated and expanded, rationally expanding and supplementing the discrete data points to construct a comprehensive and detailed dust removal association information database. The corrected dust concentration prediction value is then used to traverse this dust removal association information database. Through search and matching, the real-time dust removal area and real-time dust removal air volume that match the current corrected dust concentration prediction value are accurately obtained. For example, when the corrected dust concentration prediction value is within a specific range, the corresponding optimal dust removal area and required air volume are found from the information database. The real-time dust removal area and real-time dust removal air volume together constitute the wet dust removal control characteristics. These wet dust removal control characteristics will be output and transmitted to the relevant equipment control system for precise control of the operation of the wet dust removal equipment, such as determining the spray coverage, adjusting the spray pressure and flow, etc., to ensure efficient dust removal operations under different dust concentrations, and to ensure the cleanliness of the underground working environment and the health and safety of personnel.

[0031] Step S700: When the anchor miner is switched to the cutting mode, the onboard wet dust removal blower and the belt conveyor are started synchronously and the wet dust removal control feature and the speed gradient feature are used to perform multi-machine coordinated control of the onboard wet dust removal blower and the belt conveyor.

[0032] Specifically, when the miner switches to cutting mode, the onboard wet dust collection blower and belt conveyor are simultaneously activated to ensure efficient coordination throughout the entire operation. During the startup process, both devices are precisely controlled based on the wet dust collection control characteristics and speed gradient characteristics derived from the previous steps. The wet dust collection control characteristics include key parameters such as the real-time dust collection area and real-time dust collection air volume. Based on these parameters, the spray range and spray volume of the onboard wet dust collection blower are precisely controlled. For example, when dust concentration is predicted to be high, the spray coverage area is expanded and the spray volume is increased to ensure effective dust reduction at the source and diffusion area of ​​cutting dust. Conversely, spray parameters are adjusted accordingly to avoid resource waste. Furthermore, the speed gradient characteristics provide a control basis for the belt conveyor's operation. Based on the speed gradient characteristics determined by the gangue load and load fluctuation rate, such as the speed range (1-3 m / s) and intermittent start-stop cycle (≤30 seconds), the belt conveyor's operating speed and start-stop timing are precisely adjusted. When the gangue load is heavy and fluctuates frequently, the conveyor speed is reduced and the intermittent start-stop cycle is appropriately increased to ensure transportation safety and equipment stability. When the load is light, the conveyor speed is increased to improve transportation efficiency. Through the coordinated control of the onboard wet dust removal blower and the conveyor, the dust concentration at the work site is effectively reduced while ensuring smooth gangue transportation. This allows the integrated miner-anchor machine to carry out cutting operations efficiently and safely, creating a favorable environment for underground production.

[0033] In one possible implementation, step S100 further includes:

[0034] Step S110: Dynamically optimize anchoring parameters according to the dynamic cutting parameters, and output dynamic anchor density and support lag distance.

[0035] Step S120: When the displacement distance of the belt conveyor reaches the support lag distance, the anchor drill is activated to perform anchor implantation with the dynamic anchor density as the implantation constraint, wherein, during the displacement process of the belt conveyor, the wet dust removal control feature and the speed regulation gradient feature are used to perform coordinated control of the onboard wet dust removal blower and the belt conveyor.

[0036] Specifically, the team first conducted an in-depth analysis of several dynamic cutting parameters: real-time cutter head speed, real-time thrust pressure, and real-time rock hardness. High cutter head speed, high thrust pressure, and high rock hardness indicate greater stress in the tunnel surrounding rock, necessitating stronger support. By matching these parameters with a pre-defined support characteristic model, the first, second, and third support reduction scales were derived, reflecting the adjustment of support requirements in different aspects. The maximum of these three reduction scales was selected as the real-time support reduction scale, which comprehensively considers the support strength requirements of various factors. Using this real-time support reduction scale, the real-time lag distance was updated to determine an appropriate support lag distance that ensures timely support during tunneling without compromising tunneling efficiency due to premature support. Furthermore, anchor spacing characteristics were matched based on the real-time rock hardness and tunnel gas concentration. In areas with high rock hardness or gas concentration, anchor bolt spacing can be appropriately reduced and anchor density increased to ensure roadway stability and safety. Conversely, anchor bolt spacing can be appropriately increased and the number of anchors used can be reduced. This approach ultimately outputs dynamic anchor density and support lag distance that meet current operating conditions, enabling anchoring operations to better adapt to the complex and changing underground geological environment and excavation conditions, effectively improving the quality and efficiency of roadway support and ensuring safe underground operations.

[0037] The conveyor belt's displacement is continuously monitored. When the conveyor belt's displacement reaches the support lag distance calculated based on dynamic cutting parameter optimization, the anchor placement process is triggered. At this point, the anchor drill is activated and begins operations, using dynamic anchor density as a constraint. Dynamic anchor density is determined based on factors such as real-time rock hardness and roadway gas concentration. This ensures that the number and spacing of anchors installed meet the actual roadway support requirements, effectively safeguarding roadway stability. From the start of the conveyor belt's displacement until the support lag distance is reached, the onboard wet dust collection blower and the conveyor belt are coordinated to create a favorable working environment and ensure stable equipment operation. Based on the real-time dust collection area and air volume in the wet dust collection control feature, the onboard wet dust collection blower precisely adjusts the spray range and volume. This effectively suppresses dust dispersion in areas where the conveyor belt conveyor generates dust, reduces dust concentration in the working environment, and protects worker health. At the same time, the conveyor's operating speed and intermittent start-stop cycle are adjusted based on the speed gradient characteristics. When the gangue load is heavy or fluctuates significantly, the conveyor speed is appropriately reduced and the intermittent start-stop cycle is increased to prevent equipment overload. When the load is light, the operating speed is increased to improve transport efficiency. This coordinated control achieves efficient coordination between dust reduction and transport, providing strong support for the smooth implementation of anchor bolting operations.

[0038] In one possible implementation, step S100 further includes:

[0039] Step S130: determining the operation mode according to the position of the sliding base of the anchor and miner.

[0040] Step S140: When the anchor miner is in the cutting mode, the built-in sensing unit of the anchor miner is activated, wherein the built-in sensing unit is communicatively connected to the rotation speed sensor, the hydraulic pressure sensor, and the hardness fusion sensor respectively.

[0041] Step S150: After being activated, the built-in sensing unit receives the real-time cutting head speed, real-time propulsion pressure, and real-time rock formation hardness transmitted back by the speed sensor, hydraulic pressure sensor, and hardness fusion sensor, respectively. The real-time cutting head speed, real-time propulsion pressure, and real-time rock formation hardness constitute the dynamic cutting parameters.

[0042] Specifically, the working status of the anchor and miner is continuously monitored. The position of the sliding base is an important basis for judging its operating mode. By acquiring and analyzing the position information of the sliding base, the current operating mode of the anchor and miner can be identified, such as cutting mode, drilling mode or other modes.

[0043] Once it is determined that the anchor and miner is in cutting mode, the built-in sensing unit of the anchor and miner is immediately activated. The built-in sensing unit establishes communication connections with the speed sensor, hydraulic pressure sensor, and hardness fusion sensor respectively. The speed sensor is responsible for monitoring the rotation speed of the cutting head, the hydraulic pressure sensor provides real-time feedback on the pressure conditions during advancement, and the hardness fusion sensor is used to detect the hardness of the currently cut rock formation.

[0044] When the anchor miner is in cutting mode and the internal sensing unit is activated, it begins collecting key operating parameters in real time. The internal sensing unit works in conjunction with the speed sensor, hydraulic pressure sensor, and hardness fusion sensor. The speed sensor monitors the cutting head's rotational speed in real time, generating a signal that is rapidly transmitted back to the internal sensing unit. This data reflects the cutting head's operating efficiency and health. The hydraulic pressure sensor monitors the machine's thrust pressure, accurately transmitting real-time thrust pressure data to the internal sensing unit. This data provides an indication of the resistance encountered during the cutting process. The hardness fusion sensor measures the hardness of the rock being cut and feeds this real-time hardness data back to the internal sensing unit, providing critical information for determining rock formation characteristics. The internal sensing unit integrates the received data on the cutting head's speed, thrust pressure, and rock formation hardness to form dynamic cutting parameters.

[0045] In one possible implementation, step S300 further includes:

[0046] Step S310: Environmental sensing is performed by using the roof displacement sensor, gas sensor and dust sensor integrated in the environmental sensing network to obtain the excavation environment information, wherein the excavation environment information includes real-time roof displacement, tunnel gas concentration and tunnel dust concentration.

[0047] Step S320: pre-constructing a dust concentration regression function, and inputting the dynamic cutting parameters, gangue load and tunneling environment information into the dust concentration regression function to perform dust dispersion prediction and output a real-time dust concentration prediction value.

[0048] Step S330: performing mode collaborative compensation on the real-time dust concentration prediction value according to the real-time mode of the integrated miner and anchor machine, and outputting the initial dust concentration prediction value.

[0049] Specifically, roof displacement sensors, gas sensors, and dust sensors integrated into the environmental sensing network provide comprehensive awareness of tunneling environmental conditions. Roof displacement sensors monitor minute roof movements in real time, obtaining real-time roof displacement data for roof stability assessment. Gas sensors measure gas concentration within the tunnel to prevent accidents caused by excessive gas levels. Dust sensors measure dust concentration in the tunnel to assess the extent of dust pollution at the worksite. These sensors work together to aggregate their collected data into comprehensive tunneling environmental information, including real-time roof displacement, tunnel gas concentration, and tunnel dust concentration.

[0050] Based on a large amount of accumulated local historical records, multiple samples of diffuse dust concentration data are retrieved. Starting from this sample data, a preset sliding time window is used to trace back historical correlation data, obtaining multiple sample dust correlation data. This includes information such as cutting speed, thrust pressure, rock hardness, load, roof displacement, gas concentration, and ambient dust concentration. Multiple regression analysis is performed using these sample diffuse dust concentrations as output and multiple sample dust correlation data as input. This pre-constructs a dust concentration regression function, using a sliding time window of M times the original time as the function update cycle to ensure that the function can adapt to changing operating conditions. Once the anchor-drilling machine enters operation, dynamic cutting parameters are obtained through real-time monitoring of its excavation parameters. Simultaneously, the dynamic cutting parameter monitoring window is used to extract the gangue load from the belt conveyor's load cell. Furthermore, excavation environmental information, including real-time roof displacement, roadway gas concentration, and roadway dust concentration, is retrieved from the environmental sensor network. Subsequently, the dynamic cutting parameters, gangue load and tunneling environment information are input into the pre-built dust concentration regression function. Based on the input data, this function simulates the dust diffusion process in the current working environment, and comprehensively considers the impact of factors such as the operating status of the equipment during the cutting operation, the gangue transportation volume and the tunnel environment on the generation and diffusion of dust. Finally, it outputs a real-time dust concentration prediction value.

[0051] A decision tree algorithm is used to perform pattern-coordinated compensation on the real-time dust concentration predictions, thereby outputting an initial dust concentration prediction. A decision tree model is constructed, using the real-time mode of the anchor drill (e.g., cutting mode, drilling mode), dynamic cutting parameters (real-time cutting head speed, real-time thrust pressure, real-time rock hardness), gangue load, and tunneling environment information (real-time roof displacement, roadway gas concentration, and roadway dust concentration) as input features. By learning from a large amount of historical data, the decision tree automatically analyzes the relationships between these features and generates corresponding decision rules. After obtaining the real-time mode and other relevant data, the decision tree model makes decisions based on these input features. For example, if the cutting mode is determined, the real-time cutting head speed is high, and the gangue load is high, the decision tree will branch along a specific path. Based on the variation pattern of dust concentration in this situation in the training data, the decision tree outputs a compensation value. This compensation value is obtained by statistically analyzing the dust concentration differences under similar operating conditions in the historical data. The real-time dust concentration prediction is calculated against the compensation value output by the decision tree. If the compensation value is positive, it is added to the real-time dust concentration prediction; if it is negative, it is subtracted from the prediction. This calculation yields an adjusted dust concentration value. The decision tree model then further evaluates and fine-tunes this adjusted dust concentration value, taking into account other relevant factors such as tunnel ventilation conditions and equipment dust removal efficiency. Ultimately, an initial dust concentration prediction is output, taking into account the real-time dust concentration mode of the anchor drill and other factors.

[0052] In one possible implementation, step S110 further includes:

[0053] Step S111: Dynamically matching support characteristics is performed according to the real-time cutting head rotation speed, real-time propulsion pressure and real-time rock formation hardness to obtain a first support reduction scale, a second support reduction scale and a third support reduction scale.

[0054] Step S112: extracting a maximum shrinkage scale from the first support shrinkage scale, the second support shrinkage scale, and the third support shrinkage scale as a real-time support shrinkage scale.

[0055] Step S113: using the real-time support shrinkage scale to update the real-time lag distance to obtain the support lag distance.

[0056] Step S114: performing anchor bolt spacing feature matching based on the real-time rock formation hardness and roadway gas concentration, and outputting the dynamic anchor bolt density.

[0057] Specifically, data on support reduction scales corresponding to different combinations of cutter head speed, thrust pressure, and rock formation hardness are pre-stored. Once the real-time cutter head speed, thrust pressure, and rock formation hardness are acquired, a matching combination is searched in the database to determine the first, second, and third support reduction scales. For example, if the real-time cutter head speed is fast, the thrust pressure is high, and the rock formation hardness is high, the corresponding support reduction scale may be relatively large. This is because, under these conditions, the surrounding rock of the tunnel is less stable and requires more stringent support measures.

[0058] From the obtained first, second, and third support reduction scales, the largest reduction scale is selected as the real-time support reduction scale. This is based on the principle of safety first. The largest reduction scale means stricter support requirements, which can better ensure the stability of the roadway. For example, if the first support reduction scale is 0.8, the second support reduction scale is 0.9, and the third support reduction scale is 0.7, then the real-time support reduction scale will be determined as 0.7.

[0059] The real-time support reduction scale is used to update the real-time lag distance. The real-time lag distance refers to the distance between the cutting operation point and the support operation point of the bolter. The current real-time lag distance is adjusted based on the real-time support reduction scale to ensure that support operations can keep pace with cutting operations and maintain roadway stability. For example, if the real-time support reduction scale is 0.7, the real-time lag distance will be reduced by 30%, resulting in the final support lag distance.

[0060] Real-time rock formation hardness and roadway gas concentration data are obtained. Rock formation hardness is assessed based on established rock formation hardness classification standards. If the real-time rock formation hardness indicates hard rock (f>8), the anchor bolt spacing is reduced to 0.8m, and full-length bonded anchor bolts with a preload of ≥100kN are used. If the rock formation is identified as a fracture zone (f<4), the anchor bolt spacing is reduced to 0.6m, and resin anchoring agents are used to enhance bonding. For medium rock formations (4≤f≤8), the anchor bolt spacing is set to 1.0m, and standard end-to-end anchoring is used; f represents the rock formation hardness. After adjusting the anchor bolt spacing based on rock formation hardness, the roadway gas concentration is considered. If the gas concentration is greater than 0.5%, regardless of rock formation hardness, the roof anchor density is increased by an additional 20% on top of the anchor bolt spacing adjusted for rock formation hardness. This is because higher gas concentrations increase the risk of caving, and increasing the anchor bolt density can effectively reduce this risk. After the above anchor spacing adjustment and anchor density correction operations based on real-time rock hardness and tunnel gas concentration, the final output is a dynamic anchor density that can adapt to the current working environment conditions, thereby ensuring the safety and stability of the tunnel support.

[0061] In one possible implementation, step S320 further includes:

[0062] Step S321: Obtain the diffuse dust concentration of multiple samples by calling local historical records.

[0063] Step S322: Taking the diffuse dust concentrations of the multiple samples as the starting point, a preset sliding time window is used to backtrack historical correlation data to obtain multiple sample dust correlation data, wherein the sample dust correlation data includes sample cutting speed, sample propulsion pressure, sample rock formation hardness, sample load, sample roof displacement, sample gas concentration, and sample environmental dust concentration.

[0064] Step S323: using the diffuse dust concentrations of the multiple samples as output values ​​and the dust-related data of the multiple samples as input values, performing a multivariate regression analysis to obtain the dust concentration regression function.

[0065] Step S324: using M times the sliding time window as a function update period to dynamically update the dust concentration regression function.

[0066] Specifically, the system extracts multiple sample diffuse dust concentrations from stored historical data by calling local historical records. This historical data, derived from dust concentration information recorded and monitored in real time by the anchoring and drilling machine during past operations, serves as the foundation for subsequent analysis and modeling.

[0067] Starting with the multiple sample diffuse dust concentrations retrieved from local historical records, the process of retracing historical correlation data begins. The preset sliding time window is a pre-set time range within which historical data is searched and filtered. This process traces back to the past along the timeline, with the sample diffuse dust concentration data as the core. As the sliding time window slides through the historical data, a series of data temporally correlated with the sample diffuse dust concentrations is extracted. This data represents the multiple sample dust correlation data. The sample cutting speed reflects the rotational speed of the cutter head of the anchor-mining machine at the time, which directly affects the degree of coal and rock crushing and, in turn, has a significant impact on dust generation. The sample thrust pressure reflects the force applied by the machine during the thrust process, with varying thrust pressures resulting in varying coal and rock crushing and dust generation. The sample rock formation hardness represents the hardness of the rock formation in the operating area, with varying rock formations generating varying amounts of dust during cutting. The sample load refers to the weight of materials such as gangue transported on the conveyor belt, indirectly reflecting the intensity of the cutting operation and the potential for dust generation. The sample roof displacement reflects the deformation of the roadway roof, which can cause dust to fly and disperse. The sample gas concentration reflects the gas content within the roadway, and changes in gas concentration can affect the operation of the ventilation system and, in turn, dust dispersion. The sample ambient dust concentration refers to the pre-existing dust concentration in the operating environment at the time, serving as one of the initial conditions for dust dispersion. Through this retrospective analysis, comprehensive and systematic data on various factors related to dust dispersion were obtained.

[0068] By using the least squares method to perform multiple regression analysis, the concentration of diffuse dust in multiple samples is recorded as ( =1, 2, ..., n, where n is the number of samples), and the dust-related data of multiple samples are regarded as independent variables, respectively. , ,..., (p is the number of independent variables, including sample cutting speed, sample propulsion pressure, etc.). Assume that the dust concentration regression function is in the linear form = + + +...+ + ,in , ,... is the regression coefficient to be determined, is the random error term. Define the error sum of squares S ( , , ) = The goal is to find a set of regression coefficients , , , so that the error sum of squares S is minimized. , ,... Find the partial derivatives and set them equal to 0, thus obtaining a system of equations containing p+1 equations. Solving this system of equations will give us the estimated values ​​of the regression coefficients. , , The final dust concentration regression function is: = + + + + ,in is the dust concentration value predicted by the regression function, , , The key factor affecting dust concentration is the symbol of the independent variable (such as the variable of cutting speed itself), which is used for constructing the model formula and substituting variables in the prediction. The emphasis is on the general definition of the variable. This function predicts the dust concentration based on the input sample dust correlation data. When solving the equation, this function predicts the dust concentration based on the input sample dust correlation data. When solving the equation group, the matrix operation method is used to transform the problem into solving the normal equation ( ) = ,in is a matrix consisting of independent variables, is the vector consisting of the sample diffuse dust concentration, is the regression coefficient vector, Represents the transpose of the matrix. By calculating = The estimated value of the regression coefficient can be obtained.

[0069] A dynamic update mechanism is employed. First, a preset sliding time window is determined. Based on this window, a function update cycle is set to M times the sliding time window. As the anchor drill continues operating, new operational data is continuously generated. When the monitored time reaches the duration corresponding to M times the sliding time window, the dynamic update process for the dust concentration regression function is initiated. Local historical records are again retrieved to obtain the latest diffuse dust concentrations of multiple samples. Using these new samples as a starting point, the historical correlation data is retraced using the preset sliding time window to obtain new sample dust correlation data, including cutting speed and propulsion pressure. Multiple regression analysis is then re-performed, using these new sample diffuse dust concentrations as the new output values ​​and the new sample dust correlation data as the new input values. During the analysis, the coefficients in the regression function are recalculated and adjusted based on the characteristics and patterns of the new data. For example, if the new data reveals a change in the effect of cutting speed on dust concentration, the coefficient for cutting speed in the regression function is adjusted accordingly. This recalculation and adjustment results in an updated dust concentration regression function. This dynamic update mechanism ensures that the dust concentration regression function can keep up with changes in the working environment, providing strong support for subsequent more accurate prediction of dust concentration and reasonable scheduling of dust removal equipment and transportation systems.

[0070] In one possible implementation, step S500 further includes:

[0071] Step S510: interactively obtaining a plurality of sample dust compensation coefficients of a plurality of sample speed adjustment gradient intervals, wherein the plurality of sample speed adjustment gradient intervals are adjacent to each other.

[0072] Step S520: interactively obtaining a plurality of sample dust floating intervals of a plurality of sample volatility grades, wherein the plurality of sample volatility grades are adjacent and connected.

[0073] Step S530: using the speed gradient feature to traverse the plurality of sample speed gradient intervals to map and extract a real-time dust compensation coefficient, and using the load fluctuation rate to traverse the plurality of sample fluctuation rate grades to map and extract a real-time dust floating interval.

[0074] Step S540: applying the real-time dust compensation coefficient to update the initial dust concentration prediction value to obtain an updated dust concentration prediction value.

[0075] Step S550: using the real-time dust floating interval to correct the updated dust concentration prediction value to obtain a dust concentration prediction interval.

[0076] Step S560: performing a maximum value call on the dust concentration prediction interval to obtain the corrected dust concentration prediction value.

[0077] Specifically, through interactive means, multiple sample speed gradient intervals and corresponding multiple sample dust compensation coefficients are obtained from a large amount of historical data. These sample speed gradient intervals are adjacent and connected, covering various possible speed gradient ranges.

[0078] Similarly, multiple sample volatility classifications are determined based on pre-set volatility classification rules. Volatility is divided into low volatility, medium volatility, and high volatility. These classifications are interconnected and fully cover the entire range of load volatility without omissions. For each sample volatility classification, multiple corresponding sample dust floating ranges are determined by analyzing historical data, experimental results, or expert experience. Low volatility ( ) as an example, since the load fluctuation is small, the impact on dust concentration is relatively limited, so the corresponding sample dust floating range is shown as the dust concentration prediction value remains basically unchanged, that is, the dust concentration fluctuates within a relatively stable range and will not change significantly. ), load fluctuations will have a certain impact on the generation and diffusion of dust, causing the dust concentration to increase. At this time, the corresponding sample dust fluctuation range is 5%-8% higher than the predicted value. Under this fluctuation rate classification, the dust concentration prediction value will increase by 5%-8% on the original basis to reflect the impact of load fluctuations on dust concentration. In high fluctuations ( In the case of a sudden load change, a large amount of dust is often generated, significantly impacting dust concentration. Therefore, the corresponding sample dust fluctuation range is 10%-15% higher than the predicted value. This means that the predicted dust concentration will increase significantly, reflecting the strong impact of the sudden load change on dust concentration. Through the above process, multiple sample volatility levels and corresponding sample dust fluctuation ranges were successfully obtained.

[0079] The speed gradient characteristics are then compared against multiple pre-defined and acquired sample speed gradient intervals. These sample speed gradient intervals are divided based on extensive historical data and actual operational experience, with adjacent intervals closely connected, covering the entire range of possible speed gradients. During this traversal process, once a speed gradient characteristic falls within a sample speed gradient interval, the real-time dust compensation coefficient is accurately extracted from the corresponding sample dust compensation coefficients based on a pre-established mapping relationship. This coefficient is a key parameter that reflects the variation in dust concentration under this speed gradient and demonstrates the impact of speed regulation on dust generation and dispersion. A similar process is performed on the load fluctuation rate, comparing the acquired load fluctuation rate against multiple sample fluctuation rate classes. These sample fluctuation rate classes are also closely connected, covering the entire range of possible load fluctuation values. When the load fluctuation rate matches a corresponding sample fluctuation rate class, the real-time dust fluctuation range is extracted based on the mapping relationship. This range reflects the possible fluctuation range of dust concentration under the current load fluctuation. Through such operations, the real-time dust compensation coefficient and real-time dust floating range that match the current speed regulation gradient characteristics and load fluctuation rate are obtained respectively.

[0080] The initial dust concentration prediction value is read and the real-time dust compensation coefficient is calculated with the initial dust concentration prediction value according to a pre-set calculation rule. If the real-time dust compensation coefficient is greater than 1, it means that the change in the conveyor speed gradient has increased dust generation or diffusion. The initial dust concentration prediction value is multiplied by this coefficient to increase the prediction value accordingly. If the real-time dust compensation coefficient is less than 1, it indicates that the speed gradient change has improved the dust situation. The initial dust concentration prediction value is multiplied by this coefficient to decrease the prediction value. For example, if the initial dust concentration prediction value is 50 mg / m³ and the real-time dust compensation coefficient is 1.2, the updated calculation result is 50 × 1.2 = 60 mg / m³. This 60 mg / m³ is the updated dust concentration prediction value. This updated calculation results in an updated dust concentration prediction value that more accurately reflects the actual dust concentration in the operating environment under the current conveyor speed gradient.

[0081] Using the updated dust concentration prediction value as a benchmark, the lower limit of the real-time dust floating interval is compared with the updated dust concentration prediction value. If the lower limit of the real-time dust floating interval is lower than the updated dust concentration prediction value, the lower limit of the dust concentration prediction interval is set as the lower limit of the real-time dust floating interval; if the lower limit of the real-time dust floating interval is higher than the updated dust concentration prediction value, the updated dust concentration prediction value is used as the lower limit of the dust concentration prediction interval. Similarly, the upper limit of the real-time dust floating interval is compared with the updated dust concentration prediction value. If the upper limit of the real-time dust floating interval is higher than the updated dust concentration prediction value, the upper limit of the dust concentration prediction interval is set as the upper limit of the real-time dust floating interval; if the upper limit of the real-time dust floating interval is lower than the updated dust concentration prediction value, the updated dust concentration prediction value is used as the upper limit of the dust concentration prediction interval. In this way, the impact of load fluctuations on dust concentration is comprehensively considered, the updated dust concentration prediction value is reasonably corrected, and the dust concentration prediction interval is ultimately obtained.

[0082] The maximum value of the dust concentration prediction interval is called, and the maximum value from the interval is selected as the revised dust concentration prediction value. This revised dust concentration prediction value comprehensively considers the influence of factors such as speed gradient and load fluctuation rate on dust concentration, and is more accurate and reliable than the initial dust concentration prediction value.

[0083] In one possible implementation, step S600 further includes:

[0084] Step S610: interactively obtaining multiple sample dust removal area limits and multiple sample dust removal air volume limits for multiple sample environmental dust concentrations.

[0085] Step S620: Obtain a dust removal related information library by performing linear interpolation expansion on the multiple sample environmental dust concentrations, the multiple sample dust removal area restrictions, and the multiple sample dust removal air volume restrictions.

[0086] Step S630: using the corrected dust concentration prediction value to traverse the dust removal associated information library to obtain a real-time dust removal area and a real-time dust removal air volume, wherein the real-time dust removal area and the real-time dust removal air volume constitute the wet dust removal control feature.

[0087] Specifically, first, through interactive means, multiple sample dust removal area limits and multiple sample dust removal air volume limits corresponding to multiple sample environmental dust concentrations are obtained from historical data records, equipment operation manuals, or pre-set parameter libraries. The interactive method here can be automatic reading from the database or manual input. For example, in different operating scenarios, when the environmental dust concentration is within a certain range, the corresponding dust removal area has a specific size limit, and the dust removal air volume will also have a corresponding value range. These data are collected to provide a basis for subsequent analysis and processing.

[0088] After obtaining the basic data of multiple sample environmental dust concentrations, multiple sample dust removal area limits, and multiple sample dust removal air volume limits, the linear interpolation expansion operation begins. First, the multiple sample environmental dust concentrations are arranged in ascending order. These concentration values ​​form the basic framework for data processing. For each pair of adjacent sample environmental dust concentrations, such as C1 and C2, several intermediate concentration values ​​are selected between them. Assume that the intermediate concentration value C m Based on the linear relationship, the sample dust removal area limit A1 corresponding to C1 and the sample dust removal area limit A2 corresponding to C2 are used as the basis, and the formula A m =A1+ (A2-A1) calculates C m The corresponding new sample dust removal area limit A m Similarly, for the sample dust removal air volume limit, if C1 corresponds to the air volume limit V1, and C2 corresponds to V2, then C m The corresponding new sample dust removal air volume limit V m =V1+ This process is repeated for all adjacent sample ambient dust concentrations, continuously generating new sample dust removal area limit and sample dust removal air volume limit data. Finally, this newly generated data is integrated with the original data from multiple samples to form a richer, more comprehensive dust removal information library.

[0089] Using the corrected dust concentration prediction value as an index, a traversal search is carried out in the constructed dust removal related information library to find the data entry that best matches the corrected dust concentration prediction value in the information library. If the corrected dust concentration prediction value is exactly equal to the dust concentration of a sample environment in the information library, then the dust removal area limit and dust removal air volume limit corresponding to the sample are directly obtained and determined as the real-time dust removal area and real-time dust removal air volume. The real-time dust removal area and real-time dust removal air volume together constitute the wet dust removal control feature. This feature provides a key basis for the subsequent control of the operation of the onboard wet dust removal blower, which can ensure that the dust removal equipment operates in the most optimized state under the current dust concentration, achieve efficient dust removal operations, and effectively reduce the dust concentration in the working environment.

[0090] In one possible implementation, step S700 further includes:

[0091] Step S700: When the anchor miner is switched to the detection mode, the onboard wet dust removal blower is turned off, and the belt conveyor is adjusted to a low-speed avoidance state.

[0092] Specifically, when it is detected that the anchor and miner has switched to detection mode, the onboard wet dust removal blower is immediately turned off. This is because in detection mode, dust removal operations are not required, and the open dust removal blower may cause airflow disturbances, affecting the detection equipment's accurate collection of surrounding environment data. At the same time, the belt conveyor will be adjusted to a low-speed avoidance state. This is because in detection mode, the focus of the anchor and miner is on detecting the geological conditions ahead, and the belt conveyor does not need to run at high speed to transport materials such as coal gangue. Adjusting the belt conveyor to a low speed can reduce the vibration and noise generated during its operation, reduce interference with detection operations, and avoid potential safety hazards caused by high-speed operation of the belt conveyor, ensuring that the entire detection process is carried out smoothly and safely, and providing accurate and reliable geological data support for subsequent excavation and support operations.

[0093] The second embodiment is based on the same inventive concept as the method for scheduling anchoring operations of a multi-machine coordinated anchoring machine in the previous embodiment. Figure 2 As shown, the present application provides a multi-machine coordinated anchoring and drilling machine anchoring operation scheduling system. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0094] The dynamic cutting parameter acquisition module 10 is used to obtain dynamic cutting parameters by monitoring the excavation parameters of the integrated excavator and anchoring machine in real time.

[0095] The load data extraction module 20 is used to extract the coal gangue load and load fluctuation rate from the weighing sensor of the belt conveyor using the monitoring window of the dynamic cutting parameters.

[0096] The initial dust concentration prediction value output module 30 is used to call the excavation environment information from the environmental sensor network, use the excavation environment information as interference, perform dust dispersion prediction based on the dynamic cutting parameters and coal gangue load, and output the initial dust concentration prediction value.

[0097] The speed gradient characteristic output module 40 is used to perform load response analysis according to the gangue load and load fluctuation rate, and output the speed gradient characteristic of the belt conveyor.

[0098] The modified dust concentration prediction value output module 50 is used to introduce the speed regulation gradient characteristics and load fluctuation rate, perform compensation correction on the initial dust concentration prediction value, and output a modified dust concentration prediction value.

[0099] The wet dust removal control feature output module 60 is configured to make a dust removal linkage decision based on the corrected dust concentration prediction value and output a wet dust removal control feature.

[0100] The multi-machine collaborative control module 70 is used to synchronously start the onboard wet dust removal blower and the belt conveyor when the integrated miner and anchor machine switches to the cutting mode, and adopt the wet dust removal control feature and speed gradient feature to perform multi-machine collaborative control of the onboard wet dust removal blower and the belt conveyor.

[0101] Furthermore, the system is also used to implement the following functions:

[0102] Dynamic optimization of anchoring parameters is performed according to the dynamic cutting parameters, and dynamic anchor density and support lag distance are output; when the displacement distance of the belt conveyor reaches the support lag distance, the anchor drill is activated to implant anchors with the dynamic anchor density as the implantation constraint, wherein, during the displacement process of the belt conveyor, the wet dust removal control feature and speed gradient feature are used to coordinately control the onboard wet dust removal blower and the belt conveyor.

[0103] Furthermore, the system is also used to implement the following functions:

[0104] The operation mode is determined according to the position of the sliding base of the anchoring and digging machine; when the anchoring and digging machine is in the cutting mode, the built-in sensing unit of the anchoring and digging machine is activated, wherein the built-in sensing unit is respectively communicated with the speed sensor, the hydraulic pressure sensor, and the hardness fusion sensor; after activation, the built-in sensing unit respectively receives the real-time cutting head speed, real-time propulsion pressure, and real-time rock formation hardness transmitted back by the speed sensor, the hydraulic pressure sensor, and the hardness fusion sensor, wherein the real-time cutting head speed, real-time propulsion pressure, and real-time rock formation hardness constitute the dynamic cutting parameters.

[0105] Furthermore, the system is also used to implement the following functions:

[0106] The excavation environment information is obtained by performing environmental perception through the roof displacement sensor, gas sensor and dust sensor integrated in the environmental sensing network, wherein the excavation environment information includes real-time roof displacement, roadway gas concentration and roadway dust concentration; a dust concentration regression function is pre-constructed, and the dynamic cutting parameters, coal gangue load and excavation environment information are input into the dust concentration regression function to perform dust dispersion prediction and output a real-time dust concentration prediction value; the real-time dust concentration prediction value is subjected to mode collaborative compensation according to the real-time mode of the drilling and anchoring machine, and the initial dust concentration prediction value is output.

[0107] Furthermore, the system is also used to implement the following functions:

[0108] According to the real-time cutting head rotation speed, real-time propulsion pressure and real-time rock formation hardness, dynamic matching of support characteristics is performed to obtain the first support reduction scale, the second support reduction scale and the third support reduction scale; the maximum reduction scale is extracted from the first support reduction scale, the second support reduction scale and the third support reduction scale as the real-time support reduction scale; the real-time support reduction scale is used to update the real-time lag distance to obtain the support lag distance; according to the real-time rock formation hardness and the tunnel gas concentration, the anchor spacing characteristics are matched to output the dynamic anchor density.

[0109] Furthermore, the system is also used to implement the following functions:

[0110] By calling local historical records, multiple sample diffuse dust concentrations are obtained; taking the multiple sample diffuse dust concentrations as the starting point, a preset sliding time window is used to trace back historical associated data to obtain multiple sample dust associated data, wherein the sample dust associated data includes sample cutting speed, sample propulsion pressure, sample rock formation hardness, sample load, sample roof displacement, sample gas concentration, and sample environmental dust concentration; using the multiple sample diffuse dust concentrations as output values ​​and the multiple sample dust associated data as input values, a multivariate regression analysis is performed to obtain the dust concentration regression function; using M times the sliding time window as a function update period, the dust concentration regression function is dynamically updated.

[0111] Furthermore, the system is also used to implement the following functions:

[0112] Interactively obtain multiple sample dust compensation coefficients of multiple sample speed gradient intervals, wherein the multiple sample speed gradient intervals are adjacent and connected; interactively obtain multiple sample dust floating intervals of multiple sample volatility grades, wherein the multiple sample volatility grades are adjacent and connected; use the speed gradient feature to traverse the multiple sample speed gradient intervals to map and extract real-time dust compensation coefficients, and use the load volatility to traverse the multiple sample volatility grades to map and extract real-time dust floating intervals; apply the real-time dust compensation coefficient to update and calculate the initial dust concentration prediction value to obtain an updated dust concentration prediction value; use the real-time dust floating interval to correct the updated dust concentration prediction value to obtain a dust concentration prediction interval; perform a maximum value call on the dust concentration prediction interval to obtain the corrected dust concentration prediction value.

[0113] Furthermore, the system is also used to implement the following functions:

[0114] Interactively obtain multiple sample dust removal area restrictions and multiple sample dust removal air volume restrictions for multiple sample environmental dust concentrations; obtain a dust removal related information library by linear interpolation expansion of the multiple sample environmental dust concentrations, the multiple sample dust removal area restrictions and the multiple sample dust removal air volume restrictions; use the corrected dust concentration prediction value to traverse the dust removal related information library to obtain real-time dust removal areas and real-time dust removal air volume, wherein the real-time dust removal areas and real-time dust removal air volume constitute the wet dust removal control characteristics.

[0115] Furthermore, the system is also used to implement the following functions:

[0116] When the anchor miner is switched to the detection mode, the onboard wet dust removal blower is turned off, and the belt conveyor is adjusted to a low-speed avoidance state.

[0117] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0119] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A multi-machine coordinated anchoring and digging machine anchoring operation scheduling method, characterized in that: The method comprises: By monitoring the excavation parameters of the integrated excavator in real time, dynamic cutting parameters can be obtained; Using the monitoring window of the dynamic cutting parameters, the gangue load and load fluctuation rate are extracted from the weighing sensor of the belt conveyor; After calling the excavation environment information from the environmental sensor network, the excavation environment information is used as interference, and dust dispersion is predicted based on the dynamic cutting parameters and the coal gangue load, and an initial dust concentration prediction value is output; Performing load response analysis based on the gangue load and load fluctuation rate, and outputting a speed regulation gradient characteristic of the belt conveyor; Introducing the speed regulation gradient characteristics and load fluctuation rate, performing compensation correction on the initial dust concentration prediction value, and outputting a corrected dust concentration prediction value; Make a dust removal linkage decision based on the corrected dust concentration prediction value and output a wet dust removal control feature; When the anchor miner is switched to the cutting mode, the onboard wet dust removal blower and the belt conveyor are started synchronously and the wet dust removal control feature and the speed gradient feature are used to perform multi-machine coordinated control of the onboard wet dust removal blower and the belt conveyor; Introducing the speed gradient characteristic and the load fluctuation rate, performing compensation correction on the initial dust concentration prediction value, and outputting a corrected dust concentration prediction value, the method includes: Interactively obtaining a plurality of sample dust compensation coefficients of a plurality of sample speed adjustment gradient intervals, wherein the plurality of sample speed adjustment gradient intervals are adjacent to each other; Interactively obtaining a plurality of sample dust floating intervals of a plurality of sample volatility grades, wherein the plurality of sample volatility grades are adjacent and connected; The speed gradient feature is used to traverse the plurality of sample speed gradient intervals to map and extract a real-time dust compensation coefficient, and the load fluctuation rate is used to traverse the plurality of sample fluctuation rate grades to map and extract a real-time dust floating interval; Applying the real-time dust compensation coefficient to update the initial dust concentration prediction value to obtain an updated dust concentration prediction value; Using the real-time dust floating interval to correct the updated dust concentration prediction value to obtain a dust concentration prediction interval; The maximum value of the dust concentration prediction interval is called to obtain the corrected dust concentration prediction value.

2. The method for scheduling anchoring operations of a multi-machine coordinated anchoring and drilling machine according to claim 1, characterized in that: The method further comprises: Perform dynamic optimization of anchoring parameters according to the dynamic cutting parameters, and output dynamic anchor density and support lag distance; When the displacement distance of the belt conveyor reaches the support lag distance, the anchor drill is activated to perform anchor implantation with the dynamic anchor density as the implantation constraint, wherein, during the displacement process of the belt conveyor, the wet dust removal control feature and the speed regulation gradient feature are used to perform coordinated control of the onboard wet dust removal blower and the belt conveyor.

3. The method for scheduling anchoring operations of a multi-machine coordinated anchoring and drilling machine according to claim 2, characterized in that: By real-time monitoring of the excavation parameters of the anchoring and digging machine, dynamic cutting parameters are obtained, and the method includes: Determine the operation mode according to the position of the sliding base of the anchor and digger machine; When the anchor miner is in a cutting mode, a built-in sensing unit of the anchor miner is activated, wherein the built-in sensing unit is communicatively connected to a rotation speed sensor, a hydraulic pressure sensor, and a hardness fusion sensor respectively; After activation, the built-in sensing unit receives the real-time cutting head speed, real-time propulsion pressure, and real-time rock formation hardness transmitted back by the speed sensor, hydraulic pressure sensor, and hardness fusion sensor, respectively. The real-time cutting head speed, real-time propulsion pressure, and real-time rock formation hardness constitute the dynamic cutting parameters.

4. The method for scheduling anchoring operations of a multi-machine coordinated anchoring and digging machine according to claim 3, characterized in that: After calling the excavation environment information from the environmental sensor network, using the excavation environment information as interference, performing dust dispersion prediction based on the dynamic cutting parameters and the coal gangue load, and outputting an initial dust concentration prediction value, the method includes: The excavation environment information is obtained by performing environmental sensing using a roof displacement sensor, a gas sensor, and a dust sensor integrated in the environmental sensing network, wherein the excavation environment information includes real-time roof displacement, roadway gas concentration, and roadway dust concentration; Pre-constructing a dust concentration regression function, and inputting the dynamic cutting parameters, gangue load and tunneling environment information into the dust concentration regression function to perform dust dispersion prediction and output a real-time dust concentration prediction value; Mode collaborative compensation is performed on the real-time dust concentration prediction value according to the real-time mode of the integrated miner and anchor machine, and the initial dust concentration prediction value is output.

5. The method for scheduling anchoring operations of a multi-machine coordinated anchoring and digging machine according to claim 4, characterized in that: Dynamic optimization of anchoring parameters is performed according to the dynamic cutting parameters, and dynamic anchor density and support lag distance are output. The method includes: Dynamically matching support characteristics according to the real-time cutting head rotation speed, real-time propulsion pressure and real-time rock formation hardness to obtain a first support shrinkage scale, a second support shrinkage scale and a third support shrinkage scale; Extracting a maximum shrinkage scale from the first support shrinkage scale, the second support shrinkage scale, and the third support shrinkage scale as a real-time support shrinkage scale; The real-time support shrinkage scale is used to update the real-time lag distance to obtain the support lag distance; Anchor bolt spacing characteristics are matched according to the real-time rock formation hardness and roadway gas concentration, and the dynamic anchor bolt density is output.

6. The method for scheduling anchoring operations of a multi-machine coordinated anchoring and digging machine according to claim 4, characterized in that: A dust concentration regression function is pre-built, and the method includes: By calling local historical records, the diffuse dust concentration of multiple samples is obtained; Taking the diffuse dust concentrations of the multiple samples as a starting point, a preset sliding time window is used to perform historical correlation data backtracking to obtain multiple sample dust correlation data, wherein the sample dust correlation data includes sample cutting speed, sample propulsion pressure, sample rock formation hardness, sample load, sample roof displacement, sample gas concentration, and sample ambient dust concentration; Using the diffuse dust concentrations of the multiple samples as output values ​​and the dust-related data of the multiple samples as input values, a multivariate regression analysis is performed to obtain the dust concentration regression function; The dust concentration regression function is dynamically updated by using M times the sliding time window as a function update period.

7. The method for scheduling anchoring operations of a multi-machine coordinated anchoring and drilling machine according to claim 1, characterized in that: Performing dust removal linkage decision-making based on the corrected dust concentration prediction value and outputting wet dust removal control characteristics, the method includes: Interactively obtain multiple sample dust removal area limits and multiple sample dust removal air volume limits for multiple sample environmental dust concentrations; Obtaining a dust removal related information library by linearly interpolating and expanding the plurality of sample environmental dust concentrations, the plurality of sample dust removal area restrictions, and the plurality of sample dust removal air volume restrictions; The corrected dust concentration prediction value is used to traverse the dust removal associated information library to obtain a real-time dust removal area and a real-time dust removal air volume, wherein the real-time dust removal area and the real-time dust removal air volume constitute the wet dust removal control feature.

8. The method for scheduling anchoring operations of a multi-machine coordinated anchoring and digging machine according to claim 1, characterized in that: When the anchor miner is switched to the detection mode, the onboard wet dust removal blower is turned off, and the belt conveyor is adjusted to a low-speed avoidance state.

9. The multi-machine coordinated anchoring and digging machine anchoring operation scheduling system is characterized by: The system is used to implement the multi-machine coordinated anchoring operation scheduling method of the anchoring machine according to any one of claims 1 to 8, and the system includes: A dynamic cutting parameter acquisition module is used to obtain dynamic cutting parameters by monitoring the excavation parameters of the integrated excavator in real time; A load data extraction module is used to extract the coal gangue load and load fluctuation rate from the weighing sensor of the belt conveyor using the monitoring window of the dynamic cutting parameters; An initial dust concentration prediction value output module is used to call the excavation environment information from the environmental sensor network, use the excavation environment information as interference, perform dust dispersion prediction based on the dynamic cutting parameters and coal gangue loading, and output an initial dust concentration prediction value; A speed gradient characteristic output module is used to perform load response analysis based on the gangue load and load fluctuation rate, and output the speed gradient characteristic of the belt conveyor; a modified dust concentration prediction value output module, configured to introduce the speed regulation gradient characteristics and load fluctuation rate, perform compensation correction on the initial dust concentration prediction value, and output a modified dust concentration prediction value; a wet dust removal control feature output module, configured to make a dust removal linkage decision based on the corrected dust concentration prediction value and output a wet dust removal control feature; A multi-machine collaborative control module is used to synchronously start the onboard wet dust removal blower and the belt conveyor when the integrated miner and anchor machine switches to the cutting mode, and adopt the wet dust removal control feature and speed regulation gradient feature to perform multi-machine collaborative control of the onboard wet dust removal blower and the belt conveyor.

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