Mine master-slave machine inspection path planning method based on intelligent scheduling

Abstract

The invention discloses a mine primary and secondary aircraft inspection path planning method based on intelligent scheduling. The method comprises the following steps: constructing a gradient grading model based on slope gradient data and friction coefficient data of a mine environment; on the basis of time optimization, processing the gradient grading model and the climbing ability parameters of the primary and secondary aircrafts to obtain a preliminary inspection path sequence; on the basis of production progress information and vehicle scheduling information in a mine operation state, peak operation avoidance adjustment is performed on the preliminary inspection path sequence to obtain an updated path sequence; based on the update path sequence and the inspection time sequence data, adopting scheduling coprocessing to generate a master-slave deployment instruction; on the basis of the master-slave deployment instruction, obtaining change trend data by monitoring the change of an operation state in real time, and updating the inspection time sequence by adopting the change trend data; and on the basis of a comparison result of the change trend data and a preset threshold value, fusing the production progress information to regenerate an optimal path sequence.

Description

Technical Field

[0001] This invention belongs to the field of intelligent mine inspection and path planning technology, and particularly relates to a mine mother-daughter machine inspection path planning method based on intelligent scheduling. Background Technology

[0002] Mine inspection, as a crucial link in ensuring production safety and equipment operation, occupies an indispensable position in modern mining management. Especially in open-pit or underground mine environments, inspection work directly affects operational safety and production efficiency. Currently, mine inspections largely rely on manual labor or simple mechanical equipment. Although some methods attempt to introduce automated equipment, they still generally lack comprehensive consideration of complex terrain features and dynamic operating environments. Traditional methods often overlook the direct impact of terrain slope changes on the mobility of inspection equipment during path planning, and fail to dynamically adjust to the real-time production rhythm of the mine. This results in insufficient adaptability of inspection paths in complex terrain, posing safety hazards, and making it difficult to effectively avoid peak production areas, thus impacting overall operational efficiency.

[0003] Existing technologies for mine inspection route planning suffer from the following main problems: First, they fail to fully integrate key terrain parameters such as slope gradient and ground friction coefficient to construct accurate slope classification models, leading to potential loss of control or excessive energy consumption of inspection equipment when traversing steep slopes or unstable sections. Second, they lack a linkage mechanism with real-time mine production progress and vehicle scheduling information, making it impossible to dynamically avoid peak operating areas during inspections, which can easily cause conflicts between equipment and production vehicles. Third, they do not incorporate environmental indicators such as ventilation and safety into the route optimization closed loop, potentially leading to the planning of inspection routes in areas with insufficient air circulation, posing safety risks. Fourth, the route planning is static, unable to be replanned in real time based on changes in operational status, resulting in low inspection efficiency when the mine's production rhythm changes. These shortcomings limit the level of intelligence in mine inspections, making it difficult to achieve safe, efficient, and adaptive inspection operations. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a mine machine inspection path planning method based on intelligent scheduling, thereby resolving the issues present in the prior art.

[0005] To achieve the above objectives, this invention provides a method for planning inspection paths of mine mother-daughter machines based on intelligent scheduling, comprising:

[0006] A slope classification model is constructed based on slope gradient data and friction coefficient data of the mining environment;

[0007] Based on time optimization processing of the slope classification model and the climbing ability parameters of the mother-daughter machine, a preliminary inspection path sequence is obtained;

[0008] Based on the production progress information and vehicle scheduling information under the mine operation status, the preliminary inspection route sequence is adjusted to avoid peak operations to obtain an updated route sequence.

[0009] Based on the updated path sequence and inspection time sequence data, scheduling and collaborative processing are used to generate deployment instructions for the master and slave machines.

[0010] Based on the deployment instructions of the master and slave units, change trend data is obtained by real-time monitoring of changes in operational status, and the inspection sequence is updated using the change trend data;

[0011] Based on the comparison results between the trend data and the preset threshold, the optimal path sequence is regenerated by integrating the production progress information.

[0012] Optionally, the process of obtaining a preliminary inspection path sequence based on the slope grading model and the climbing ability parameters of the mother-daughter machine using time optimization includes:

[0013] Extract the slope classification information of different road segments from the slope grading model;

[0014] Based on the tilt angle classification information and the climbing ability parameters of the mother-daughter machine, the travel time of each road segment is calculated to obtain the road segment time value;

[0015] The optimized time series is obtained by processing the time values ​​of the road segment using dynamic programming.

[0016] Low-energy-consumption road segments are identified from the optimized time series based on road segment energy consumption assessment;

[0017] The preliminary inspection route sequence is determined based on low-energy road sections that meet the climbing ability parameters of the mother-daughter unit.

[0018] Optionally, the process of adjusting the initial inspection path sequence to avoid peak operations and obtain an updated path sequence includes:

[0019] Based on the production progress information obtained from the sensor network, the operation density in a specific area is determined to obtain a peak operation status identifier.

[0020] Based on the peak operation status indicator, select an alternative route that avoids the specific area from the preset route library, and determine the route adjustment plan;

[0021] Based on the path adjustment scheme and the vehicle scheduling information, a graph theory algorithm is used to calculate the path connectivity to obtain the modified sequence.

[0022] The updated path sequence is obtained by performing security verification on the modified sequence based on device monitoring data.

[0023] Optionally, after obtaining the updated path sequence, ventilation operation data is extracted based on the updated path sequence; the ventilation operation data is compared with a preset safety threshold to obtain a comparison result; and energy consumption optimization processing is performed on the updated path sequence based on the comparison result to obtain a safe path sequence.

[0024] Optionally, the process of performing energy consumption optimization on the updated path sequence to obtain a safe path sequence includes:

[0025] The ventilation operation data is obtained by monitoring the mine operation and using the air circulation index corresponding to the updated path sequence.

[0026] When the ventilation operation data is lower than the preset safety threshold, energy consumption optimization calculation is performed by summarizing and integrating the air volume of the ventilation equipment;

[0027] The updated path sequence is adjusted based on the energy consumption optimization calculation results, and the safe path sequence is obtained through iterative calculation.

[0028] Optionally, the process of generating deployment instructions for the parent and child machines using scheduling and collaborative processing includes:

[0029] Environmental area division information is extracted from the inspection time series data;

[0030] The environmental region division information is matched and time-series synchronized with the coordinate points of the safety path sequence using a task data fusion method to obtain fused path data;

[0031] Based on the fused path data, the smooth positioning coordinates of the mother machine are determined according to the slope threshold of the smooth area and the path continuity.

[0032] Obtain the attitude adjustment parameters of the slave machine corresponding to the smooth positioning coordinates of the mother machine;

[0033] Based on the attitude adjustment parameters of the slave unit, a cooperative communication protocol is used to transmit real-time obstacle avoidance signals, and the deployment position calibration value is adjusted based on the comparison result of the signals and the preset threshold to obtain calibration attitude data.

[0034] The deployment instructions for the mother and daughter machines are generated and output based on the calibration attitude data.

[0035] Optionally, the process of regenerating the optimal path sequence by integrating the production progress information based on the comparison results of the trend data and the preset threshold includes:

[0036] The trend data is compared with a preset threshold. If the trend data exceeds the preset threshold, key indicators are extracted from the production progress information.

[0037] The key indicators and the trend data are integrated using a weighted summation method to obtain fused information;

[0038] Based on the fused information, the Dijkstra algorithm is applied to calculate the path options and obtain a preliminary path sequence.

[0039] Based on the fluctuation response obtained from the inventory data, the node order of the preliminary path sequence is adjusted to generate the optimal path sequence.

[0040] Compared with the prior art, the present invention has the following advantages and technical effects:

[0041] This invention integrates mine topography data with real-time operational information to construct a slope grading model and optimize time based on equipment capabilities, enabling safe and low-energy passage of inspection routes in complex terrain. By introducing adaptive adjustment of production status and ventilation safety verification mechanisms, it dynamically avoids peak operating periods and poorly ventilated areas, significantly improving the safety and adaptability of the inspection process. Furthermore, by monitoring operational change trends in real time and triggering route replanning, the inspection system can respond to dynamic changes in the mine, ultimately achieving the technical effects of improving inspection efficiency and ensuring continuous and stable operation. Attached Figure Description

[0042] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0043] Figure 1 This is a flowchart of an embodiment of the present invention. Detailed Implementation

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0045] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0046] like Figure 1 As shown, this embodiment provides a method for planning inspection paths of mine mother-daughter machines based on intelligent scheduling, including the following steps:

[0047] S101. Slope data and friction coefficient data are collected from the mining environment through a sensor array. The slope data and friction coefficient data are processed by terrain modeling to obtain a slope classification model. The slope classification model contains inclination angle classification information for different road sections.

[0048] Slope and friction coefficient data are collected from the mining environment using a sensor array. These data are then integrated using a data fusion method to obtain an initial terrain dataset. For this initial terrain dataset, terrain modeling is used to generate a slope grading model, which includes inclination angle classification information and stability indices for different road segments. Road segment stability indices are extracted from the slope grading model. If the stability index is below a preset threshold, high-risk road segments are marked, resulting in a risk distribution map. The slope grading model is updated based on the risk distribution map, and the road segment stability indices and inclination angle classification information are fused to determine the safe path planning characteristics in the mining environment.

[0049] As a specific implementation of this embodiment, in a mining environment, when collecting slope data and friction coefficient data through a sensor array, an array system consisting of a multi-axis tilt sensor and a surface friction tester is deployed. These sensors are installed on unmanned vehicles or fixed monitoring stations to capture terrain changes in real time.

[0050] The modeling process first interpolates the dataset to generate a continuous surface, then classifies the dip angle information, and calculates the value of each road segment using formulas such as stability index = cos(θ)*μ. This model contains dip angle classification information and stability index for different road segments, and can intuitively display the potential hazards of mine paths.

[0051] The slope grading model extracts road segment stability indices. If the stability index is below a preset threshold, such as 0.4, the road segment is marked as high-risk, resulting in a risk distribution map. The slope grading model is updated based on the risk distribution map. When determining the safe path planning characteristics in the mining environment by integrating road segment stability indices and slope classification information, an iterative optimization algorithm, such as A* path planning, is used to incorporate risk data. For example, the weight of high-risk road segments is set to infinity. After integration, the model outputs the shortest safe path characteristics, such as a total length of 500 meters and an average slope of 10 degrees.

[0052] S102. Based on the slope classification model and the climbing ability parameters of the mother-daughter machine, calculate the travel time of each road segment, optimize the travel time, and determine the preliminary inspection route sequence. The preliminary inspection route sequence prioritizes low-energy-consumption road segments.

[0053] By combining a preset slope grading model with the climbing capacity parameters of the mother-daughter unit, the travel time for each road segment is calculated to obtain the segment time value. Based on the segment time value, time optimization processing is performed to determine the optimized time series. For the optimized time series, low-energy-consumption road segments are identified based on road segment energy consumption assessment. If a low-energy-consumption road segment meets the climbing capacity parameters of the mother-daughter unit, it is selected first. Inspection paths are obtained from the low-energy-consumption road segments to determine the preliminary inspection path sequence.

[0054] As a specific implementation method of this embodiment, the process of calculating the travel time of each road segment by combining a preset slope classification model with the climbing ability parameters of the mother-daughter locomotive is as follows: First, the slope angle classification information of different road segments is extracted from the model, and the mine path is divided into three categories: low slope, medium slope, and high slope. Among them, the slope angle of low slope is less than 15 degrees, medium slope is 15 to 30 degrees, and high slope is more than 30 degrees. Then, the climbing ability parameters of the mother-daughter locomotive are incorporated into the calculation. The maximum climbing angle of the mother-daughter locomotive is 25 degrees and the average speed is 5 kilometers per hour. The travel time on low slope road segments is calculated by dividing the road segment length by the speed, while the speed reduction factor needs to be considered on medium slope road segments, and additional time adjustment is required for high slope road segments.

[0055] When determining the optimal time series based on the time values ​​of these road segments, a dynamic programming method is used. First, the time values ​​are sorted and potential bottleneck road segments are identified. The total path is divided into multiple sub-paths. The time of each sub-path is summed and minimized. Assuming there are three road segments A, B, and C, with time values ​​of 0.3, 0.5, and 0.7 hours respectively, the order of the segments is adjusted or adjacent road segments are merged through optimization algorithms to generate an optimized sequence from the starting point to the end point. For example, merging B and C reduces the total time to 1.1 hours, ensuring that the sequence reflects the path with the minimum overall time.

[0056] The steps for determining the inspection path from low-energy-consumption road sections are as follows: connect the eligible road sections into a sequence, select low-energy-consumption road sections 1, 3, and 5 to form a path from the mine entrance to the mining area, and ensure that the sequence is continuous without any breaks. For example, when connecting road section 1 to road section 3, it is necessary to verify that there are no high-risk gaps in between, and finally obtain the preliminary inspection path sequence.

[0057] S103. Obtain production progress information and vehicle scheduling information under the mine operation status. If the production progress information shows that a specific area is in a peak operation state, adjust the preliminary inspection path sequence to avoid the specific area and obtain an updated path sequence.

[0058] By acquiring production progress and vehicle scheduling information under mine operation status through a sensor network, the operation density of specific areas in the production progress information is determined to obtain a peak operation status indicator. If the peak operation status indicator shows that a specific area is at its peak, alternative route options are retrieved from a preset route library to determine a route adjustment plan that avoids the specific area. Based on the route adjustment plan, vehicle scheduling information is integrated, and graph theory algorithms are used to calculate the connectivity of the route sequence to obtain a modified sequence of preliminary inspection routes. The safety of the modified sequence is verified through equipment monitoring data to obtain an updated route sequence.

[0059] As a specific implementation of this embodiment, when the sensor detects that the number of mining machines in a certain area has increased to more than 10 per square kilometer, it will generate corresponding production progress data and record the vehicle scheduling route, such as the transportation route from mine A to warehouse B.

[0060] Determining the operational density of a specific area in production progress information involves analyzing collected data, such as calculating the activity level of equipment within that area per unit time. If the density exceeds a preset threshold, such as more than 15 devices operating per hour, it is marked as a peak operational state. This determination process includes data filtering and density calculation. First, the sensor data is cleaned to remove noise. Then, a density formula, such as the number of active devices divided by the area, is used to obtain the density value. This value is then compared with a threshold to generate an identifier, ensuring that the identifier accurately reflects the peak state.

[0061] If the peak operation status indicator shows that a specific area is in peak condition, such as area C being congested due to intensive mining, then alternative route options are retrieved from the preset route library. The route library is a database that stores various predefined routes, including main roads and branch roads. The system will filter routes that avoid area C, such as selecting an alternative route that detours through area D, thereby determining the route adjustment plan.

[0062] After integrating vehicle scheduling information based on the route adjustment plan, graph theory algorithms are used to calculate the connectivity of the route sequence. Graph theory algorithms, such as Dijkstra's algorithm, model the mining area routes as a graph, where nodes represent intersections and edges represent road segments with weights including distance and time. Then, the connected paths from the starting point to the ending point are calculated to ensure that the sequence has no breaks, thus obtaining the preliminary modified sequence of the inspection routes.

[0063] S104. Extract ventilation operation data from the updated path sequence, determine whether the ventilation operation data meets the preset safety threshold, and if the ventilation operation data is lower than the preset safety threshold, recalculate the energy consumption to optimize the updated path sequence and obtain a safe path sequence. The ventilation operation data is the mine air circulation index.

[0064] Ventilation operation data is extracted from the updated path sequence, and air circulation indicators are obtained through mine operation monitoring. It is determined whether the ventilation operation data meets a preset safety threshold. If the ventilation operation data is lower than the preset safety threshold, energy consumption optimization calculations are performed by aggregating and integrating equipment airflow data. The optimization sequence is adjusted based on the integrated equipment airflow results, and the updated path sequence is recalculated iteratively. A safe path sequence is obtained from the adjusted sequence.

[0065] In a specific implementation of this embodiment, after receiving the updated path sequence generated in S103, ventilation operation data corresponding to each path node is extracted from the sequence. This data is a comprehensive indicator reflecting the air circulation status of the mine. The judgment process uses a preset safety threshold for comparison. The preset safety threshold is dynamically set according to the safety standards of different work areas. Once the system determines that the ventilation operation data of a certain road segment is lower than its corresponding preset safety threshold, it determines that there is a safety hazard in that road segment and immediately triggers the path re-optimization mechanism.

[0066] The optimization process is a multi-objective iterative process. First, the system aggregates real-time output data from all ventilation equipment (such as main fans and auxiliary fans) by summarizing airflow data. Based on this, energy consumption optimization calculations are performed to evaluate the total system energy consumption under different equipment airflow configurations. Second, based on the integrated equipment airflow results, the system adjusts weakly ventilated sections in the original "updated path sequence," generating multiple path modification schemes. Then, these schemes are recalculated and evaluated iteratively: in each iteration, the adjusted path is simulated, and its corresponding ventilation operation data and system energy consumption are recalculated until a path scheme is found where the ventilation operation data of all nodes meets the preset safety threshold and the system energy consumption is relatively optimal. After multiple iterative adjustments, a safe path sequence that meets ventilation safety and energy consumption requirements is finally output.

[0067] S105. The safety path sequence and inspection timing data are processed by scheduling and coordination to generate a master-slave deployment instruction. The master-slave deployment instruction specifies that the master machine is positioned in a flat area and coordinates the attitude adjustment of the slave machine.

[0068] Environmental area division information is obtained from inspection time-series data. A task data fusion method is used to integrate the environmental area division information and the safety path sequence by matching coordinate points and synchronizing time, resulting in fused path data. For this fused path data, scheduling and collaborative processing determines the smooth positioning coordinates of the mother machine based on the slope threshold and path continuity of the smooth area, obtaining the slave machine attitude adjustment parameters corresponding to these coordinates. Based on the slave machine attitude adjustment parameters, a collaborative communication protocol is used to transmit real-time obstacle avoidance signals. If the real-time obstacle avoidance signal exceeds a preset threshold, the deployment position calibration value is adjusted and corrected using an offset vector to obtain calibrated attitude data. Using this calibrated attitude data, a multi-machine collaborative verification sequence is generated under a command generation mechanism. The matching degree between the multi-machine collaborative verification sequence and the optimized attitude parameters is determined by comparing attitude vectors. The matching degree is obtained, and deployment commands for both the mother and slave machines are output.

[0069] As a specific implementation of this embodiment, environmental area division information (e.g., dividing the mine into high-risk areas, low-risk areas, and flat areas) is obtained from the inspection time series data. Subsequently, a task data fusion method is used to match the environmental area division information with the coordinates of each point in the safety path sequence, and time synchronization is used to ensure that the path planning matches the task time window, thereby generating spatiotemporally consistent fused path data.

[0070] Based on the fused path data, the scheduling and collaborative processing mechanism begins operation. This mechanism identifies suitable continuous, gentle road sections within the fused path for the mother machine to remain, based on the slope threshold for flat areas and the principle of path continuity, and calculates the precise flat positioning coordinates of the mother machine. Simultaneously, based on the mother machine's positioning and the path segments that the daughter machines need to inspect, the required attitude adjustment parameters for each daughter machine are derived to ensure stable operation and effective observation in complex terrain.

[0071] The collaborative process relies on reliable communication and real-time feedback. The system adjusts the attitude parameters of the slave units and transmits real-time obstacle avoidance signals (such as the distance to obstacles ahead) between the master and slave units using a collaborative communication protocol. The central scheduler continuously monitors these signals to determine if they exceed preset thresholds. If a signal exceeds the threshold, indicating a collision risk, the system immediately calculates an offset vector to correct the slave unit's predetermined deployment position calibration value, thus obtaining updated calibrated attitude data.

[0072] Finally, the instruction generation system operates based on the calibration attitude data. First, it generates a multi-machine collaborative verification sequence that simulates the coordinated actions of multiple machines over a future period. By comparing the attitude vectors in the simulated sequence with the theoretical optimized parameters, the degree of matching between the two is determined. When the degree of matching reaches a reliable level, the system officially outputs deployment instructions for the mother and daughter machines, containing the final positioning coordinates, attitude parameters, and action commands, guiding the mother and daughter systems to begin safe and collaborative inspection operations.

[0073] S106. Monitor changes in operational status in real time according to the deployment instructions of the master and slave units, obtain trend data of changes, and update the inspection sequence through the trend data of changes. The trend data of changes includes records of fluctuations in operational variables.

[0074] Initial operational status values ​​are obtained from the parent and child deployment instructions. Status changes are monitored using these initial values ​​to obtain variable fluctuation records. These variable fluctuation records are then integrated with equipment load data. A load data balancing distribution method is adopted, and if the load exceeds a preset threshold, the record priority is adjusted to obtain trend data. Fluctuation peak features are extracted from the trend data, and the trend direction is determined using these peak features to obtain time-series update parameters. The inspection sequence is adjusted using these time-series update parameters to obtain an optimized inspection sequence.

[0075] In a specific implementation of this embodiment, initial operational status values ​​are parsed from the deployment instructions of the master and slave units, serving as the monitoring benchmark. These initial values ​​include key parameters such as the desired location, attitude, and power consumption rate of the equipment. Subsequently, the system monitors changes in operational status in real time through a sensor network embedded in the equipment and the environment. The real-time collected data (such as actual coordinates, actual power consumption, and sensor readings) is continuously compared with the initial values, the deviation is calculated and recorded, forming a variable fluctuation record containing timestamps.

[0076] Next, the system integrates and analyzes the variable fluctuation records with device load data (such as CPU utilization and communication load) from the device itself. This data is processed using a load balancing method to determine whether the overall or local load exceeds a preset threshold. If it does, the priority of different fluctuation records is adjusted accordingly, highlighting key changes that may lead to system bottlenecks or risks, thereby extracting trend data that better reflects the system's operational status.

[0077] The trend analysis module performs in-depth data mining on changing trends. It extracts fluctuation peak characteristics to identify abnormal spikes or regular peaks in the data. Peak characteristics are used to determine the trend direction, such as whether the system load is rising, falling, or fluctuating periodically. Based on this trend direction, the system derives specific time-series update parameters, such as suggesting adjustments to the execution interval, sequence, or duration of inspection tasks.

[0078] Finally, the system adjusts the inspection sequence by updating the timing parameters. Based on these parameters, the original inspection task schedule is optimized, for example, by extending the task interval during high-load periods and executing more tasks in parallel during stable system periods, thus obtaining an optimized inspection sequence that is more adapted to the real-time equipment status and system load.

[0079] S107. Determine whether the trend data exceeds a preset threshold. If the trend data exceeds the preset threshold, then regenerate the optimal path sequence by integrating the production progress information.

[0080] The algorithm acquires trend data and compares it with a preset threshold to determine if it exceeds the threshold. If the threshold is exceeded, key indicators are extracted from production progress information. A weighted summation is used to integrate these key indicators with the trend data, where the weighted summation involves assigning weights to the indicators and trends before adding them together to determine the fusion information. For this fusion information, Dijkstra's algorithm is applied to calculate path options. Dijkstra's algorithm uses the fusion information as graph edge weights and searches for the shortest path by relaxing edge values ​​node by node from the starting point, obtaining a preliminary path sequence. The preliminary path sequence is adjusted by obtaining fluctuation responses from inventory data, where the fluctuation response is used to correct the order of path nodes based on inventory changes, generating the optimal path sequence.

[0081] As a specific implementation of this embodiment, the system continuously analyzes the trend data from S106. This data integrates multi-dimensional information such as equipment performance fluctuations and load changes. The system determines whether the trend data exceeds a preset threshold. This preset threshold is set based on historical operating data and safety requirements, and is used to define the boundary between normal operation and abnormal fluctuations.

[0082] If the trend data is determined to exceed a preset threshold, a global path replanning is triggered. The replanning process deeply integrates real-time production progress information. The system obtains information from the production scheduling center and extracts key indicators, such as the operational density of each area, the real-time location of transport vehicles, and the planned large-scale operations.

[0083] The system employs a weighted summation method, assigning weights to key indicators and out-of-target trend data, and then integrating and calculating them to determine a new set of integrated information reflecting the current overall operating costs. This information quantifies the traffic difficulty, safety risks, and impact on production of different road segments at the current moment.

[0084] The system uses the fused information as input weights and applies Dijkstra's algorithm to calculate path options. This algorithm uses the mine road network as a graph, with the fused information as edge weights. Starting from the origin, it relaxes edge values ​​node by node, searching for the path with the lowest global cost, thus obtaining a preliminary path sequence.

[0085] The system obtains fluctuation responses from inventory data. Here, inventory data and fluctuation responses can be broadly interpreted as changes in the surplus or shortage of materials, equipment, or resources at various stages of the mining production system. Based on these fluctuation responses (e.g., a work stoppage in a certain area due to material shortages, reducing the risk of obstruction), the system fine-tunes the node order or path selection of the initial path sequence.

[0086] Ultimately, by combining dynamic threshold judgment, production information fusion, graph algorithm search, and inventory response adjustment, the system regenerates an optimal path sequence that fully adapts to the latest operating status and has the best overall cost, thus completing a complete dynamic path replanning closed loop.

[0087] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for planning inspection paths of mine mother-daughter machines based on intelligent scheduling, characterized in that, Includes the following steps: A slope classification model is constructed based on slope gradient data and friction coefficient data of the mining environment; Based on time optimization processing of the slope classification model and the climbing ability parameters of the mother-daughter machine, a preliminary inspection path sequence is obtained; Based on the production progress information and vehicle scheduling information under the mine operation status, the preliminary inspection route sequence is adjusted to avoid peak operations to obtain an updated route sequence. Based on the updated path sequence and inspection time sequence data, scheduling and collaborative processing are used to generate deployment instructions for the master and slave machines. Based on the deployment instructions of the master and slave units, change trend data is obtained by real-time monitoring of changes in operational status, and the inspection sequence is updated using the change trend data; Based on the comparison results between the trend data and the preset threshold, the optimal path sequence is regenerated by integrating the production progress information.

2. The method for planning inspection paths of mine mother-daughter machines based on intelligent scheduling according to claim 1, characterized in that, The process of obtaining the preliminary inspection path sequence based on the slope classification model and the climbing ability parameters of the mother-daughter machine using time optimization includes: Extract the slope classification information of different road segments from the slope grading model; Based on the tilt angle classification information and the climbing ability parameters of the mother-daughter machine, the travel time of each road segment is calculated to obtain the road segment time value; The optimized time series is obtained by processing the time values ​​of the road segment using dynamic programming. Low-energy-consumption road segments are identified from the optimized time series based on road segment energy consumption assessment; The preliminary inspection route sequence is determined based on low-energy road sections that meet the climbing ability parameters of the mother-daughter unit.

3. The method for planning inspection paths of mine mother-daughter machines based on intelligent scheduling according to claim 1, characterized in that, The process of adjusting the preliminary inspection route sequence to avoid peak operations and obtain an updated route sequence includes: Based on the production progress information obtained from the sensor network, the operation density in a specific area is determined to obtain a peak operation status identifier. Based on the peak operation status indicator, select an alternative route that avoids the specific area from the preset route library, and determine the route adjustment plan; Based on the path adjustment scheme and the vehicle scheduling information, a graph theory algorithm is used to calculate the path connectivity to obtain the modified sequence. The updated path sequence is obtained by performing security verification on the modified sequence based on device monitoring data.

4. The method for planning inspection paths of mine mother-daughter machines based on intelligent scheduling according to claim 3, characterized in that, After obtaining the updated path sequence, ventilation operation data is extracted based on the updated path sequence; the ventilation operation data is compared with a preset safety threshold to obtain a comparison result; and energy consumption optimization processing is performed on the updated path sequence based on the comparison result to obtain a safe path sequence.

5. The method for planning inspection paths of mine mother-daughter machines based on intelligent scheduling according to claim 4, characterized in that, The process of optimizing the updated path sequence for energy consumption to obtain a safe path sequence includes: The ventilation operation data is obtained by monitoring the mine operation and using the air circulation index corresponding to the updated path sequence. When the ventilation operation data is lower than the preset safety threshold, energy consumption optimization calculation is performed by summarizing and integrating the air volume of the ventilation equipment; The updated path sequence is adjusted based on the energy consumption optimization calculation results, and the safe path sequence is obtained through iterative calculation.

6. The method for planning inspection paths of mine mother-daughter machines based on intelligent scheduling according to claim 5, characterized in that, The process of generating deployment instructions for the host and slave machines using scheduling and collaborative processing includes: Environmental area division information is extracted from the inspection time series data; The environmental region division information is matched and time-series synchronized with the coordinate points of the safety path sequence using a task data fusion method to obtain fused path data; Based on the fused path data, the smooth positioning coordinates of the mother machine are determined according to the slope threshold of the smooth area and the path continuity. Obtain the attitude adjustment parameters of the slave machine corresponding to the smooth positioning coordinates of the mother machine; Based on the attitude adjustment parameters of the slave unit, a cooperative communication protocol is used to transmit real-time obstacle avoidance signals, and the deployment position calibration value is adjusted based on the comparison result of the signals and the preset threshold to obtain calibration attitude data. The deployment instructions for the mother and daughter machines are generated and output based on the calibration attitude data.

7. The method for planning inspection paths of mine mother-daughter machines based on intelligent scheduling according to claim 1, characterized in that, The process of regenerating the optimal path sequence by integrating the production progress information based on the comparison results of the changing trend data and the preset threshold includes: The trend data is compared with a preset threshold. If the trend data exceeds the preset threshold, key indicators are extracted from the production progress information. The key indicators and the trend data are integrated using a weighted summation method to obtain fused information; Based on the fused information, the Dijkstra algorithm is applied to calculate the path options and obtain a preliminary path sequence. Based on the fluctuation response obtained from the inventory data, the node order of the preliminary path sequence is adjusted to generate the optimal path sequence.

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