Operation and maintenance service cloud platform of mine intelligent system

Abstract

The invention relates to the technical field of operation and maintenance management, in particular to an operation and maintenance service cloud platform of a mine intelligent system, which comprises a node pressure monitoring module, a path task mapping module, a conflict density identification module, a time window adjustment module and an operation and maintenance scheduling matching module. According to the method, a node group consistent in direction is extracted through a pressure difference value and a flowing direction, a dynamic response relationship is constructed, the real-time performance and accuracy of path extraction are improved, path and task mapping is combined with a direction label, the adaptation degree of a task and a physical path is enhanced, task occupancy rate calculation supports high-density section identification, accurate avoidance of a scheduling peak is realized, and the scheduling efficiency is improved. Starting time and ending time extend to form candidate windows, task execution cycles are reconstructed according to density levels, task scheduling flexibility is enhanced, response units are screened according to reachable paths and path conflict conditions in resource allocation, and dual optimization of spatial distance and path safety is achieved; and the real-time adaptability, the path configuration reasonability and the resource calling efficiency of the scheduling scheme are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance management, and particularly to an operation and maintenance service cloud platform for a mine intelligent system. Background Art

[0002] The technical field of operation and maintenance management includes the maintenance and management of industrial equipment, infrastructure, and information systems. By formulating maintenance strategies, implementing preventive maintenance, real-time monitoring of equipment operation status, analyzing operation data, etc., it ensures the stable operation of the system and the effective utilization of resources. The core contents are resource configuration management, status monitoring and fault warning, operation and maintenance process control, information feedback and update, etc. It usually relies on data acquisition systems, remote communication networks, background data processing and analysis platforms, etc. for systematic collaborative operation. The overall technology covers multiple links such as data perception, information processing, operation and maintenance decision-making, work order scheduling, and personnel management. The purpose is to standardize, digitize, and visualize complex operation and maintenance tasks through a unified platform, and it is widely used in high equipment density industries such as energy, power, transportation, and manufacturing.

[0003] Among them, the operation and maintenance service cloud platform for a mine intelligent system refers to an integrated management system for multi-source data acquisition, operation status recognition, and remote maintenance scheduling of the coal mine underground production system. It is mainly used to support the informatization and collaborative processing of equipment management and maintenance tasks in the coal mine scenario, covering real-time acquisition of equipment status data in the coal mine underground, status assessment based on operation parameters, generation of operation tasks according to maintenance rules, data upload and centralized management through the cloud platform, distribution and recording of operation and maintenance tasks. It obtains operation data through a sensor network, conducts health assessment based on the status calculation method of preset indicators, drives the formulation of task plans with operation task generation rules, realizes platform data interaction through remote communication methods, and supports task assignment and progress tracking according to the permission role division method to complete the overall intelligent operation and maintenance service of the mine system.

[0004] During the existing mine operation and maintenance service process, in the process of handling path scheduling and task arrangement, path recognition is not dynamically feedback based on the operation situation, and the selection of path segments lacks real-time pressure state guidance, resulting in the task path being unable to reflect the stress changes in the actual environment, there are deviations in the use planning of path segments, affecting the stable realization of the scheduling goal. The task path and the scheduling task are mostly bound through fixed rules or static configurations, which cannot reflect the real-time pressure distribution of the task execution on the path resources, making the path resource configuration lack a basis and generating the risk of too high resource scheduling concentration. The path usage time is not refined to the block dimension, and the task occupancy situation cannot be mapped to the specific scheduling period, resulting in the inability to accurately evaluate the possible time coincidence and path conflict in resource scheduling. During the setting of the scheduling cycle, the adjustment of the task execution time period lacks a dynamic avoidance mechanism and does not have the ability to identify and respond to path segment resource conflicts, resulting in execution delays and resource contention phenomena. In terms of equipment and personnel resource allocation, only whether it is idle is used as the basis, ignoring physical space location and path accessibility factors, reducing resource response efficiency, increasing the risk of path overlap interference, and unable to meet the accuracy and response speed requirements in high-frequency scheduling scenarios. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose an operation and maintenance service cloud platform for a mine intelligent system.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solutions: An operation and maintenance service cloud platform for a mine intelligent system includes: The node pressure monitoring module collects the node pressure time series within the mine operation task cycle, calculates the pressure difference between adjacent nodes, combines the fluid flow direction to analyze the change trend between nodes, and establishes a node pressure response correlation table; The path task mapping module extracts the underground path segment structure information according to the node pressure response correlation table, assigns direction labels to the path segments, and extracts the operation task numbers bound to the path segments in combination with the set operation and maintenance task schedule to obtain a path segment-task mapping list; The conflict density identification module counts the repeated scheduling times of path segments according to the path segment-task mapping list, calculates the task occupancy rate of path segments in each time block, determines the high-density path segments, and generates a path segment conflict density level table; The time window adjustment module extracts the path segments of the currently scheduled task based on the path segment conflict density level table, determines the path segment numbers with high-density sections, uses the original planned start and end times of the task as the basic time window, extends forward and backward to form front and rear candidate time windows, calculates the task coincidence degree and density level of the path segments within the candidate time windows, compares the path segment distribution situation to determine the reconstructed window, and generates a path conflict avoidance execution period.

[0007] As a further aspect of the present invention, the node pressure response correlation table includes a response delay time, a pressure change synchronization coefficient, and an associated node pair identifier; the path segment - task mapping list includes a path segment number, a task number, and a path direction label; the path segment conflict density level table includes a path segment number, a time block number, a task overlap count level, and a task occupancy density value; the path conflict avoidance execution period includes a pre - candidate time window, a post - candidate time window, and a reconstruction time window.

[0008] As a further aspect of the present invention, the node pressure monitoring module includes: The pressure data acquisition sub - module acquires the node pressure sensor data in the mine operation area, continuously monitors each node during the operation task cycle, and acquires time - series data, records the pressure change time - series of each node, and generates node pressure time - series values; The pressure difference calculation sub - module performs merging processing on the sequential pressure data of adjacent nodes according to the node pressure time - series values, extracts the pressure values at corresponding moments on its time axis, calculates the pressure difference between each pair of adjacent nodes through the difference relationship between the pressure values, and combines the fluid flow direction labels between each node to perform a trend direction discrimination operation, generating a direction - consistent pressure difference trend judgment record; The associated trend extraction sub - module combines the direction - consistent pressure difference trend judgment record, and performs a screening operation on the trend values between nodes according to the change trend relationship between the pressure difference and the flow direction between nodes, extracts a set of node pairs with the same direction, and uses the formula: ; Calculate the trend response intensity value of node , identify and classify the node pairs whose response intensity values exceed the trend intensity reference value, establish a mapping relationship, and obtain the node pressure response correlation table. Among them, , represents the pressure difference between node and its adjacent node, represents the flow direction scalar of node , represents the pressure fluctuation amplitude of node , represents the total pressure change value of node during the entire task cycle.

[0009] As a further aspect of the present invention, the path task mapping module includes: Based on the node pressure response correlation table, the path segment structure extraction sub-module identifies node pairs with correlation values, locates the corresponding connection segments in the underground space structure diagram, extracts the path segment structure information according to the continuity of node numbers, and conducts number sorting to obtain the path segment numbers within the underground operation range, generating a path segment structure number set; Based on the path segment structure number set, the path direction marking sub-module compares the numerical positions of the start and end nodes of the path segment on the coordinate axes in combination with the coordinate axis flow direction rules set in the mine operation tasks, and takes the main flow axis direction as the judgment benchmark to perform path segment direction recognition operations, assigning corresponding direction labels to each path segment to obtain a path segment direction identification table; Based on the path segment direction identification table, the task binding extraction sub-module extracts the operation task numbers, task start and end times, and task execution path information in the operation and maintenance task schedule. By comparing each item in the path segment number and the number field of the path field in the schedule, it analyzes whether there is an intersection relationship between the task time interval and the path segment task plan, and establishes a path segment-task mapping list.

[0010] As a further solution of the present invention, the conflict density identification module includes: Based on the path segment-task mapping list, the repeated scheduling statistics sub-module retrieves the task number information bound to each path segment, summarizes and statistically calculates the repeated scheduling times of each path segment in different tasks, calculates the total frequency of the same path segment being used by multiple tasks, and generates a path segment scheduling frequency value; Based on the path segment scheduling frequency value, the usage block division sub-module divides continuous time blocks within a unified scheduling period according to the start and end times of the tasks bound to each path segment, extracts the time coverage segments of the task numbers in each time block, statistically calculates the number of covering tasks and accumulates their task durations, using the formula: ; Calculate the task occupancy rate of the path segment in the time block , establish the usage density distribution within the path segment scheduling period according to the calculation results of each block, and obtain the path segment task occupancy rate distribution data, where , and are respectively the end time and start time of the task in the block , is the total time length of the time block , is the total number of times the path segment is scheduled by tasks in this block ; The density level marking sub-module calculates the average task occupancy rate of all blocks of the path segment with reference to the occupancy rate of each path segment in different time blocks according to the path segment task occupancy rate distribution data, screens the time blocks greater than the average value, marks the corresponding path segment as a high-density section, and divides the conflict level based on the number of blocks and the density degree, and establishes a path segment conflict density level table.

[0011] As a further solution of the present invention, the time window adjustment module includes: The path density identification sub-module extracts the path segment numbers bound to the currently scheduled task based on the path segment conflict density level table, compares the path segment density level values one by one, identifies the set of path segment numbers with a density level higher than the set value, and generates a high-density path segment number set; The candidate time generation sub-module obtains the original planned start and end times of the current task as the basic time window based on the high-density path segment number set, extends the start point forward and the end point backward by a set duration to form two candidate time windows, extracts the task distribution time information of the path segments within each candidate time window, counts the task quantity and time distribution of each path segment, and uses the formula: ; Calculate the overlapping density value of the path segments within the candidate time window Combined with the calculation results of the front and back two time windows, establish an evaluation index for the path segment scheduling conflict distribution, and obtain the candidate time density value of the path segment. Among them, and are the end time and start time of the path segment scheduled by the task , represents the total number of task coverage times of the path segment within the time window , is the time span of the candidate time window , is the total duration of all tasks within the time window , is the number of tasks participated by the path segment within the candidate time window ; The window optimal screening sub-module compares the density values of the front and back candidate time windows with the path segment density level value of the current time window according to the candidate time density value of the path segment, screens the candidate time windows that simultaneously satisfy that the density values of each path segment are lower than the density level of the current time window, and uses them as the available intervals for conflict adjustment to obtain the path conflict avoidance execution period.

[0012] As a further solution of the present invention, the cloud platform further includes an operation and maintenance scheduling matching module; Based on the path conflict avoidance execution period, the operation and maintenance scheduling matching module reads all the equipment scheduling plans and personnel deployment information associated with the mine tasks, determines the available status of the equipment and personnel and their positions at the task nodes during the target time period, sorts them according to the shortest reachable path distance and reachable time from the equipment to the target path segment, and selects the equipment and personnel with the shortest distance and whose paths do not involve conflict path segments as the matching units to obtain the mine operation and maintenance resource scheduling adaptation plan; The mine operation and maintenance resource scheduling adaptation plan includes equipment numbers, personnel numbers, shortest reachable path numbers, and path conflict verification results.

[0013] As a further solution of the present invention, the operation and maintenance scheduling matching module includes: The equipment and personnel scheduling sub-module reads the equipment scheduling plan and personnel deployment information associated with the current task based on the path conflict avoidance execution period, identifies the available status of each equipment and personnel during the target time period, and obtains their positions at the task nodes to generate equipment and personnel available status information; The path shortest distance calculation sub-module calculates the shortest reachable path distance and reachable time from the equipment to the target path segment according to the equipment and personnel available status information and the current positions of the equipment and personnel, sorts the path distances and times of each equipment and personnel, and obtains the shortest reachable distance and time values of the path; The scheduling adaptation screening sub-module sorts all the equipment and personnel according to the shortest reachable distance and time values of the path, preferentially selects the equipment and personnel closest to the target path segment and whose paths do not involve conflict path segments as the scheduling matching units, and adapts them to the tasks to generate the mine operation and maintenance resource scheduling adaptation plan.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by constructing a pressure response relationship through the node pressure difference and flow direction, it is possible to dynamically identify the node groups with consistent force trends in the underground environment, avoid misjudgment of the operation situation by the static topological structure, make the path extraction more real-time, construct a path flow chain through direction labels to make the coupling between the operation and maintenance tasks and the physical path more accurate, establish a path usage density analysis mechanism through the scheduling period division and task occupancy rate calculation to capture the resource scheduling aggregation characteristics during task execution, quantitatively control the resource load change, set candidate time windows and reconstruct the scheduling period based on the density level difference of path segments, realize the flexible reorganization of the task cycle on the basis of conflict identification, avoid resource contention caused by high-density path blocks, screen the operation and maintenance units by matching the available status, reachable paths and path conflict exclusion situations, improve the scheduling granularity and effectiveness, and overall construct a highly adaptable operation and maintenance scheduling chain in the two dimensions of time and space, enhancing the adaptation ability of the execution plan, the rationality of path configuration and the time efficiency accuracy of resource use. Description of the Drawings

[0015] Figure 1 This is the flowchart of the cloud platform of the present invention; Figure 2 This is the flowchart of the node pressure monitoring module of the present invention; Figure 3 This is the flowchart of the path task mapping module of the present invention; Figure 4 This is the flowchart of the conflict density identification module of the present invention; Figure 5 This is the flowchart of the time window adjustment module of the present invention; Figure 6 This is the flowchart of the operation and maintenance scheduling matching module of the present invention. Detailed implementation manners

[0016] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0018] Please refer to Figure 1 , an operation and maintenance service cloud platform of a mine intelligent system includes: The node pressure monitoring module obtains the data of the node pressure sensors in the mine operation area, collects the node pressure time series during the mine operation task cycle, calculates the pressure difference between adjacent nodes, analyzes the change trend between nodes in combination with the fluid flow direction, extracts the node groups with consistent direction pressure changes, and establishes a node pressure response association table; The path task mapping module extracts the structure information of the underground path segments according to the node pressure response association table, assigns direction labels to the path segments according to the mine operation flow direction, and extracts the operation task numbers bound to each path segment in combination with the set operation and maintenance task schedule to obtain a path segment - task mapping list; The conflict density identification module counts the number of times each path segment is repeatedly scheduled in different tasks according to the path segment-task mapping list, divides the path segment usage period block by scheduling time as the dimension, calculates the task occupancy rate of the path segment in each time block, marks the time blocks where the task occupancy rate exceeds the average usage of the path segment, determines the high-density path segments, and generates a path segment conflict density level table; Based on the path segment conflict density level table, the time window adjustment module extracts the path segments of the currently scheduled task, determines the path segment numbers with high-density sections, obtains the original planned start and end times of the task as the basic time window, extends the start point forward and the end point backward by a fixed period respectively to form the front and rear candidate time windows, recalculates the task overlap degree and density level of the path segments within the candidate time windows, compares the path segment distribution of the two, and selects the candidate time window with a path segment density level lower than the current time window as the reconstructed window to generate the path conflict avoidance execution period; Based on the path conflict avoidance execution period, the operation and maintenance scheduling matching module reads all the equipment scheduling plans and personnel allocation information associated with the mine tasks, determines the available status and the location of the task nodes to which the equipment and personnel belong within the target time period, sorts them according to the shortest reachable path distance and reachable time from the equipment to the target path segment, and selects the equipment and personnel with the shortest distance and non-conflicting path segments as the matching units to obtain the mine operation and maintenance resource scheduling adaptation plan.

[0019] The node pressure response correlation table includes the response delay time, the pressure change synchronization coefficient, and the associated node pair identifier. The path segment-task mapping list includes the path segment number, the task number, and the path direction label. The path segment conflict density level table includes the path segment number, the time block number, the task overlap count level, and the task occupancy density value. The path conflict avoidance execution period includes the front candidate time window, the rear candidate time window, and the reconstructed time window. The mine operation and maintenance resource scheduling adaptation plan includes the equipment number, the personnel number, the shortest reachable path number, and the path conflict verification result.

[0020] Please refer to Figure 2 , the node pressure monitoring module includes: The pressure data acquisition sub-module obtains the node pressure sensor data in the mine operation area, continuously monitors each node during the operation task cycle, collects time series data, records the pressure change time series of each node, and generates the node pressure time series value; To obtain the data of the node pressure sensors in the mine operation area, it is necessary to clarify the distribution density of the sensors in the mine space and the area division method. For example, in a 30m×30m working face, 9 pressure sensors are set, numbered from A1 to C3, arranged in a regular grid structure. The sensors are deployed in groups every 10m, and the initial acquisition frequency is set to 1Hz, that is, data is collected once every second. The acquisition period covers a complete operation task cycle. Assuming this cycle is 3600 seconds, in actual acquisition, through sensor A1, the pressure sequence Pa1=[101.3, 101.2, 101.1,..., 100.8] (unit: kPa) is recorded, with a total of 3600 groups of data. Other nodes such as B2 and C3 are the same, forming a pressure time series set P={Pa1, Pb1,..., Pc3} for the nine nodes. During the recording process, the abnormal data rejection rule should be considered, and the points with a pressure jump rate greater than 2kPa / s are removed. Subsequently, the sequences of all nodes need to be synchronized and aligned, that is, bound to the sequence with a unified time axis. If some sensors are lost for a short time, linear interpolation is performed on the missing points to form a complete pressure time series matrix. The rows of the matrix correspond to time points, and the columns correspond to node numbers. For example, the data at the 100th second is: A1:101.1, A2:101.0, A3:100.9..., and this matrix provides a basic data set for subsequent difference calculation and directional analysis. Through the above acquisition process, the node pressure time series values are obtained.

[0021] The pressure difference calculation sub-module merges the sequential pressure data of adjacent nodes according to the node pressure time series values, extracts the pressure values at the corresponding moments on the time axis, calculates the pressure difference between each pair of adjacent nodes through the difference relationship between the pressure values, and combines the fluid flow direction labels between each node to perform a trend direction discrimination operation to generate a pressure difference trend judgment record with consistent direction; According to the node pressure time series values, it is necessary to first identify the connection relationships between all pairs of adjacent nodes. Based on the aforementioned grid structure, A1 and A2, B1 are adjacent node pairs. For each pair of nodes, the pressure difference is calculated using the data pairs at the same time point. Taking the 500th second as an example, the pressure of node A1 is 100.9kPa, and the pressure of node A2 is 100.5kPa. The difference ΔP = 100.9 - 100.5 = 0.4kPa. This difference is repeatedly calculated at each moment to form a ΔP sequence; subsequently, combined with the previously obtained flow direction label D, the label value can be +1 or 1, indicating that the air flow flows from the previous node to the next node or in the reverse direction. If the direction from A1 to A2 is +1, it means that the high-pressure value is in the same direction as the low-pressure value; if it is If it is 1, the direction is opposite. Multiply the difference sequence of each pair of nodes by the direction label to form a direction consistency sequence. For example, if the above difference is 0.4 kPa and the direction is +1, the consistency value is +0.4 kPa. When the consistency value is continuously positive or negative, it indicates that there is a stable trend in that direction. According to the fluctuation range of the direction consistency trend sequence, the maximum value, mean value, and coefficient of variation of each pair of nodes can be calculated as evaluation indicators of the trend strength to identify whether the air flow forms a stable flow path, and then obtain the direction-consistent pressure difference trend judgment record.

[0022] The associated trend extraction sub-module combines the direction-consistent pressure difference trend judgment record, and based on the change trend relationship between the pressure difference between nodes and the flow direction, performs a screening operation on the trend values between nodes to extract the set of node pairs with consistent directions, using the formula: ; Calculate the trend response intensity value of the node , identify and classify the node pairs whose response intensity value exceeds the trend intensity reference value, establish a mapping relationship, and obtain the node pressure response association table. Among them, represents the pressure difference between node and its adjacent node, represents the flow direction scalar of node , represents the pressure fluctuation amplitude of node , represents the total pressure change value of node during the entire task cycle; Combined with the direction-consistent pressure difference trend judgment record, it is necessary to first establish a node trend response screening rule. For each node, taking all the direction-consistent differences it participates in as input, perform normalization processing. Suppose the pressure difference sequence participated by node A1 is [0.4, 0.3, 0.5], the flow direction sequence is [+1, 1, +1], the pressure fluctuation amplitude V is 0.6 kPa, and the total change value T is 2.4 kPa. Then substitute into the formula: ; In the above operation, P represents the current pressure difference (0.4 kPa), D is the direction value (+1), V is the fluctuation amplitude (0.6 kPa), T is the total value of periodic pressure change (2.4 kPa), and the calculated trend response intensity R is 0.129. Subsequently, such operations are performed on all nodes to form an R sequence, and then the results are compared with the reference value. The reference value of the trend intensity is set to 0.25 (the setting basis of this trend intensity reference value is the ratio interval of the standard deviation of the total value of periodic pressure change T of the node to the median of the directional pressure difference P. Through the statistical analysis of at least 12 groups of mine operation data in the early stage, it is found that when the node response intensity value R is between [0.20, 0.30], the stable flow trend and accidental disturbances can be accurately distinguished. Therefore, the median value 0.25 of the interval is set as the critical judgment value for judging the node response significance. This reference value shows a linear offset trend with the fluctuation of the amplitude of T. When the periodic pressure change value increases, this value can be slightly increased, and vice versa), when R > 0.25, it is determined that the node has significant trend response characteristics. If not satisfied, the node group is excluded. Finally, only the node set with common trend response is retained, and the corresponding response mapping relationship is established according to its spatial distribution, such as A1 - A2 - A3, to form a response group with the same direction, and then the node pressure response correlation table is obtained.

[0023] Table 1 Calculation Table of Trend Response of Monitoring Nodes

[0024] Table 1 lists the calculation data of the trend response of some nodes. By comparing with the trend reference value of 0.25, it can be seen that nodes B2 and C3 meet the response requirements, while A1 does not meet the screening conditions. Therefore, node A1 is excluded from the response correlation table.

[0025] By jointly modeling the pressure difference, directionality, and fluctuation amplitude, and through the normalization process of the total pressure change, the change response intensity between nodes can be clearly analyzed, so as to accurately delimit the node group with significant response trends. The result shows that the level of the trend response intensity value can be used as a discriminant criterion for delimiting the node response group. The higher the value, the more stable and directionally consistent the pressure transmission trend of the node shows during the entire operation cycle, which helps to further establish the node response correlation relationship and construct the trend analysis table.

[0026] Please refer to Figure 3 , the path task mapping module includes: Based on the node pressure response correlation table, the path segment structure extraction sub-module identifies node pairs with correlation values, locates the corresponding connection segments in the underground space structure diagram, extracts the path segment structure information according to the continuity of node numbers, and conducts number sorting to obtain the path segment numbers within the underground operation range, generating a set of path segment structure numbers; To extract the structural information of path segments based on the node pressure response correlation table, it is first necessary to obtain the node pair numbers and their correlation degree values in the node pressure response correlation table. For example, the node number pairs are A1 - A2, A2 - A3, A3 - B1, etc. Assume that node pairs with a pressure response correlation degree greater than 0.75 between nodes are considered to have an effective connection relationship. Match these node pairs one by one in the mine structure diagram, extract the coordinate points of these nodes in the actual space, and confirm whether their connection lines fall within the effective passage area set in the mine. For the node pair A1 - A2, its coordinates are A1(10, 20, 5) and A2(12, 20, 5), and its spatial distance is 2 meters. Verify that it is within the 10 - meter range of the passage standard, confirm that the connection relationship is established, and number this path segment as P001. Using this method, extract multiple effective node pairs to form multiple path segments, such as P001, P002, P003, etc. Each path segment includes a starting point, an ending point, and a segment number. By merging adjacent path segments, a continuous path segment structure is further formed. During the process, the method of comparing line segment connections is used to determine whether they are continuous. The judgment criterion is whether the ending node number of one path segment is the same as the starting node number of another path segment. If the ending point of P001 is A2, which is the same as the starting point of P002, they are merged into a continuous path segment sequence. Use a structure table to record information such as the path segment number, starting and ending nodes, and path length, and establish a path segment structure number set. As shown in Table 1, after summarizing the numbers of multiple path segments, a path segment structure number set can be obtained.

[0027] Table 2 Path Segment Structure Number Table

[0028] As shown in Table 2, path segments P001, P002, and P003 are respectively connected to nodes A1 to B1, generally forming a complete path. Through the above - mentioned numbering rules, a path segment structure number set is established for subsequent direction marking and task matching processing to obtain a path segment structure number set.

[0029] The path direction marking sub - module, based on the path segment structure number set, combines the axis - flow rules set in the mine operation tasks, compares the positions of the starting and ending nodes of the path segment on the coordinate axes, and takes the main - flow axis direction as the judgment benchmark to perform path segment direction recognition operations, assigns corresponding direction labels to each path segment, and obtains a path segment direction identification table; Call path segment structure number set, extract the spatial coordinates of the start and end nodes of each path segment, set the main flow direction axis as the positive direction of the X-axis. Assume the coordinates of node A1 are (10, 20, 5) and the coordinates of node A2 are (12, 20, 5). Compare the X-axis coordinate values of the two. The X coordinate of A2 is greater than that of A1, and it is determined that path segment P001 is a forward path. In a similar manner, calculate the start and end nodes of path segment P002 from A2 (12, 20, 5) to A3 (15, 20, 5). The X-axis direction difference is 3 meters, which is forward; the start and end of path segment P003 are from A3 (15, 20, 5) to B1 (15, 23, 5). The X-axis direction difference is 0, but the Y-axis direction difference is 3 meters. At this time, the secondary direction determination should be set to the Y-axis direction, and the direction difference is positive, which is also defined as forward. If the X value of the end point is less than the X value of the start point, such as from the start point (18, 20, 5) to the end point (15, 20, 5), it is defined as reverse, and the direction label is set to "negative". Set the direction label value to 1 for forward and 0 for reverse. Compare each item in the entire path segment structure number set one by one, and finally obtain the direction values of each path segment. The direction determination standard is set as follows: if the coordinate difference is greater than 0 and satisfies the main flow direction, it is forward; if it is less than 0 or deviates from the main flow direction, it is reverse. The direction results are saved in the direction identification table. Each record corresponds to a path segment number and its direction value, and a path segment direction identification table is established.

[0030] The task binding extraction sub-module extracts the job task number, task start and end times, and task execution path information in the operation and maintenance task schedule according to the path segment direction identification table. Compare each item one by one through the path segment number and the number field of the path field in the schedule, analyze whether there is an intersection relationship between the task time interval and the path segment task plan, and establish a path segment-task mapping list; Extract the direction values of each path segment according to the content of the path segment direction identification table, and match the corresponding relationship between the task execution path segment number and the task number set in the operation and maintenance task schedule. The matching principle is as follows: If the path segment number appears in the task execution path field, and there is an intersection between the start and end times of the task plan and the usage time of the path segment, a binding relationship between the path segment and the task is established. Set the task number as T001, its task execution path segment is P001 - P002, the task time is from 08:00 to 10:00, and the actual usage time of path segment P001 is from 07:30 to 09:30, with a time intersection of 1.5 hours, and it is determined that the match is successful. Set the task time intersection threshold as 0.5 hours. This threshold is set according to the minimum operation duration of the path segment. That is, in the path segment usage record, the shortest operation duration is 0.6 hours. To avoid misjudgment of task binding due to too short an intersection, the threshold is set slightly lower than the minimum operation duration and adjusted according to the average task duration of the path segment. If the average duration rises above 2 hours, the threshold can be increased to 0.8 hours; if the average duration drops below 1 hour, the threshold should be correspondingly reduced to the range of 0.3 hours to maintain sensitivity to the coincidence degree of key tasks. Compare each path segment with each task one by one to determine whether there is number consistency and time coincidence. Match through the task plan number field and the path segment number field, and filter out records with a time intersection greater than 0.5 hours to establish a mapping relationship. For example, path segment P002 corresponds to two tasks, T001 and T003. Respectively judge whether their usage time periods coincide. If the time of T003 is from 09:15 to 11:00, and the coincidence time with P002 is 0.75 hours, which also meets the threshold, the two-way binding is established. Finally, organize and output each path segment and its corresponding task number to form a mapping list, and obtain the path segment - task mapping list.

[0031] Please refer to Figure 4 , the conflict density identification module includes: Based on the path segment - task mapping list, the repeated scheduling statistics sub - module retrieves the task number information bound to each path segment, summarizes and statistically counts the repeated scheduling times of each path segment in different tasks, calculates the total frequency of a path segment being used by multiple tasks, and generates the path segment scheduling frequency value; Based on the path segment task mapping list, first retrieve the task number information of all path segments one by one. Using the path segment number as an index, extract the list of associated task numbers. On this basis, count the total number of times each path segment is scheduled by different tasks in the job scheduling plan table. For example, path segment A1 corresponds to task numbers T01 and T02, path segment A2 corresponds to task numbers T02 and T03, and path segment A3 corresponds to task numbers T01 and T03. Then the scheduling frequency of path segment A1 is 2 times, and A2 and A3 are also 2 times. After recording the number of tasks corresponding to each path segment, further screen whether there is a situation where the task numbers are repeated but the scheduling times overlap. For path segments with overlapping tasks, perform a time intersection judgment. For example, in A1, the execution time of task T01 is from 5 to 25 minutes, and the execution time of task T02 is from 30 to 50 minutes, with no time intersection, so it is determined as a non-conflicting repeated scheduling path segment. For A2, if the scheduling times of tasks T02 and T03 are from 10 to 30 minutes and from 20 to 40 minutes respectively, there is an intersection part of 10 minutes, and at this time it is recorded as a repeated scheduling path segment. After completing the above judgment and statistical operations for all path segments, calculate the repeated scheduling frequency of all path segments within the scheduling plan period and complete the structured record, and finally obtain the path segment scheduling frequency value.

[0032] Use the block division sub-module. Based on the path segment scheduling frequency value, according to the start and end times of the tasks bound to each path segment, divide continuous time blocks within the unified scheduling period, extract the time coverage segments of the task numbers within each time block, count the number of covered tasks and accumulate their task durations. Use the formula: ; Calculate the task occupancy rate of the path segment within the time block and establish the usage density distribution within the path segment scheduling period according to the calculation results of each block to obtain the path segment task occupancy rate distribution data. Among them, and are the end time and start time of task in block respectively, is the total time length of time block , and is the total number of times the path segment is scheduled by tasks in this block ; Call path segment scheduling frequency value. For the start and end times of tasks bound to each path segment, first divide the entire job cycle uniformly into multiple consecutive time blocks. For example, with a block length of 60 minutes, if the job cycle of path segment A1 is from 0 minutes to 60 minutes, the job time of task 1 on this path segment is from 5 minutes to 25 minutes, and the job time of task 2 is from 30 minutes to 50 minutes. Then, first record the start and end times of each task in the current time block for this path segment, and then calculate the duration of task 1 as 25 - 5 = 20 minutes, task 2 as 50 - 30 = 20 minutes. The total task duration is 40 minutes. At the same time, the total length of the time block is 60 minutes. Subsequently, calculate the task occupancy rate of the path segment by dividing the total task duration by the time block length, that is, 40 / 60 = 0.666, indicating that the task occupancy rate of path segment A1 in this time block is 66.6%. The start and end times of tasks for path segments A2 and A3 are 10 to 30 minutes and 35 to 55 minutes, and 15 to 35 minutes and 40 to 55 minutes respectively. Calculate their task durations to be 40 minutes and 35 minutes respectively, and the total block length is also 60 minutes. Therefore, their task occupancy rates are 0.666 and 0.583 respectively. To clearly show the usage intensity of each path segment during the scheduling cycle, the statistical results are shown in Table 3: Table 3 Task Block Occupancy Rate Table for Path Segments

[0033] As shown in Table 3, the task occupancy rates of path segments A1 and A2 are both 66.7%, and that of path segment A3 is 58.3%. Based on this, establish a record table of task occupancy intensity for each path segment in each block during the scheduling cycle to obtain the task occupancy rate distribution data for the path segments.

[0034] The density level marking sub-module calculates the average task occupancy rate of all blocks of the path segment based on the task occupancy rate distribution data of the path segment, using the occupancy rates of each path segment in different time blocks as a reference. It screens out the time blocks with occupancy rates greater than the average, marks the corresponding path segments as high-density sections, and divides the conflict levels according to the number of blocks and the density degree to establish a conflict density level table for the path segments; According to the task occupancy rate distribution data of path segments, first extract the task occupancy rates of each path segment item by item in all time blocks, calculate the average task occupancy rate of each path segment. Taking path segment A1 as an example, its occupancy rate in block 1 is 0.667. If multiple subsequent blocks are divided and the occupancy rate sequences obtained by calculation are [0.667, 0.5, 0.7] respectively, then the average value is calculated as (0.667 + 0.5 + 0.7) / 3 = 0.622. By analogy, after obtaining the average values of each path segment, compare the task occupancy rate of each time block with the average value of the corresponding path segment. If the occupancy rate of a certain block is higher than the average value, then this time block is marked as a high-density block. For example, in A1, 0.7 > 0.622, so this block is determined as a high-density block, and at the same time mark that path segment A1 is involved in a conflict. Finally, according to the number of occurrences and distribution of high-density blocks in each path segment, and based on the uniformly set density classification standard, classify the path segments. For example, if there are 2 or more high-density blocks, it is marked as grade 3, 1 is marked as grade 2, and 0 is marked as grade 1. Organize the grade information of all path segments to establish a path segment conflict density grade table.

[0035] Please refer to Figure 5 , the time window adjustment module includes: Based on the path segment conflict density grade table, the path density identification sub-module extracts the path segment numbers bound to the currently scheduled task, compares the path segment density grade values item by item, identifies the set of path segment numbers with a density grade higher than the set value, and generates a high-density path segment number set; Based on the path segment conflict density grade table, first extract the path segment numbers of the currently scheduled task. These path segment numbers are closely related to the actual operation tasks. Each task corresponds to one or more path segments, especially those path segments with a density grade higher than the set threshold need to be concerned. In actual application scenarios, some path segments may be regarded as "high-density" path segments due to excessive task volume or high task density. For example, in the operation area of a mine, the tasks in some areas are scheduled multiple times, and the frequent scheduling leads to a high task overlap on this path segment. Therefore, these high-density path segments must be identified first. For example, during the scheduling process of task T1, the tasks on path segment 1 are scheduled repeatedly. The system queries the path segment conflict density grade table, identifies that the conflict density value of this path segment exceeds the set benchmark, and determines it as a high-density path segment, generating a high-density path segment number set. Further, through the path segment number set, the scheduling history of the relevant path segments can be obtained, clearly showing which path segments have been frequently scheduled by multiple tasks and the specific density grades of these path segments. This process mainly relies on the scheduling history data of tasks and the conflict density information of path segments to ensure that all path segments with high-density scheduling problems can be accurately identified.

[0036] Based on the high-density path segment number set, the candidate time generation sub-module obtains the originally planned start and end times of the current task as the basic time window, extends a set duration forward from the start point and backward from the end point to form two candidate time windows, extracts the task distribution time information of the path segments within each candidate time window, counts the number of tasks and the time distribution of each path segment, and uses the formula: ; Calculate the overlap density value of the path segments within the candidate time window ; , combine the calculation results of the front and back two time windows, establish an evaluation index for the path segment scheduling conflict distribution, and obtain the candidate time density value of the path segment. Among them, and are the end time and start time when the path segment is scheduled by the task , represents the total number of task coverage times of the path segments within the time window , is the time span of the candidate time window , is the total duration of all tasks within the time window , is the number of path segments participating in tasks within the candidate time window ; According to the high-density path segment number set, extract the start time and end time of the originally planned current task from the path segment task mapping list, and use this as the basic time window. After the basic time window, extend a fixed time period forward through a set rule, such as 20 minutes, to form a pre-candidate time window; similarly, extend the same period backward to form a post-candidate time window, and construct the front and back candidate time windows. For example, assume that the originally planned time of task k is from 0 minute to 30 minutes, then the pre-candidate time window is 20 to 30 minutes, and the post-candidate time window is 0 to 50 minutes, covering the front and back time intervals of the task, which is convenient for evaluating the task scheduling conflicts of the path segments within these time windows. Next, the system extracts the scheduling task information of each path segment according to the path segments involved in these candidate time windows, and calculates the task density of the path segments within these time windows. In this process, it is necessary to count the number of path segment tasks and their durations within the candidate time window. The more tasks and the longer the duration, the higher the density of the path segment and the higher the coincidence degree of task scheduling.

[0037] Assume that task k is on path segment 1, and the originally planned scheduling time is from 0 minute to 30 minutes. The pre-candidate time window is then 20 minutes to 30 minutes, and the post-candidate time window is 0 minute to 50 minutes. Taking path segment 1 as an example, calculate the task overlap density of the pre-candidate time window.

[0038] The start time of task k is 0 minutes and the end time is 30 minutes; Path segment 1 has task 1 once during this time period, and the time span of task 1 is 30 minutes; Time window span minutes (total duration of the pre-candidate time window); Sum of the time spans of tasks within the time window minutes (i.e., the task duration of task k); Substitute into the formula: ; Calculation result Indicates that the task overlap density of path segment 1 within the pre-candidate time window is 0.0172. This shows that within this time window, the task density of path segment 1 is low and the task coverage is relatively dispersed. This value can provide a basis for selecting the conflict avoidance time period in the future. A lower density value means that there is less task overlap within this time window, and the scheduling conflict risk of path segment 1 is low, making it suitable as a candidate time period for the adjusted time window.

[0039] The window preference screening sub-module compares the density values of the front and back candidate time windows with the path segment density level value of the current time window according to the path segment candidate time density value, screens the candidate time windows that satisfy that the density values of each path segment are lower than the density level of the current time window, and uses them as the available intervals for conflict adjustment to obtain the path conflict avoidance execution time periods; According to the path segment candidate time density value, compare the task density of the front and back candidate time windows with the path segment density level of the current task time window one by one. In this process, compare the path segment task density of the current time window with the path segment task density in the candidate time window, and screen out the candidate time window with a lower path segment density as the reconstructed window. For example, if the path segment task density of the current time window is 0.03 and the density of the candidate time window is 0.0172, the former has a higher density, so it needs to be adjusted to a low-density time window. Through this process, finally select a candidate time window with a task density lower than that of the current time window as the reconstructed window to ensure that tasks are not scheduled in high-density sections, reduce the scheduling conflicts of path segments, and thus generate the path conflict avoidance execution time periods, which are used for subsequent path segment scheduling to ensure that the conflict problems of path segments are effectively avoided and the execution order of tasks is optimized.

[0040] Please refer to Figure 6 , the operation and maintenance scheduling matching module includes: Based on the time period for avoiding path conflicts, the equipment and personnel scheduling sub-module reads the equipment scheduling plan and personnel deployment information associated with the current task, identifies the available status of each equipment and personnel within the target time period, and obtains the location of the affiliated task node to generate the equipment and personnel available status information; First, it is necessary to read the scheduling information of the current task and extract the equipment and personnel scheduling plans associated with the task. Suppose there are multiple equipment and personnel available for scheduling in the mine. The equipment includes internal transportation equipment in the mine, maintenance tools, etc., and the personnel include miners and technical support personnel, etc. By comparing this information, it can be determined whether the equipment and personnel are available within the target time period. Next, based on the scheduling status information of the equipment and personnel, it is associated with the task node location. This process first involves identifying the working time window of each equipment and personnel and checking its overlap with the current task time period. For a simple example, if the working period of equipment a is from 12:00 to 16:00 and the scheduling period of the current task is from 13:00 to 14:00, then equipment a is available and can be assigned to this task. At this time, if the location between equipment a and the task node is feasible, that is, its operating range matches the path segment required by the task, then equipment a will be recognized as an equipment meeting the scheduling conditions. After this operation, finally, a table of available equipment and personnel status information is generated, including the available time of each equipment and personnel, the task node location, etc., providing basic data support for subsequent task scheduling and ultimately forming the equipment and personnel available status information.

[0041] Based on the equipment and personnel available status information and the current locations of the equipment and personnel, the shortest path distance calculation sub-module calculates the shortest reachable path distance and reachable time from the equipment to the target path segment, sorts the path distances and times of each equipment and personnel, and obtains the shortest reachable distance and time values; First, it is necessary to obtain the current location of the equipment or personnel, which can be obtained through the real-time positioning system of the equipment and personnel. For example, the current location of equipment a is the starting point of path segment 1, and the current location of personnel B is the starting point of path segment 2. Then, based on the current location of the equipment or personnel, calculate the shortest reachable path distance and time to the target path segment. Here, the path segment can be represented by the road network diagram of the mine, and equipment a and personnel B need to find the shortest driving path according to this network diagram. Suppose the shortest path from the current location of equipment a to the target path segment is 200 meters, while the shortest path from the current location of equipment b to the target path segment is 300 meters. At this time, by comparison, equipment a is considered the equipment closest to the target path segment. During the process of calculating the path, the speed of the equipment and the passing conditions (such as road conditions, slopes, etc.) will affect the required time. Suppose the average driving speed of equipment a is 30 meters per minute, and the average driving speed of equipment b is 25 meters per minute. Then the time required for equipment a is 200 meters / 30 meters per minute = 6.67 minutes, and the time for equipment b is 300 meters / 25 meters per minute = 12 minutes. Therefore, the shortest reachable time of the path for equipment a is 6.67 minutes, and for equipment B is 12 minutes. Through such calculations, the shortest reachable path time between each equipment and the task path segment can be obtained, and then sorted. The finally obtained shortest reachable path distance and time value are the shortest path times for each equipment or personnel, which can ensure the efficiency during the scheduling process.

[0042] The scheduling adaptation screening sub-module sorts all equipment and personnel according to the shortest reachable path distance and time value, and preferentially selects the equipment and personnel that are closest to the target path segment and whose paths do not involve conflict path segments as the scheduling matching units, and adapts them to the task to generate a mine operation and maintenance resource scheduling adaptation plan; Based on the shortest reachable path distance and time value calculated above, sort all equipment and personnel. This sorting is not only based on the shortest path distance, but also needs to comprehensively consider whether the path involves conflict path segments. Here, we set a list of conflict path segments, which includes the path segments that are unavailable for the current task (such as due to maintenance, blockage, etc.). When the path of each equipment or personnel must pass through these path segments, these path segments need to be avoided. Suppose the shortest reachable path time of equipment a is 6.67 minutes and the path segment has no conflict, then equipment a can be preferentially scheduled. If the shortest reachable path time of equipment b is 12 minutes and the path segment it passes through has conflict path segments, then this equipment will be excluded. In this way, select the equipment and personnel whose paths do not involve conflict path segments and have the minimum shortest reachable time. Finally, a mine operation and maintenance resource scheduling adaptation plan can be obtained, which includes the optimal matching and task allocation of equipment and personnel, ensuring the optimal configuration in terms of time and resources and improving the mine operation and maintenance efficiency.

[0043] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the relevant art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An operation and maintenance service cloud platform for an intelligent mine system, characterized in that The cloud platform includes: The node pressure monitoring module collects the node pressure time series during the mine operation task cycle, calculates the pressure difference between adjacent nodes, combines the fluid flow direction to analyze the change trend between nodes, and establishes a pressure response correlation table between nodes; The path task mapping module extracts the underground path segment structure information according to the pressure response correlation table between nodes, assigns direction labels to the path segments, and extracts the operation task numbers bound to the path segments in combination with the set operation and maintenance task schedule to obtain a path segment - task mapping list; The conflict density identification module counts the repeated scheduling times of the path segments according to the path segment - task mapping list, calculates the task occupancy rate of the path segments in each time block, determines the high - density path segments, and generates a path segment conflict density level table; The time window adjustment module extracts the path segments of the currently scheduled task based on the path segment conflict density level table, determines the path segment numbers with high - density sections, uses the original planned start and end times of the task as the basic time window, extends forward and backward to form front and rear candidate time windows, calculates the task coincidence degree and density level of the path segments in the candidate time windows, compares the path segment distribution to determine the reconstructed window, and generates a path conflict avoidance execution period.

2. The operation and maintenance service cloud platform of the mine intelligent system according to claim 1, characterized in that The pressure response correlation table between nodes includes response delay time, pressure change synchronization coefficient, and associated node pair identifier. The path segment - task mapping list includes path segment number, task number, and path direction label. The path segment conflict density level table includes path segment number, time block number, task coincidence times level, and task occupancy density value. The path conflict avoidance execution period includes front candidate time window, rear candidate time window, and reconstructed time window.

3. The operation and maintenance service cloud platform of the mine intelligent system according to claim 1, characterized in that The node pressure monitoring module includes: The pressure data acquisition sub - module obtains the node pressure sensor data in the mine operation area, continuously monitors each node during the operation task cycle, collects time - series data, records the pressure change time series of each node, and generates node pressure time series values; The pressure difference calculation sub - module performs merging processing on the sequential pressure data of adjacent nodes according to the node pressure time series values, extracts the pressure values at the corresponding moments on the time axis, calculates the pressure difference between each pair of adjacent nodes through the difference relationship between the pressure values, and combines the fluid flow direction labels between nodes to perform a trend direction discrimination operation, generating a pressure difference trend judgment record with consistent directions; The associated trend extraction sub - module combines the pressure difference trend judgment record with consistent directions, performs a screening operation on the trend values between nodes according to the change trend relationship between the pressure difference and the flow direction between nodes, extracts the set of node pairs with consistent directions, and uses the formula: ； Computing node of the trend response intensity value , identify and classify node pairs whose response intensity values exceed the trend intensity reference value, establish a mapping relationship, and obtain a node pressure response association table, where represents the pressure difference between node and its adjacent node, represents the flow direction scalar of node , represents the pressure fluctuation amplitude of node , represents the total pressure change value of node during the entire task cycle.

4. The operation and maintenance service cloud platform of the mine intelligent system according to claim 1, characterized in that, The path task mapping module includes: The path segment structure extraction sub - module identifies the node pairs with association values based on the pressure response correlation table between nodes, locates the corresponding connection segments in the underground space structure diagram, extracts the path segment structure information according to the continuity of node numbers, and performs number sorting to obtain the path segment numbers within the underground operation range, generating a path segment structure number set; The path direction annotation sub-module numerically compares the positions of the start and end nodes of the path segments on the coordinate axes according to the set of path segment structure numbers, and combines with the coordinate axis flow direction rules set in the mine operation tasks. Taking the main flow direction axis as the judgment benchmark, it performs path segment direction recognition operations, assigns corresponding direction labels to each path segment, and obtains the path segment direction identification table; The task binding extraction sub-module extracts the operation task numbers, task start and end times, and task execution path information in the operation and maintenance task schedule according to the path segment direction identification table. By comparing each item in turn through the path segment number and the number field of the schedule path field, it analyzes whether there is an intersection relationship between the task time interval and the path segment task plan, and establishes a path segment-task mapping list.

5. The operation and maintenance service cloud platform of the mine intelligent system according to claim 1, characterized in that, The conflict density identification module includes: The repeated scheduling statistics sub-module retrieves the task number information bound to each path segment based on the path segment task mapping list, summarizes and counts the repeated scheduling times of each path segment in different tasks, calculates the total frequency of the same path segment being used by multiple tasks, and generates the path segment scheduling frequency value; The usage block division sub-module divides continuous time blocks within a unified scheduling period based on the path segment scheduling frequency value according to the start and end times of the tasks bound to each path segment, extracts the time coverage segments of the task numbers within each time block, counts the number of covered tasks and accumulates their task durations, using the formula: ； Calculate the task occupancy rate of the computing path segment within the time block and establish the usage density distribution within the scheduling period of the path segment according to the calculation results of each block to obtain the path segment task occupancy rate distribution data, where , and are respectively the end time and start time of the task in the block , is the total time length of the time block , is the total number of times the path segment is scheduled by tasks in this block ; The density level annotation sub-module calculates the average task occupancy rate of all blocks of the path segment based on the task occupancy rate distribution data of the path segment, using the occupancy rate of each path segment in different time blocks as a reference. It screens the time blocks greater than the average value, marks the corresponding path segments as high-density sections, and divides the conflict levels according to the number of blocks and the density degree, and establishes the path segment conflict density level table.

6. The operation and maintenance service cloud platform of the mine intelligent system according to claim 1, characterized in that The time window adjustment module includes: The path density identification sub-module extracts the path segment numbers bound to the currently scheduled task based on the path segment conflict density level table, compares the path segment density level values item by item, identifies the set of path segment numbers with a density level higher than the set value, and generates the high-density path segment number set; The candidate time generation sub-module takes the originally planned start and end times of the current task as the basic time window based on the high-density path segment number set, extends the start time forward and the end time backward by a set duration to form two candidate time windows, extracts the task distribution time information of the path segments within each candidate time window, and counts the number of tasks and time distribution of each path segment, using the formula: ； Calculate candidate time window Overlap density value of the inner path segment , combining the calculation results of the previous and next time windows, establish an evaluation index for the path segment scheduling conflict distribution, and obtain the candidate time density value of the path segment. Among them, and are the end time and start time of the path segment scheduled by the task , represents the total number of task coverage times of the path segment within the time window , is the candidate time window time span of is the time window total duration of all tasks within is the candidate time window number of tasks participated by the path segment within The window optimization and screening sub-module compares the density values of the front and rear candidate time windows with the path segment density level value of the current time window according to the candidate time density value of the path segment, screens the candidate time windows that simultaneously meet the condition that the density values of each path segment are lower than the density level of the current time window, and uses them as the available intervals for conflict adjustment to obtain the path conflict avoidance execution period.

7. The operation and maintenance service cloud platform of the mine intelligent system according to claim 1, characterized in that, The cloud platform further includes an operation and maintenance scheduling matching module; Based on the path conflict avoidance execution period, the operation and maintenance scheduling matching module reads all the equipment scheduling plans and personnel allocation information associated with the mine tasks, determines the available status of the equipment and personnel and their positions at the task nodes during the target time period, sorts them according to the shortest reachable path distance and reachable time from the equipment to the target path segment, and selects the equipment and personnel with the shortest distance and whose paths do not involve conflict path segments as the matching units to obtain the mine operation and maintenance resource scheduling adaptation plan; The mine operation and maintenance resource scheduling adaptation plan includes equipment numbers, personnel numbers, shortest reachable path numbers, and path conflict verification results.

8. The operation and maintenance service cloud platform of the mine intelligent system according to claim 7, characterized in that, The operation and maintenance scheduling matching module includes: The equipment and personnel scheduling sub-module, based on the path conflict avoidance execution period, reads the equipment scheduling plans and personnel allocation information associated with the current task, identifies the available status of each equipment and personnel during the target time period, and obtains their positions at the task nodes to generate equipment and personnel available status information; The shortest path distance calculation sub-module, according to the equipment and personnel available status information and the current positions of the equipment and personnel, calculates the shortest reachable path distance and reachable time from the equipment to the target path segment, sorts the path distances and times of each equipment and personnel, and obtains the shortest reachable path distance and time values; The scheduling adaptation screening sub-module, according to the shortest reachable path distance and time values, sorts all the equipment and personnel, preferentially selects the equipment and personnel that are closest to the target path segment and whose paths do not involve conflict path segments as the scheduling matching units, and adapts them to the tasks to generate the mine operation and maintenance resource scheduling adaptation plan.

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