Mine intelligent system operation and maintenance service cloud platform

Abstract

The present application relates to the technical field of operation and maintenance management, in particular to a mine intelligent system operation and maintenance service cloud platform, the cloud platform 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.In the present application, the nodes with the same direction are extracted by the pressure difference and the flow direction, the dynamic response relationship is constructed, the real-time and accuracy of path extraction are improved, the path and task mapping are combined with the direction label, the adaptation degree of task and physical path is strengthened, the task occupancy rate calculation supports high-density section identification, the scheduling peak is accurately avoided, the start and end time is extended to form a candidate window, the task execution cycle is reconstructed according to the density level, the task scheduling flexibility is enhanced, the resource allocation is filtered according to the reachable path and the path conflict situation, the response unit is realized, the spatial distance and path safety are double optimized, and the real-time adaptability, path configuration rationality and resource calling efficiency of the scheduling scheme are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of operation and maintenance, and particularly relates to an operation and maintenance service cloud platform of a mine intelligent system. BACKGROUND

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

[0003] Among them, the operation and maintenance service cloud platform of the mine intelligent system refers to a comprehensive management system for multi-source data acquisition, running state identification and remote maintenance scheduling of underground production systems in coal mines. It is mainly used to support the informatization and collaborative processing of equipment management and maintenance tasks in coal mine scenarios, covering real-time acquisition of underground equipment state data, state evaluation based on running parameters, generation of operation and maintenance tasks according to maintenance rules, data uploading and centralized management through the cloud platform, and dispatch and recording of operation and maintenance tasks. Through the sensor network, running data acquisition is completed. The health evaluation is carried out based on the state calculation method of the preset index. The task plan is formulated by using the operation and maintenance task generation rule to drive the task plan. The platform data interaction is realized by using the remote communication mode. According to the permission role division mode, the task assignment and progress tracking are supported to complete the overall intelligent operation and maintenance service of the mine system.

[0004] In the existing mine operation and maintenance service process, in the process of path scheduling and task arrangement, the path recognition is not based on the dynamic feedback of the running situation, the path segment selection lacks the real-time pressure state guidance, which leads to that the task path cannot reflect the stress change in the actual environment, there is a deviation in the path segment use planning, and the stable realization of the scheduling target is affected. The task path and the scheduling task are bound through fixed rules or static configuration, which cannot reflect the real-time pressure distribution of the task execution on the path resources, so that the path resource configuration lacks basis, and the risk of high resource scheduling concentration is produced. The path use time is not refined to the block dimension, and the task occupation condition cannot be mapped to the specific scheduling period, which leads to that the time coincidence and path conflict that may exist in the resource scheduling cannot be accurately evaluated. In the scheduling cycle setting process, the task execution time period adjustment lacks a dynamic avoidance mechanism, and does not have the ability to identify and respond to path segment resource conflicts, resulting in execution delay and resource contention phenomenon. In the device and personnel resource configuration, only whether it is idle is taken as the basis, the physical space position and path accessibility factors are ignored, the resource response efficiency is reduced, the path overlap interference risk is increased, and the accuracy and response speed requirements in the high-frequency scheduling scene cannot be met. SUMMARY

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

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: an operation and maintenance service cloud platform of a mine intelligent system comprises:

[0007] The node pressure monitoring module collects the node pressure time sequence in the mine operation task cycle, calculates the pressure difference value between adjacent nodes, analyzes the change trend between nodes in combination with the fluid flow direction, and establishes a node pressure response correlation table;

[0008] The path task mapping module extracts the underground path segment structure information according to the node pressure response correlation table, gives the path segment direction label, extracts the operation task number bound to the path segment in combination with the set operation and maintenance task plan table, and obtains a path segment-task mapping list;

[0009] The conflict density identification module calculates the path segment in each time block task occupancy rate according to the path segment-task mapping list, determines the high-density path segment, and generates a path segment conflict density level table;

[0010] The time window adjustment module extracts the path segment of the current to-be-scheduled task based on the path segment conflict density level table, determines the path segment number of the high-density section, takes the original planned start and end time of the task as the base time window, extends the front and rear to form the front and rear candidate time windows, calculates the path segment task coincidence degree and density level in the candidate time window, compares the path segment distribution to determine the reconstruction window, and generates the path conflict avoidance execution period.

[0011] As a further scheme of the application, the inter-node pressure response correlation table includes a response delay time, a pressure change synchronization coefficient, and a correlation 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 coincidence frequency level, and a task occupation density value, and the path conflict avoidance execution period includes a front candidate time window, a rear candidate time window, and a reconstruction time window.

[0012] As a further scheme of the application, the node pressure monitoring module includes:

[0013] The pressure data acquisition submodule acquires the node pressure sensor data of the mine operation area, continuously monitors each node within the operation task period, and acquires time sequence data, records the pressure change time sequence of each node, and generates node pressure time sequence values;

[0014] The pressure difference calculation submodule merges the time sequence pressure data of adjacent nodes according to the node pressure time sequence values, extracts the pressure values at 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 performs a trend direction discrimination operation in combination with the fluid flow direction label of each node to generate a direction-consistent pressure difference trend judgment record;

[0015] The correlation trend extraction submodule extracts a set of node pairs with consistent directions by performing a filtering operation on the trend values between nodes in combination with the direction-consistent pressure difference trend judgment record and according to the change trend relationship between the pressure difference and the flow direction between nodes, and uses the formula:

[0016]

[0017] The trend response intensity value R of node i is calculated i Nodes pairs with a response intensity value exceeding a trend intensity reference value are identified and classified, a mapping relationship is established, and an inter-node pressure response correlation table is acquired, wherein P i represents the pressure difference between node i and an adjacent node, D i represents the flow direction scalar of node i, V i represents the pressure fluctuation amplitude of node i, T iThe total value of pressure changes of the representative node i in the entire task cycle.

[0018] As a further scheme of the present application, the path task mapping module comprises:

[0019] The path segment structure extraction submodule identifies the node pair with the correlation degree value based on the inter-node pressure response correlation table, locates the corresponding connection segment in the downhole space structure diagram, extracts the path segment structure information according to the node number continuity, and performs number arrangement to obtain the path segment number in the downhole operation range, and generates a path segment number set;

[0020] The path direction labeling submodule performs numerical comparison on the positions of the path segment start and end nodes on the coordinate axis according to the path segment number set in combination with the coordinate axis flow direction rule set in the mine operation task, takes the main flow direction axis as the judgment reference, performs path segment direction identification operation, and assigns each path segment with a corresponding direction label to obtain a path segment direction identification table;

[0021] The task binding extraction submodule extracts the operation task number, task start and end time and task execution path information in the operation and maintenance task plan table according to the path segment direction identification table, performs piece-by-piece comparison through the path segment number and the number field of the plan table path field, 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.

[0022] As a further scheme of the present application, the conflict density identification module comprises:

[0023] The repeated scheduling statistics submodule retrieves the task number information bound to each path segment based on the path segment-task mapping list, summarizes and statistics the repeated scheduling times of each path segment in different tasks, calculates the total frequency of the same path segment used by multiple tasks, and generates a path segment scheduling frequency value;

[0024] The use block division submodule divides continuous time blocks in a unified scheduling cycle according to the start and end time of the task bound to each path segment based on the path segment scheduling frequency value, extracts the time coverage segment of the task number in each time block, statistics the number of covered tasks and accumulates the task duration, and uses the formula:

[0025]

[0026] The task occupation rate U of the path segment in the time block j is calculated as follows: j The use density distribution in the path segment scheduling cycle is established according to the calculation results of each block, and the path segment task occupation rate distribution data is obtained, wherein e k And s k Are the end time and start time of task k in the time block j, tj is the total time length of the time block j, n is the total number of times that the path segment is scheduled by the task in the time block j;

[0027] The density level marking submodule calculates the mean task occupancy rate of all blocks of the path segment according to the path segment task occupancy rate distribution data, takes the occupancy rate of each path segment in different time blocks as a reference, filters the time blocks greater than the mean value, marks the corresponding path segment as a high-density section, and divides the conflict level according to the block quantity and density degree, and establishes a path segment conflict density level table.

[0028] As a further scheme of the application, the time window adjustment module comprises:

[0029] The path density identification submodule extracts the path segment number bound by the current task to be scheduled, compares the path segment density level value piece by piece, identifies the path segment number set whose density level is higher than the set value based on the path segment conflict density level table, and generates a high-density path segment number set;

[0030] The candidate time generation submodule obtains the original planned start and end time of the current task as a basic time window based on the high-density path segment number set, extends the set time length forward from the starting point and backward from the ending point to form two candidate time windows, extracts the path segment task distribution time information in each candidate time window, and statistically analyzes the task quantity and time distribution of each path segment, and uses the formula:

[0031]

[0032] Calculates the overlap density value D of the path segment in the candidate time window x x , combines the calculation results of the two time windows before and after, establishes a path segment scheduling conflict distribution evaluation index, and obtains the path segment candidate time density value, wherein e k and s k are the end time and the start time of the path segment scheduled by the task k, m x represents the total number of times that the path segment is covered in the time window x, q x is the time span of the candidate time window x, is the total time length of all tasks in the time window x, and N is the number of path segments participating in the task in the candidate time window x;

[0033] The window optimization filtering submodule compares the density values of the two candidate time windows before and after with the path segment density level value of the current time window according to the path segment candidate time density value, filters the candidate time window that meets the condition that the density value of each path segment is lower than the density level of the current time window, and takes it as a conflict adjustment available interval to obtain the path conflict avoidance execution period.

[0034] As a further scheme of the present application, the cloud platform further comprises an operation and maintenance scheduling matching module;

[0035] The operation and maintenance scheduling matching module reads all device scheduling plans and personnel deployment information associated with the mine task based on the path conflict avoidance execution period, determines the available states of the devices and personnel in the target time period and the positions of the task nodes to which the devices and personnel belong, sorts the devices and personnel according to the shortest reachable path distance and reachable time of the devices to the target path segment, selects the devices and personnel with the shortest distance and the path not involving the conflict path segment as the matching units, and obtains a mine operation and maintenance resource scheduling adaptation scheme.

[0036] The mine operation and maintenance resource scheduling adaptation scheme comprises a device number, a personnel number, a shortest reachable path number, and a path conflict verification result.

[0037] As a further scheme of the present application, the operation and maintenance scheduling matching module comprises:

[0038] The device and personnel scheduling submodule reads the device scheduling plans and personnel deployment information associated with the current task based on the path conflict avoidance execution period, identifies the available states of the devices and personnel in the target time period, and obtains the positions of the task nodes to which the devices and personnel belong, and generates device and personnel available state information.

[0039] The shortest path distance calculation submodule calculates the shortest reachable path distance and reachable time of the devices to the target path segment according to the current positions of the devices and personnel based on the device and personnel available state information, sorts the path distances and times of the devices and personnel, and obtains the shortest reachable path distance and time values.

[0040] The scheduling adaptation screening submodule sorts all the devices and personnel according to the shortest reachable path distance and time values, preferentially selects the devices and personnel closest to the target path segment and not involving the conflict path segment as the scheduling matching units, and adapts the devices and personnel to the task, and generates a mine operation and maintenance resource scheduling adaptation scheme.

[0041] Compared with the prior art, the present application has the following advantages and positive effects:

[0042] In the application, the pressure response relationship is constructed by the node pressure difference value and the flow direction, the node group with consistent stress trend in the downhole environment can be dynamically identified, the misjudgment of the static topology structure to the running situation is avoided, the path extraction is more real-time, the path flow direction chain is constructed through the direction label, the operation and maintenance task and the physical path are more accurately coupled, the path use density analysis mechanism is constructed by the scheduling period division and the task occupancy rate calculation, the resource scheduling aggregation characteristics in the task execution are captured, the resource load change is quantitatively controlled, the candidate time window is set based on the path segment density level difference and the scheduling period is reconstructed, the flexible reorganization of the task cycle is realized based on the conflict identification, the resource contention caused by the high-density path block is avoided, the operation and maintenance unit is screened by matching the available state, the reachable path and the path conflict elimination condition, the scheduling granularity and the effectiveness are improved, the high adaptability operation and maintenance scheduling chain is constructed in the two-dimensional space-time, the adaptation ability of the execution plan, the rationality of the path configuration and the time efficiency accuracy of the resource use are enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The cloud platform flowchart of the application is shown in the figure;

[0044] Figure 2 The node pressure monitoring module flowchart of the application is shown in the figure;

[0045] Figure 3 The path task mapping module flowchart of the application is shown in the figure;

[0046] Figure 4 The conflict density identification module flowchart of the application is shown in the figure;

[0047] Figure 5 The time window adjustment module flowchart of the application is shown in the figure;

[0048] Figure 6 The operation and maintenance scheduling matching module flowchart of the application is shown in the figure. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the application more clear and obvious, the application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application, and are not used to limit the application.

[0050] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0051] Please refer to Figure 1 A mine intelligent system operation and maintenance service cloud platform comprises:

[0052] The node pressure monitoring module acquires node pressure sensor data of a mine operation area, collects node pressure time series within a mine operation task cycle, calculates pressure difference values between adjacent nodes, analyzes node change trends in combination with fluid flow directions, extracts node groups with consistent pressure changes in the same direction, and establishes a node-to-node pressure response correlation table.

[0053] The path task mapping module extracts underground path segment structure information according to the node-to-node pressure response correlation table, assigns path segment direction labels according to mine operation flow directions, extracts operation task numbers bound to each path segment in combination with a set operation and maintenance task plan table, and obtains a path segment-task mapping list.

[0054] 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 path segment usage cycle blocks in the dimension of scheduling time, calculates the task occupancy rate of the path segment in each time block, labels time blocks with task occupancy rates exceeding the average usage of the path segment, determines high-density path segments, and generates a path segment conflict density level table.

[0055] The time window adjustment module extracts the path segment of the current task to be scheduled based on the path segment conflict density level table, determines the path segment number of the high-density section, acquires the original planned start and end time 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 front and rear candidate time windows, recalculates the task overlap and density level of the path segment in the candidate time window, compares the path segment distribution of the two, selects the candidate time window with a path segment density level lower than the current time window as the reconstruction window, and generates a path conflict avoidance execution period.

[0056] The operation and maintenance scheduling matching module reads all device scheduling plans and personnel deployment information associated with the mine task based on the path conflict avoidance execution period, determines the available state and task node position of the device and personnel in the target time period, sorts them according to the shortest reachable path distance and reachable time of the device to the target path segment, selects the device and personnel with the shortest distance and the path not involving the conflict path segment as the matching unit, and obtains the mine operation and maintenance resource scheduling adaptation scheme.

[0057] The inter-node pressure response correlation table includes a response delay time, a pressure change synchronization coefficient, and a correlation node pair identifier. The path segment-task mapping list includes a path segment number, a task number, a path direction label, and a path conflict density level table. The path conflict density level table includes a path segment number, a time block number, a task overlap frequency level, and a task occupation density value. The path conflict avoidance execution period includes a front candidate time window, a rear candidate time window, and a reconstruction time window. The mine operation and maintenance resource scheduling adaptation scheme includes a device number, a personnel number, a shortest reachable path number, and a path conflict verification result.

[0058] Referring to Figure 2 , the node pressure monitoring module includes:

[0059] The pressure data acquisition submodule acquires node pressure sensor data of the mine operation area, continuously monitors each node within the operation task period, and acquires time series data. The pressure change time series of each node is recorded, and the node pressure time series value is generated.

[0060] The node pressure sensor data of the mine operation area is acquired, and the distribution density of the sensors in the mine space and the area division method need to be determined. For example, in a 30m x 30m operation area, 9 pressure sensors are arranged, numbered A1 to C3, arranged in a regular grid structure, and a group of sensors is deployed every 10m. The initial collection frequency is set to 1 Hz, that is, data is collected once every second, and the collection period covers a complete operation task cycle. Assuming that the cycle is 3600 seconds, in actual collection, the pressure sequence Pa1 = [101.3, 101.2, 101.1,..., 100.8] (unit: kPa) is recorded by sensor A1, a total of 3600 groups of data, and other nodes such as B2 and C3 are similarly recorded, forming a pressure time sequence set P = {Pa1, Pb1,..., Pc3} of nine nodes. During the recording process, the abnormal data elimination rule should be considered, and the points with a pressure jump rate greater than 2 kPa / s are removed. Subsequently, the sequences of all nodes need to be synchronized and aligned, that is, the sequences are bound on a unified time axis. If some sensors are missing for a short time, linear interpolation is performed to fill in the missing points, and a complete pressure time sequence matrix is formed. 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,..., which provides a basic data set for subsequent difference calculation and direction analysis. Through the above acquisition process, the node pressure time sequence value is obtained.

[0061] The pressure difference calculation submodule merges the time series pressure data of adjacent nodes according to the node pressure time sequence value, extracts the pressure values at the corresponding time on the time axis, calculates the pressure difference between each pair of adjacent nodes through the difference relationship between the pressure values, and performs trend direction discrimination operation combined with the fluid flow direction label between nodes to generate direction consistent pressure difference trend judgment record.

[0062] According to the node pressure time series value, the connection relationship between all pairs of adjacent nodes needs to be identified first, based on the aforementioned grid structure, A1 and A2, B1 are adjacent node pairs, wherein each pair of nodes respectively calculates the pressure difference value with the same time point data pair, taking the 500th second as an example, the pressure of node A1 is 100.9kPa, the pressure of node A2 is 100.5kPa, the difference ΔP = 100.9-100.5 = 0.4kPa, the difference is repeatedly operated at each time, forming a ΔP sequence; then, combined with the flow direction label D obtained in advance, the label value can be +1 or-1, indicating that the gas flow is from the front node to the rear node or in the opposite direction, if the direction of A1 to A2 is +1, it means that the high pressure value is consistent with the low value direction; if it is-1, the direction is opposite, multiply the difference value sequence of each pair of nodes by the direction label to form the direction consistency sequence, for example, the above difference value is 0.4kPa, and the direction is +1, then the consistency value is +0.4kPa; when the consistency value is continuously positive or negative, it means that there is a stable trend in this 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 the evaluation index of the trend strength, to identify whether the gas flow forms a stable flow path, and then obtain the direction consistent pressure difference value trend judgment record.

[0063] The correlation trend extraction submodule combines the direction consistent pressure difference value trend judgment record, and performs a screening operation on the trend values between nodes according to the change trend relationship between the pressure difference values and the flow directions between nodes, extracts the set of node pairs with consistent directions, uses the formula:

[0064]

[0065] The trend response strength value R of node i is calculated i The node pairs with response strength values exceeding the trend strength reference value are identified and classified, a mapping relationship is established, and a pressure response correlation table between nodes is obtained, wherein P i represents the pressure difference value between node i and the adjacent node, D i represents the flow direction scalar of node i, V i represents the pressure fluctuation amplitude of node i, T i represents the total pressure change value of node i in the entire task period;

[0066] Combined with the direction consistent pressure difference value trend judgment record, the node trend response screening rule needs to be established first, for each node, all the direction consistent differences it participates in are input, and normalization processing is performed, assuming that the participating pressure difference sequence of A1 node is [0.4, 0.3, 0.5], the flow direction sequence is [+1, -1, +1], the pressure fluctuation amplitude V is 0.6kPa, and the total change value T is 2.4kPa, then the formula is:

[0067]

[0068] In the above operation, P represents the current pressure difference value (0.4 kPa), D is the direction value (+1), V is the fluctuation amplitude (0.6 kPa), T is the total value of the periodic pressure change (2.4 kPa), and the calculated trend response intensity R is 0.129. Then, the same operation is performed on all nodes to form an R sequence, and the results are compared with the reference value. The trend intensity reference value is set to 0.25 (the setting of this trend intensity reference value is based on the difference ratio interval between the standard deviation of the total value of the periodic pressure change T and the median of the directional pressure difference value P. Through 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 the occasional disturbance can be accurately distinguished, so the interval median 0.25 is set as the critical judgment value for judging the significance of the node response. This reference value shows a linear bias trend with the amplitude fluctuation of the periodic pressure change value, which can be slightly raised when the periodic pressure change value rises, and vice versa). When R > 0.25, it is determined that the node has a significant trend response feature, and if it does not meet the requirements, the node group is removed. 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, forming a response group with consistent direction, and then obtaining the pressure response correlation table between nodes.

[0069] Table 1 Monitoring node trend response calculation table

[0070]

[0071] Table 1 lists the trend response calculation data of some nodes. By comparing with the trend reference value 0.25, it can be seen that nodes B2 and C3 meet the response requirements, and A1 does not meet the screening conditions, so A1 node is removed from the response correlation table.

[0072] By jointly modeling the pressure difference value, directionality and fluctuation amplitude, and normalizing the total pressure change, the change response intensity between nodes can be clearly analyzed, so as to accurately determine the node group with significant response trend. The results show that the trend response intensity value can be used as a criterion for determining the node response group. The higher the value, the more stable and consistent the pressure transmission trend of the node in the entire operation period, which helps to further establish the node response correlation and construct the trend analysis table.

[0073] Please refer to Figure 3 , the path task mapping module comprises:

[0074] The path segment structure extraction submodule identifies the node pairs with correlation degree values based on the inter-node pressure response correlation table, locates the corresponding connection segments in the underground space structure diagram, extracts path segment structure information according to node number continuity, and numbers and arranges the path segment structure information to obtain the path segment numbers in the underground operation range and generate a path segment structure number set;

[0075] Based on the inter-node pressure response correlation table, the path segment structure information is extracted. First, the node pair numbers and their correlation degree values in the inter-node pressure response correlation table are obtained, for example, node numbers A1-A2, A2-A3, A3-B1, etc. It is assumed that the node pairs with an inter-node pressure response correlation degree greater than 0.75 are considered to have an effective connection relationship. These node pairs are matched one by one in the mine structure diagram to extract the coordinate points of these nodes in the actual space and confirm whether their connecting lines fall within the effective travel area set in the mine. For the node pair A1-A2, the coordinates are A1(10, 20, 5) and A2(12, 20, 5), the spatial distance is 2 meters, and it is verified that it is within the 10-meter range of the travel standard, confirming that the connection relationship is established, and the path segment is numbered as P001. In this way, multiple effective node pairs form multiple path segments, such as P001, P002, P003, etc. Each path segment contains a starting point, an ending point, and a segment number. By merging adjacent path segments, a continuous path segment structure is further formed. In the process, a line segment connection comparison method is used to determine whether it is continuous, and the judgment standard is whether the ending node number of a path segment is consistent with the starting node number of another path segment. If the ending point of P001 is A2, which is consistent with the starting point of P002, then they are merged into a continuous path segment sequence. The structure table records the path segment numbers, starting and ending nodes, path lengths, etc., and establishes a path segment structure number set. As shown in Table 1, after processing multiple path segment numbers, the path segment structure number set can be obtained.

[0076] Table 2 Path segment structure number table

[0077]

[0078] As shown in Table 2, path segments P001, P002, and P003 are connected to nodes A1 to B1, respectively, and collectively form a complete path. The path segment structure number set is established by the above numbering rules for subsequent direction labeling and task matching processing to obtain the path segment structure number set.

[0079] The path direction labeling submodule compares the positions of the path segment starting and ending nodes on the coordinate axis according to the path segment structure number set and the coordinate axis flow direction rules set in the mine operation task, and takes the main flow direction axis as the judgment reference to perform path segment direction identification operation, assign a corresponding direction label to each path segment, and obtain a path segment direction identification table.

[0080] The path segment structure number set is set, the start and end nodes of each path segment are extracted, the main flow direction axis is set as the positive direction of the X axis, it is assumed that the coordinates of node A1 are (10, 20, 5), the coordinates of node A2 are (12, 20, 5), the X axis coordinate values of the two are compared, the X coordinate of A2 is greater than that of A1, and it is judged that the path segment P001 is a positive path. In a similar manner, the start and end nodes A2 (12, 20, 5) to A3 (15, 20, 5) of the path segment P002 are calculated, the X axis direction difference is 3 meters, which is positive; the start and end of the path segment P003 are 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 should be set as the Y axis direction, the direction difference is positive, and it is also defined as a positive direction. If the end point X value is less than the start point X value, such as the start point (18, 20, 5) to the end point (15, 20, 5), it is defined as a reverse direction, the direction label is set as “negative”, the direction label value is set as 1 indicating a positive direction and 0 indicating a reverse direction, each path segment is compared, and finally the direction value of each path segment is obtained. The direction determination standard is set as: if the coordinate difference is greater than 0 and meets the main flow direction, it is a positive direction; if it is less than 0 or deviates from the main flow direction, it is a reverse direction, and the direction result is saved in the direction identification table, each record corresponds to a path segment number and its direction value, and the path segment direction identification table is established.

[0081] The task binding extraction submodule extracts the operation task number, task start and end time and task execution path information in the operation and maintenance task plan table according to the path segment direction identification table, compares each path segment number with the number field in the plan table path field, 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;

[0082] According to the path segment direction value, the matching operation and maintenance task plan table is set up, the task execution path segment number and the task number corresponding relationship is matched, and the matching principle is determined: if the path segment number appears in the task execution path field, and the task plan start and end time and the path segment use time exist intersection, then the path segment and the task binding relationship is established. Set the task number T001, the task execution path segment P001-P002, the task time 08:00-10:00, the path segment P001 actual use time 07:30-09:30, and the time intersection 1.5 hours, and the matching is determined to be successful. Set the task time intersection threshold value to 0.5 hours, the threshold value is set according to the minimum operation time of the path segment, that is, in the path segment use record, the shortest operation time is 0.6 hours, in order to avoid the task binding from being misjudged due to the short intersection, the threshold value is set to be slightly lower than the minimum operation time, and is adjusted according to the average task duration of the path segment, if the average duration rises to 2 hours or more, the threshold value can be expanded to 0.8 hours; if the average duration is reduced to 1 hour or less, the threshold value should be correspondingly reduced to 0.3 hours, in order to maintain the sensitivity to the key task coincidence degree. A plurality of path segments and a plurality of tasks are compared one by one, whether there is number consistency and time coincidence is judged, the task plan number field and the path segment number field are matched, and the records with time intersection greater than 0.5 hours are screened to establish the mapping relationship. For example, the path segment P002 corresponds to two tasks T001 and T003, whether the use time period is coincident is judged, such as T003 time 09:15-11:00, and P002 coincident time 0.75 hours, also meeting the threshold value, the bidirectional binding is established. Finally, each path segment and its corresponding task number are sorted and output to form a mapping list, and the path segment-task mapping list is obtained.

[0083] Please refer to Figure 4 , the conflict density identification module comprises:

[0084] The repeated scheduling statistics submodule retrieves the task number information bound by each path segment based on the path segment-task mapping list, aggregates and counts the repeated scheduling times of each path segment in different tasks, calculates the total frequency of the same path segment used by multiple tasks, and generates a path segment scheduling frequency value;

[0085] Based on the path segment-task mapping list, first, the task number information of all path segments is retrieved one by one, taking the path segment number as the index, and the task number list associated with it is extracted. On this basis, the total number of times each path segment is scheduled by different tasks in the job scheduling plan table is counted. 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 screening is performed to determine whether there is a repeated task number but overlapping scheduling time. For path segments with overlapping tasks, time intersection judgment is performed. For example, in A1, the execution time of task T01 is 5 to 25 minutes, and the execution time of task T02 is 30 to 50 minutes, and there is no time intersection. Therefore, it is determined that it is a non-conflicting repeated scheduling path segment. For A2, if the scheduling time of task T02 and T03 is 10 to 30 minutes and 20 to 40 minutes respectively, there is a 10-minute intersection. At this time, it is recorded as a repeated scheduling path segment. After completing the above judgment and statistical operation on all path segments, the number of repeated scheduling of all path segments in the scheduling plan period is calculated and structured record is completed, and finally the path segment scheduling frequency value is obtained.

[0086] Using the block division sub-module, based on the path segment scheduling frequency value, the start and end time of each path segment binding task is divided into continuous time blocks within a unified scheduling period, the time coverage segment of the task number in each time block is extracted, the number of covered tasks is counted and its task duration is accumulated, and the formula is used:

[0087]

[0088] The task occupancy rate U of the path segment in time block j is calculated j , and the usage density distribution in the path segment scheduling period is established according to the calculation results of each block, and the path segment task occupancy rate distribution data is obtained, wherein e k and s k are the end time and start time of task k in block j, t j is the total time length of time block j, and n is the total number of times the path segment is scheduled by tasks in the block j.

[0089] The call path segment scheduling frequency value binds the start and end time of the task for each path segment. First, the entire job cycle is uniformly divided into multiple consecutive time blocks, for example, with 60 minutes as a block length. If the job cycle of path segment A1 is 0 minutes to 60 minutes, the job time of task 1 on the path segment is 5 minutes to 25 minutes, and the job time of task 2 is 30 minutes to 50 minutes, then the start and end time of each task in the current time block is recorded first. The duration of task 1 is 25-5=20 minutes, and the duration of task 2 is 50-30=20 minutes. The total task duration is 40 minutes, and the total time of the time block is 60 minutes. Then, the task occupancy rate of the path segment is calculated by dividing the total task duration by the length of the time block, i.e. 40 / 60=0.666, which represents the task occupancy rate of path segment A1 in the time block, which is 66.6%. The start and end time of tasks of 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. The task duration is 40 minutes and 35 minutes, respectively. The total length of the block is also 60 minutes. Therefore, the task occupancy rates are 0.666 and 0.583, respectively. In order to clearly show the use intensity of each path segment in the scheduling cycle, the statistical results are shown in Table 3:

[0090] Table 3 Path segment task block occupancy rate table

[0091]

[0092] As shown in Table 3, the task occupancy rates of path segments A1 and A2 are both 66.7%, and the task occupancy rate of path segment A3 is 58.3%. Based on this, a task occupancy intensity record table of each path segment in each block in the scheduling cycle is established, and path segment task occupancy rate distribution data is obtained.

[0093] The density level labeling sub-module calculates the average task occupancy rate of all blocks of the path segment based on the path segment task occupancy rate distribution data, taking the occupancy rate of each path segment in different time blocks as a reference. The time blocks greater than the average value are selected, and the corresponding path segment is marked as a high-density section. The conflict level is divided according to the number of blocks and the density level, and a path segment conflict density level table is established.

[0094] According to the path segment task occupancy rate distribution data, first, the task occupancy rate of each path segment in all time blocks is extracted item by item, the mean value of the task occupancy rate of each path segment is calculated, and path segment A1 is taken as an example. The occupancy rate in block 1 is 0.667. If multiple blocks are divided and the occupancy rate sequence is calculated as [0.667, 0.5, 0.7], the mean value is (0.667+0.5+0.7) / 3=0.622. Similarly, the mean value of each path segment is calculated. The task occupancy rate of each time block is compared with the mean value of the corresponding path segment. If the occupancy rate of a block is higher than the mean value, the time block is marked as a high-density block. For example, 0.7>0.622 in A1, so the block is determined to be a high-density block, and A1 path segment is marked as a conflict. Finally, according to the number of high-density block appearances and distribution of each path segment, the path segment is divided into grades according to the unified density classification standard. 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. The grade information of all path segments is sorted to establish the path segment conflict density grade table.

[0095] Please refer to Figure 5 , the time window adjustment module comprises:

[0096] The path density identification submodule extracts the path segment number bound by the current task to be scheduled, compares the path segment density grade value item by item, identifies the path segment number set 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.

[0097] Based on the path segment conflict density grade table, first, the path segment number of the current task to be scheduled is extracted. These path segment numbers are closely related to the actual task. Each task corresponds to one or more path segments, especially those with a density grade higher than the set threshold. In actual application scenarios, some path segments may be considered as "high-density" path segments due to excessive task quantity or high task density. For example, in the operation area of a mine, some tasks are scheduled multiple times, and frequent scheduling results in a high degree of task overlap on the path segment. Therefore, these high-density path segments must be identified first. For example, in the scheduling process of task T1, the task of path segment 1 is repeatedly scheduled multiple times. The system identifies that the conflict density value of this path segment exceeds the set benchmark by querying the path segment conflict density grade table, determines that it is a high-density path segment, and generates a high-density path segment number set. Further, through the path segment number set, the scheduling history of the related path segment can be obtained, and it can be clearly presented which path segments have been frequently scheduled by multiple tasks and the specific density grade of these path segments. This process mainly relies on the scheduling history data of the task and the conflict density information of the path segment to ensure that all path segments with high-density scheduling problems can be accurately identified.

[0098] The candidate time generation submodule obtains the original planned start and end times of the current task as a 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 time length to form two candidate time windows, extracts path segment task distribution time information in each candidate time window, and statistically analyzes the number of tasks and time distribution of each path segment. The formula is:

[0099]

[0100] The overlapping density value D of the path segment in the candidate time window x is calculated x , the calculation results of the two time windows before and after are combined, the path segment scheduling conflict distribution evaluation index is established, and the path segment candidate time density value is obtained, wherein e k and s k are the end time and start time of the path segment scheduled by the task k, m x represents the total amount of task coverage of the path segment in the time window x, q x is the time span of the candidate time window x, is the total time length of all tasks in the time window x, and N is the number of path segments participating in the task in the candidate time window x.

[0101] According to the high-density path segment number set, the original planned start and end times of the current task are extracted from the path segment-task mapping list and used as a basic time window. After the basic time window, a fixed time period, for example, 20 minutes, is extended forward to form a pre-candidate time window. Similarly, the same period is extended backward to form a post-candidate time window, and the pre-candidate and post-candidate time windows are constructed. For example, assuming that the original planned time of the task k is from 0 minutes to 30 minutes, the pre-candidate time window is -20 to 30 minutes, and the post-candidate time window is 0 to 50 minutes, which covers the time interval before and after the task, facilitating the evaluation of the task scheduling conflict of the path segment in these time windows. Next, the system extracts the scheduling task information of each path segment according to the path segments involved in the candidate time windows, and calculates the task density of the path segment in these time windows. In this process, the number of tasks and the duration of the path segment in the candidate time window need to be counted. The more the number of tasks and the longer the duration, the higher the density of the path segment, and the higher the coincidence degree of task scheduling.

[0102] Assuming that the task k is on the path segment 1, and the original planned scheduling time is from 0 minutes to 30 minutes. The pre-candidate time window is -20 minutes to 30 minutes, and the post-candidate time window is 0 minutes to 50 minutes. Taking the path segment 1 as an example, the task overlapping density of the pre-candidate time window is calculated.

[0103] The start time of the task k is 0 minutes, and the end time is 30 minutes;

[0104] Path segment 1 has task 1 once in this time period, and the time span of task 1 is 30 minutes;

[0105] Time window span q x = 60 minutes (total length of the preceding candidate time window);

[0106] Time span of task in time window and minutes (i.e., task duration of task k);

[0107] Substitute into the formula:

[0108]

[0109] The calculation result D1 = 0.0172 indicates that the task overlap density of path segment 1 in the preceding candidate time window is 0.0172. This shows that the task density of path segment 1 is low and the task coverage is scattered in this time window. This value can provide a basis for subsequent conflict avoidance period selection. A lower density value means that there is less task overlap in this time window, and the scheduling conflict risk of path segment 1 is low, which is suitable as a candidate time window for adjustment.

[0110] The window optimization screening submodule compares the density values of the preceding and following 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 meet the condition that the density values of all path segments are lower than the density level of the current time window, and takes them as conflict adjustment available intervals to obtain path conflict avoidance execution periods;

[0111] According to the path segment candidate time density value, the task densities of the preceding and following candidate time windows are compared with the path segment density level of the current task time window one by one. In this process, the path segment task density of the current time window is compared with the path segment task density in the candidate time window, and the candidate time window with a lower path segment density is screened out as a reconstruction window. For example, the path segment task density of the current time window is 0.03, and the density of the candidate time window is 0.0172, so the density of the former is higher, and it needs to be adjusted to a low-density time window. Through this process, a candidate time window with a task density lower than the current time window is finally selected as a reconstruction window to ensure that tasks will not be scheduled in a high-density section, reduce the scheduling conflict of the path segment, and thus generate path conflict avoidance execution periods, which are used for subsequent path segment scheduling to ensure that the conflict problem of the path segment is effectively avoided and the execution order of the task is optimized.

[0112] Please refer to Figure 6 , the operation and maintenance scheduling matching module comprises:

[0113] The device and personnel scheduling submodule reads the device scheduling plan and personnel allocation information associated with the current task based on the path conflict avoidance execution period, identifies the available state of each device and personnel in the target time period, and obtains the position of the task node to generate device and personnel available state information;

[0114] First, the scheduling information of the current task needs to be read, and the device and personnel scheduling plan associated with the task is extracted. Assuming that there are multiple devices and personnel available for scheduling in the mine, the devices include mine internal transportation devices, maintenance tools, etc., and the personnel include miners and technical support personnel, etc. By comparing these information, it can be determined whether the device and personnel are available in the target time period. Next, based on the scheduling state information of the device and personnel, it is associated with the task node position. This process first involves identifying the working time window of each device and personnel, and checking its overlap with the current task time period. As a simple example, if the working period of device a is 12:00 to 16:00, and the scheduling period of the current task is 13:00 to 14:00, then device a is available and can be assigned to the task. At this time, if the position between device a and the task node is feasible, that is, its running range matches the path segment required by the task, then device a will be identified as a device that meets the scheduling conditions. After this operation, a usable device and personnel state information table is finally generated, which contains the available time, task node position, etc. of each device and personnel, providing basic data support for subsequent task scheduling, and finally forming the device and personnel available state information.

[0115] The path shortest distance calculation submodule calculates the shortest reachable path distance and time of the device to the target path segment according to the current position of the device and personnel based on the device and personnel available state information, sorts the path distance and time of each device and personnel, and obtains the path shortest reachable distance and time value;

[0116] First, the current location of the device or personnel needs to be obtained, which can be obtained through the real-time positioning system of the device and personnel. For example, the current location of device 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 device or personnel, the shortest reachable path distance and time to the target path segment are calculated. The path segment here can be represented by the road network graph of the mine, and device a and personnel B need to find the shortest travel path according to the network graph. Assuming that the shortest path from the current location of device a to the target path segment is 200 meters, and the shortest path from the current location of device b to the target path segment is 300 meters. At this time, through comparison, device a is considered to be the closest device to the target path segment. During the calculation of the path, the speed of the device, the traffic conditions (such as road conditions, slope, etc.) will affect the required time. Assuming that the average travel speed of device a is 30 meters / minute, and the average travel speed of device b is 25 meters / minute, then the time required by device a is 200 meters / 30 meters / minute = 6.67 minutes, and the time required by device b is 300 meters / 25 meters / minute = 12 minutes. Therefore, the shortest reachable time of device a is 6.67 minutes, and the shortest reachable time of device B is 12 minutes. Through such calculation, the shortest reachable path time between each device and the task path segment can be obtained, and then sorted. The final obtained path shortest reachable distance and time value is the shortest path time of each device or personnel, which can ensure the efficiency in the scheduling process.

[0117] The scheduling adaptation screening submodule sorts all devices and personnel according to the path shortest reachable distance and time value, preferentially selects devices and personnel closest to the target path segment and whose paths do not involve conflict path segments as the scheduling matching unit, and adapts to the task to generate a mine operation and maintenance resource scheduling adaptation scheme;

[0118] Based on the path shortest reachable distance and time value calculated as described above, all devices and personnel are sorted. This sorting not only sorts according to the shortest path distance, but also needs to consider whether the path involves a conflict path segment. Here, a conflict path segment list is set, which contains path segments that are not available for the current task (for example, due to maintenance, congestion, etc.). When the path of each device or personnel passes through these path segments, these path segments need to be avoided. Assuming that the shortest reachable path time of device a is 6.67 minutes, and the path segment is not in conflict, then device a can be preferentially scheduled. If the shortest reachable path time of device b is 12 minutes, and the path segment has a conflict path segment, then the device will be excluded. In this way, devices and personnel whose paths do not involve conflict path segments and whose shortest reachable times are the smallest are selected. Finally, a mine operation and maintenance resource scheduling adaptation scheme can be obtained, which contains the optimal matching and task allocation of devices and personnel, ensuring the optimal configuration of time and resources and improving the efficiency of mine operation and maintenance.

[0119] The above merely describes the preferred embodiments of the present application, but does not limit the present application in other forms. Any person skilled in the art can make changes or modifications to the above disclosed technical contents into equivalent embodiments with equivalent changes, and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the technical solution content of the present application still falls within the protection scope of the present application.

Claims

1. A cloud platform for operation and maintenance services of intelligent mine systems, characterized by: The cloud platform 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, analyzes the change trend between nodes based on the fluid flow direction, and establishes a node pressure response association table; The path task mapping module extracts the downhole path segment structure information based on the inter-node pressure response association table, assigns a path segment direction label, and extracts the task number bound to the 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 repeated scheduling of path segments based on the path segment-task mapping list, calculates the task occupancy rate of the path segment in each time block, identifies 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 tasks based on the path segment conflict density level table, determines the path segment numbers of the high-density sections, takes the originally planned start and end times of the tasks as the basic time window, extends it forward and backward to form candidate time windows, calculates the task overlap and density level of the path segments within the candidate time windows, compares the distribution of the path segments to determine the reconstruction window, and generates the path conflict avoidance execution period.

2. The operation and maintenance service cloud platform of the mine intelligent system according to claim 1 is characterized in that: The inter-node pressure response association 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 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 reconstruction time window.

3. The operation and maintenance service cloud platform of the mine intelligent system according to claim 1 is characterized in that: The node pressure monitoring module includes: The pressure data acquisition submodule 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; The pressure difference calculation submodule merges the time series pressure data of adjacent nodes based on 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 based on the difference relationship between the pressure values, and performs trend direction judgment operations in combination with the fluid flow direction labels between the nodes to generate a pressure difference trend judgment record with consistent direction; The correlation trend extraction submodule combines the direction-consistent pressure difference trend judgment record, and performs a screening operation on the trend values ​​between nodes based on the relationship between the pressure difference between nodes and the flow direction, and extracts a set of node pairs with consistent directions using the formula: Calculate the trend response intensity value R of node i i , identify and classify the node pairs whose response intensity values ​​exceed the trend intensity benchmark value, establish a mapping relationship, and obtain the pressure response association table between nodes, where P i Represents the pressure difference between node i and its adjacent nodes, D i Represents the flow direction scalar of node i, V i represents the pressure fluctuation amplitude of node i, T i Represents the total pressure change of node i during the entire task cycle.

4. The operation and maintenance service cloud platform of the mine intelligent system according to claim 1 is characterized in that: The path task mapping module includes: The path segment structure extraction submodule identifies node pairs with correlation values ​​based on the inter-node pressure response association table, locates the corresponding connection segments in the downhole spatial structure diagram, extracts path segment structure information according to the continuity of node numbers, organizes the numbers, obtains path segment numbers within the downhole operation range, and generates a path segment structure number set; The path direction labeling submodule compares the positions of the start and end nodes of the path segment on the coordinate axis based on the path segment structure number set and the coordinate axis flow direction rules set in the mine operation task, and performs path segment direction identification operation based on the main flow axis direction as the judgment basis, assigning a corresponding direction label to each path segment to obtain a path segment direction identification table; The task binding extraction submodule extracts the job task number, task start and end time and task execution path information in the operation and maintenance task plan table according to the path segment direction identification table, compares the path segment number with the number field of the plan table path field one by one, 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 is characterized in that: The conflict density identification module includes: The repeated scheduling statistics submodule retrieves the task number information bound to each path segment based on the path segment-task mapping list, summarizes and counts the number of repeated schedulings 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; The block division submodule is used to divide the continuous time blocks within the unified scheduling cycle based on the scheduling frequency value of the path segment and the start and end time of the tasks bound to each path segment. The time coverage segments of the task numbers in each time block are extracted, the number of covered tasks is counted, and their task durations are accumulated using the formula: Calculate the task occupancy rate U of the path segment in time block j j , establish the usage density distribution of the path segment scheduling period according to the calculation results of each block, and obtain the path segment task occupancy rate distribution data, where, e k With s k are the end time and start time of task k in time block j, t j is the total time length of time block j, n is the total number of times the path segment is scheduled by tasks in time block j; The density level labeling submodule calculates the average task occupancy rate of all blocks in the path segment based on the task occupancy rate distribution data of the path segment and the occupancy rate of each path segment in different time blocks as a reference. It selects time blocks with a value greater than the average, marks the corresponding path segment as a high-density segment, and divides the conflict level according to the number of blocks and the degree of density to establish a path segment conflict density level table.

6. The operation and maintenance service cloud platform of the mine intelligent system according to claim 1 is characterized in that: The time window adjustment module includes: The path density identification submodule extracts the path segment numbers bound to the current 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 submodule obtains the original planned start and end time of the current task as the basic time window based on the high-density path segment number set, extends the set time forward from the starting point and backward from the end point to form two candidate time windows, extracts the time distribution information of the path segment tasks within each candidate time window, and counts the number of tasks and time distribution of each path segment, and uses the formula: Calculate the overlap density value D of the path segments within the candidate time window x x , combined with the calculation results of the two time windows, the path segment scheduling conflict distribution evaluation index is established to obtain the candidate time density value of the path segment, where, e k With s k is the end time and start time of the path segment scheduled by task k, m x represents the total number of task coverage times of the path segment within the time window x, q x is the time span of the candidate time window x, is the total duration of all tasks in the time window x, and N is the number of tasks involved in the path segments in the candidate time window x; The window optimization screening submodule compares the density values ​​of the previous and next 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, and screens the candidate time windows that meet the requirement that the density values ​​of all path segments 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 is characterized in that: The cloud platform also includes an operation and maintenance scheduling and matching module; The operation and maintenance scheduling matching module reads all equipment scheduling plans and personnel deployment information associated with the mine task based on the path conflict avoidance execution period, determines the available status of equipment and personnel and the location of their respective task nodes 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, selects the equipment and personnel with the shortest distance and path that does not involve the conflicting path segment as the matching unit, and obtains the mine operation and maintenance resource scheduling adaptation plan; The mine operation and maintenance resource scheduling adaptation plan includes equipment number, personnel number, shortest reachable path number, and path conflict verification result.

8. The operation and maintenance service cloud platform of the mine intelligent system according to claim 7 is characterized in that: The operation and maintenance scheduling matching module includes: The equipment and personnel scheduling submodule reads the equipment scheduling plan and personnel deployment information associated with the current task based on the path conflict avoidance execution period, identifies the availability status of each device and personnel within the target time period, obtains the location of the corresponding task node, and generates equipment and personnel availability status information; The shortest path distance calculation submodule calculates the shortest reachable path distance and reachable time from the device to the target path segment based on the available status information of the device and personnel and the current location of the device and personnel, sorts the path distance and time of each device and personnel, and obtains the shortest reachable path distance and time value; The scheduling adaptation screening submodule sorts all equipment and personnel according to the shortest reachable distance and time value of the path, prioritizes equipment and personnel that are closest to the target path segment and whose paths do not involve conflicting path segments as scheduling matching units, and adapts them to the tasks to generate a mine operation and maintenance resource scheduling adaptation plan.

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