A battery replacement scheduling method and system for unmanned mine cars

Abstract

This invention belongs to the field of vehicle scheduling and provides a method and system for battery swapping scheduling of unmanned mining trucks. The method includes: retrieving mine map information and equipment status information to obtain map resource data and equipment status data; performing a hash pre-caching query on the coordinates of the task to be scheduled based on the equipment status data to obtain cached path results; performing dynamic collision detection on the cached path results based on the map resource data to obtain valid path data; making a closed-loop decision on battery swapping demand based on the valid path data to obtain a battery swapping scheduling instruction; and scheduling the mining truck to be scheduled based on the battery swapping scheduling instruction. This invention significantly shortens the path calculation response time, dynamically adapts to changes in the mine environment, and ensures that the battery swapping scheduling decision is synchronized with the on-site execution status in real time.

Description

Technical Field

[0001] This invention belongs to the field of vehicle scheduling technology, and particularly relates to a battery swapping scheduling method and system for unmanned mining trucks. Background Technology

[0002] With the development of autonomous driving technology, the mining transportation sector is entering a new stage of large-scale application. Compared with traditional manual driving, autonomous driving technology effectively solves the problem of driver fatigue caused by long-term operation in harsh environments and significantly reduces the risk of safety accidents.

[0003] However, as an emerging technology, the current unmanned mining truck system still faces challenges in terms of insufficient technological maturity in some details, and urgently needs to be improved through continuous technological innovation and practical verification. In particular, for battery-swapping mining trucks, this type of mining truck is of great significance for extending the driving range and reducing downtime. However, due to the short time that battery-swapping mining trucks have been put into operation, the corresponding battery swapping management technology is not yet fully developed.

[0004] In existing battery swapping management technologies, path planning algorithms suffer from high computational overhead and significant response delays when faced with large-scale mining area maps or high-frequency scheduling requests. Furthermore, existing collision detection mechanisms are mostly based on static maps or perception data with low update frequency, which cannot identify newly emerging obstacles in a timely manner or can only achieve passive obstacle avoidance, lacking the ability to actively predict and adaptively adjust. In addition, existing systems are mostly in open-loop mode, with simulation results directly sent to the actual vehicle for execution. This can lead to discrepancies between the actual operating conditions and simulation assumptions, which can easily cause scheduling failures or even safety accidents. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of the invention is to provide a battery swapping scheduling method and system for unmanned mining trucks.

[0006] This invention provides a battery swapping scheduling method for unmanned mining trucks, comprising:

[0007] S1: Retrieve mining farm map information and equipment status information to obtain map resource data and equipment status data;

[0008] S2: Based on the device status data, perform a hash pre-caching query on the coordinates of the task to be scheduled to obtain the cache path result;

[0009] S3: Based on the map resource data, perform dynamic collision detection on the cached path results to obtain valid path data;

[0010] S4: Make a closed-loop decision on the battery swapping demand based on the effective path data, and obtain a battery swapping scheduling instruction;

[0011] S5: Dispatch the mining truck to be dispatched based on the battery swapping dispatch instruction.

[0012] According to the present invention, a battery swapping scheduling method for unmanned mining trucks is provided, wherein step S1 specifically includes:

[0013] Data is retrieved from the mining platform via API interface to obtain map resource data of obstacles, loading area, unloading area, battery swapping station and lane;

[0014] Device information is collected in real time via a TCP long connection to obtain device status data including latitude and longitude coordinates, remaining power, and scheduling status.

[0015] According to the present invention, a battery swapping scheduling method for unmanned mining trucks, step S2 further includes:

[0016] S21: Generate a normalized key value based on the coordinates of the task to be scheduled, and obtain a hash lookup key; wherein the coordinates of the task to be scheduled include the latitude and longitude coordinates of the minecart to be scheduled and the latitude and longitude coordinates of the task target;

[0017] S22: Using an offline hash table, perform a pre-cached path retrieval on the hash query key to obtain the cached path result.

[0018] According to the present invention, a battery swapping scheduling method for unmanned mining trucks is provided, wherein step S21 further includes:

[0019] S211: Extract the coordinates of the task to be scheduled, and perform coordinate granularity standardization on the latitude and longitude coordinates of the task target to obtain standardized coordinate data;

[0020] S212: Perform string concatenation processing on the standardized coordinate data to obtain the hash lookup key.

[0021] According to the present invention, a battery swapping scheduling method for unmanned mining trucks is provided, wherein step S22 further includes:

[0022] S221: Based on the historical operation data of the mining platform, pre-calculate the regular start and end path to obtain the path distance cache set;

[0023] S222: Store and manage the path distance cache set according to the hash index to obtain the cache path result.

[0024] According to the present invention, a battery swapping scheduling method for unmanned mining trucks, step S3 further includes:

[0025] S31: Based on the map resource data, the obstacles are segmented into line segments to obtain a set of obstacle line segments;

[0026] S32: The collision detection algorithm is used to determine the intersection between the cached path result and the set of obstacle segments to obtain a path validity result including invalid and valid paths;

[0027] S33: The invalid coordinate pairs of the path are replanned using the A-star algorithm to obtain the valid path data.

[0028] According to the present invention, a battery swapping scheduling method for unmanned mining trucks, step S33 further includes:

[0029] S331: Calculate the node evaluation function based on the spherical distance metric to obtain the total cost including the actual cost and the estimated cost;

[0030] S332: The optimal path node sequence with the minimum total agent value is obtained by filtering the total agent value.

[0031] According to the present invention, a battery swapping scheduling method for unmanned mining trucks, the detailed steps of step S4 are as follows:

[0032] S41: Make an initial decision on battery swapping demand based on the effective path data and the equipment status data, and obtain battery swapping flag data;

[0033] S42: By real-time status monitoring, deviation detection is performed on the battery swapping flag data to obtain the execution deviation result;

[0034] S43: Based on the execution deviation result, perform closed-loop correction on the battery swapping flag data to obtain the battery swapping scheduling instruction.

[0035] According to the present invention, a battery swapping scheduling method for unmanned mining trucks is provided, wherein the battery swapping flag data in step S41 includes a True flag indicating that battery swapping is required and a False flag indicating that battery swapping is not required.

[0036] Step S41 specifically includes:

[0037] Force the scheduled mining trucks that are in the battery swapping execution state to be marked with the True flag;

[0038] The battery swapping decision model is used to determine the timing of battery swapping based on the remaining battery power and path distance of the mining trucks to be dispatched in the operation state, and to obtain battery swapping flag data.

[0039] Step S43 specifically includes:

[0040] When the battery swapping flag is True and the scheduled mining truck is performing a non-battery swapping task, the True flag will be changed to False.

[0041] When the battery swapping flag is set to False and the mining truck performs a battery swapping task, the False flag will be changed to True.

[0042] The present invention also provides a battery swapping scheduling system for unmanned mining trucks, comprising:

[0043] Acquisition module: Used to retrieve mining farm map information and equipment status information, and obtain map resource data and equipment status data;

[0044] Cache module: used to perform hash pre-caching query on the coordinates of the task to be scheduled based on the device status data, and obtain the cache path result;

[0045] Detection module: used to perform dynamic collision detection on the cached path results based on the map resource data to obtain valid path data;

[0046] Decision module: used to make closed-loop decisions on battery swapping demand based on the effective path data and obtain battery swapping scheduling instructions;

[0047] Scheduling module: Used to schedule the mining trucks to be scheduled based on the battery swapping scheduling instructions.

[0048] This invention provides a battery swapping scheduling method and system for unmanned mining trucks. Firstly, by constructing a path planning mechanism based on hash pre-caching query processing, the origin-end point coordinates of the mining truck and the task objective are converted into normalized hash query keys. An offline hash table is then established to pre-process historical operational data. This significantly shortens the path calculation response time when facing high-frequency scheduling demands from large-scale mining truck clusters, avoiding the computational bottleneck problem of traditional real-time path planning algorithms in complex mining environments, and greatly improving the overall scheduling system's operational efficiency. Simultaneously, the dynamic collision detection processing technology employed in this invention, by segmenting obstacles into line segments and judging their intersection with the cached path results, can promptly identify newly appearing temporary obstacles or map changes in the mining environment, ensuring that the pre-cached path remains in place in the complex and ever-changing mining operation environment. Ultimately, it maintains effectiveness and safety, effectively reducing safety risks and operational interruptions caused by path failures. Furthermore, the closed-loop correction mechanism established in this invention, by monitoring the consistency between the mine car's execution status and the battery swapping flag data in real time, can automatically identify execution deviations caused by disturbances such as on-site manual intervention and temporary scheduling changes. Through forward and reverse correction processing, it achieves dynamic synchronization between the decision-making and execution layers, effectively solving the problem that traditional open-loop scheduling systems cannot adapt to actual changes on-site. This ensures long-term consistency between the battery swapping scheduling strategy and actual production, reducing economic losses caused by decision-making deviations. Simultaneously, by using a battery swapping decision model to calculate the timing of battery swapping based on the mine car's remaining power and path distance, it achieves more precise energy consumption management and operational time optimization, further improving the overall economic efficiency and operational stability of mine transportation operations. Attached Figure Description

[0049] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. It is obvious that the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings.

[0050] Figure 1 This is a schematic diagram of a battery swapping scheduling method for unmanned mining trucks provided in an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of a battery swapping scheduling system for unmanned mining trucks, provided as an embodiment of the present invention.

[0052] Figure label:

[0053] 100. Acquisition Module; 200. Caching Module; 300. Detection Module; 400. Decision Module; 500. Scheduling Module. Detailed Implementation

[0054] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0055] Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts disclosed in this invention.

[0056] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0057] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of methods and systems consistent with some aspects of the invention as detailed in the appended claims.

[0058] To better understand this invention, the research background of this invention will be explained in detail below.

[0059] With the development of autonomous driving technology, the mining transportation sector is entering a new stage of large-scale application. Compared with traditional manual driving, autonomous driving technology effectively solves the problem of driver fatigue caused by long-term operation in harsh environments and significantly reduces the risk of safety accidents. This technological revolution increases effective operating time and greatly improves the overall transportation efficiency of mines. However, as an emerging technology, current unmanned mining truck systems still face challenges in terms of technological maturity in some details, and urgently need to be improved through continuous technological innovation and practical verification.

[0060] Furthermore, the high fuel costs of traditional fuel-powered trucks severely restrict the progress of mines in improving production efficiency. Currently, battery-swapping mining trucks are gradually being introduced into mining operations, and electric mining trucks with battery swapping capabilities are already in operation. This type of mining truck is of great significance for extending range and reducing downtime. However, due to the relatively short time that battery-swapping mining trucks have been in operation, the corresponding technology is not yet fully mature, and exploration in related fields is still ongoing.

[0061] Currently, in the research on battery swapping and dispatching systems for electric unmanned mining trucks, several technical solutions have been proposed and applied, mainly including the following categories:

[0062] Battery swapping management based on static path planning and scheduling systems: Most existing systems adopt pre-planned paths and scheduling strategies, and arrange mining trucks to go to battery swapping stations for battery swapping through a unified scheduling center. Such systems are usually based on map information and vehicle status (such as battery level and location) for centralized scheduling.

[0063] Scheduling pre-run and verification based on simulation platform: A simulation platform is introduced to pre-run the scheduling strategy to verify its feasibility and efficiency under different operating conditions. The simulation results are used to guide actual scheduling decisions and improve the robustness and adaptability of the system.

[0064] Feedback mechanism based on real-time monitoring: Some systems deploy onboard sensors and roadside sensing devices to achieve real-time monitoring of vehicle operation status and feed back abnormal information (such as path deviation and obstacle appearance) to the dispatch center for adjustment of subsequent dispatch plans.

[0065] The existing technology has the following problems and shortcomings:

[0066] Slow path planning response: Existing path planning algorithms suffer from high computational overhead and significant response latency when dealing with large-scale mining area maps or high-frequency scheduling requests. This is especially true in multi-vehicle collaborative battery swapping scenarios, where frequent path conflicts and the lack of efficient conflict resolution mechanisms lead to a decline in overall scheduling efficiency. Furthermore, most systems do not effectively utilize historical path data, requiring recalculation for each planning iteration and lacking rapid indexing and reuse capabilities.

[0067] Insufficient dynamic obstacle detection capability: The mining environment is complex and ever-changing, with a large number of temporary obstacles (such as operating machinery, stockpiles, landslides, etc.). Existing collision detection mechanisms are mostly based on static maps or perception data with a low update frequency, which cannot identify newly emerging obstacles in a timely manner, or can only achieve passive obstacle avoidance, lacking the ability to actively predict and adaptively adjust.

[0068] The simulation is disconnected from the actual vehicle operation and lacks closed-loop feedback: Most existing systems are in "open-loop" mode: the simulation results are directly sent to the actual vehicle for execution, lacking the ability to perceive and correct deviations in actual operation, such as positioning errors, equipment failures, and human interference in real time. Once the on-site working conditions do not match the simulation assumptions, the system cannot dynamically adjust its strategy, leading to scheduling failures or even safety accidents.

[0069] The embodiments of the present invention are described below with reference to the figures.

[0070] like Figure 1 As shown, the present invention provides a battery swapping scheduling method for unmanned mining trucks, comprising:

[0071] S1: Retrieve mining farm map information and equipment status information to obtain map resource data and equipment status data.

[0072] Specifically, step S1 includes: pulling data from the mining platform via API interface to obtain map resource data of obstacles, loading area, unloading area, battery swapping station and lane; and collecting equipment information in real time via TCP long connection to obtain the equipment status data including latitude and longitude coordinates, remaining power and scheduling status.

[0073] In step S1 of this invention, for complex mines with large scale and multiple devices, a dual-channel data base of "high-precision digital map - real-time equipment status" is established to provide a unified spatial benchmark for subsequent path planning, battery swapping decisions and closed-loop control.

[0074] Specifically, this invention connects to the mining platform via an API interface with HTTP / HTTPS+Token authentication. Then, based on the connected platform, it pulls data from the entire mining farm according to functional semantics and parses it into map resources such as obstacles, loading areas, unloading areas, battery swapping stations, and lanes. At the same time, this invention also subscribes to the mining data center based on a TCP long connection to obtain real-time status information of equipment such as mining trucks and excavators, including latitude and longitude coordinates, remaining power, and scheduling status, providing the battery swapping decision model with actual and complete data input from the production site.

[0075] S2: Based on the device status data, perform a hash pre-cache query on the coordinates of the task to be scheduled to obtain the cache path result.

[0076] In step S2 of this invention, based on the mine car coordinate information and task allocation information in the device status data obtained from the TCP long connection, the current position coordinates of the mine car to be scheduled and the target position coordinates to be reached are extracted to form a task coordinate pair to be scheduled. Subsequently, this invention processes these coordinate pairs through two sub-steps: hash key generation and offline cache query, and finally outputs a cached path result containing path node sequence and distance information, thus avoiding the time overhead of real-time path calculation.

[0077] Step S2 further includes:

[0078] S21: Generate a normalized key value based on the coordinates of the task to be scheduled, and obtain a hash lookup key; wherein the coordinates of the task to be scheduled include the latitude and longitude coordinates of the minecart to be scheduled and the latitude and longitude coordinates of the task target.

[0079] In step S21, the present invention receives coordinate pairs of tasks to be scheduled. Through two processing steps, coordinate truncation and string concatenation, the original double-precision floating-point coordinate data is converted into a standardized string format hash lookup key. This not only unifies the coordinate precision specifications and eliminates the impact of floating-point precision differences on hash lookup, but also ensures that the generated hash lookup key has uniqueness and reproducibility.

[0080] Step S21 further includes:

[0081] S211: Extract the coordinates of the task to be scheduled, and perform coordinate granularity standardization on the latitude and longitude coordinates of the task target to obtain standardized coordinate data.

[0082] Specifically, in step S221, the present invention receives the latitude and longitude coordinates of the mine car to be scheduled and the latitude and longitude coordinates of the task target extracted from the equipment status data. The coordinate data is stored in double-precision floating-point format with a precision of 15 decimal places. The present invention performs a numerical truncation operation on these original coordinate data, specifically truncating the decimal part of the longitude and latitude values ​​to the third decimal place, i.e., retaining three decimal places and discarding all numbers from the fourth decimal place onwards. For example, the original longitude coordinate 114.123456789 will be truncated to 114.123, and the original latitude coordinate 22.987654321 will be truncated to 22.987. The truncation precision corresponds to a spatial resolution of approximately 100 meters, which meets the accuracy requirements of mine car path planning while effectively compressing the storage space of the hash table. After the truncation process, each task to be scheduled generates four standardized values: starting longitude, starting latitude, ending longitude, and ending latitude, forming standardized coordinate data.

[0083] S212: Perform string concatenation processing on the standardized coordinate data to obtain the hash lookup key.

[0084] In step S212, the standardized numerical values ​​output in step S211 of this invention are concatenated into a string according to a fixed format. The concatenation format adopts the structure "starting longitude_starting latitude_ending longitude_ending latitude", using underscores as delimiters. During the concatenation process, this invention ensures that the string conversion of numerical values ​​maintains the original precision and avoids precision loss during floating-point number conversion. The concatenated string serves as a hash lookup key, possessing uniqueness and repeatability; identical coordinate pairs always generate the same hash lookup key.

[0085] S22: Using an offline hash table, perform a pre-cached path retrieval on the hash query key to obtain the cached path result.

[0086] In step S22, this invention utilizes a pre-built offline hash table data structure, receives the hash query key as an index, and uses hash function calculation and key-value matching to find the corresponding pre-cached path data. The offline hash table stores regular path information pre-calculated based on historical running data. The query process directly returns the cached path node sequence and distance data, realizing the rapid acquisition of path information.

[0087] Step S22 further includes:

[0088] S221: Based on the historical operation data of the mining platform, pre-calculate the regular start and end point paths to obtain a path distance cache set.

[0089] Furthermore, in step S221, the present invention first obtains historical operation data from the mining platform. The data includes all scheduling task records executed by the mining trucks in the past period. Each record includes the task execution time, mining truck identification, starting coordinates, ending coordinates, actual travel path, and total path distance. Subsequently, the historical data is statistically analyzed to identify frequently occurring starting-ending coordinate pairs, which represent regular transportation paths within the mine. Then, for each regular starting-ending coordinate pair, the present invention calls the A-star path planning algorithm for offline pre-calculation. In the calculation, the A-star algorithm receives the starting coordinates, ending coordinates, and the current map information as input, and outputs path data containing the path node sequence and total distance. The final path node sequence records all intermediate coordinate points passed from the starting point to the ending point, and the total distance is calculated by accumulating the values ​​using the Haversine formula, generating a large number of path-distance data pairs. These data pairs constitute a path distance cache set.

[0090] S222: Store and manage the path distance cache set according to the hash index to obtain the cache path result.

[0091] Specifically, the hash table uses the hash query key generated in step S212 as the index key and stores the corresponding path data and distance data as values. During storage, the present invention converts the path node sequence into a compressed format to reduce storage space, and retains the distance data to meter-level precision. The hash table uses open addressing to handle key-value collisions. When multiple different coordinate pairs generate the same hash value, the next available storage location is found through linear probing. After storage, the hash table supports fast query operations. When a hash query key is received, the present invention directly looks up the corresponding path and distance data through the key value. If the lookup is successful, the cached path result is returned. If the lookup fails, an empty value is returned to indicate that the coordinate pair has no pre-cached data. The final output cached path result contains the complete path node coordinate sequence, the total path distance, and the map version information used when calculating the path.

[0092] S3: Based on the map resource data, perform dynamic collision detection on the cached path results to obtain valid path data.

[0093] In step S3, the present invention receives the cached path results output in step S2 and the map resource data obtained in step S1. Through three sub-steps, namely obstacle geometry decomposition, path collision detection and failed path replanning, the path in the cached path results that may conflict with the current map obstacles is identified and updated. Finally, valid path data that is fully compatible with the current map environment is output.

[0094] Step S3 further includes:

[0095] S31: Based on the map resource data, the obstacles are segmented into line segments to obtain a set of obstacle line segments.

[0096] In step S31, the present invention first extracts the geometric shape information of obstacles from map resource data, and then splits the polygon boundary of each obstacle into multiple line segment units to generate a set of obstacle line segments that are easy to calculate the intersection of line segments. The splitting process aims to simplify the complex polygon collision detection problem into a problem of judging the intersection of multiple line segments.

[0097] Specifically, this invention reads obstacle data from map resource data. Each obstacle is stored in polygon format, containing a series of sequentially connected vertex coordinates. Obstacle types include temporary material piles, work equipment, buildings, and terrain protrusions, each type of obstacle represented as a closed polygon. After acquiring the data, this invention performs boundary segment extraction on each obstacle polygon. The specific process is as follows: read the vertex coordinate sequence of the polygon, and generate line segments between adjacent vertices according to the vertex connection order. For example, a quadrilateral obstacle has vertices A, B, C, and D, which are split into four line segments: AB, BC, CD, and DA. Each line segment is represented by its start and end coordinates, forming a line segment data structure. In step S31, this invention performs the same splitting operation on all obstacles, summarizing all the split line segments to form an obstacle line segment set. Each element in the set contains the start and end latitude and longitude coordinates of the line segment, the end latitude and longitude coordinates, and the identification information of the obstacle to which it belongs.

[0098] S32: The collision detection algorithm is used to determine the intersection between the cached path result and the set of obstacle segments to obtain a path validity result including invalid and valid paths.

[0099] In step S32, the present invention uses a line segment intersection detection algorithm in plane geometry to determine the intersection of each path in the cached path result with each line segment in the obstacle line segment set, and generates a path validity result indicating whether the path collides with the obstacle.

[0100] Specifically, this invention receives a sequence of path nodes from the cached path results. Each path contains a series of intermediate node coordinates from the starting point to the ending point. Subsequently, this invention converts the path node sequence into a sequence of path segments. The specific method is as follows: take the coordinates of two adjacent nodes as the starting and ending points of the line segment to generate the path line segment. For example, the path node sequence P1→P2→P3 is converted into two path line segments P1P2 and P2P3.

[0101] After conversion, this invention performs intersection detection on each path segment and each obstacle segment in the obstacle segment set. In this invention, the intersection detection uses a vector cross product algorithm. The specific calculation steps are as follows: Let the path segment be AB and the obstacle segment be CD. Calculate the cross product value V1 of vector AC and vector AB, calculate the cross product value V2 of vector AD and vector AB, calculate the cross product value V3 of vector CA and vector CD, and calculate the cross product value V4 of vector CB and vector CD. When V1 and V2 have opposite signs and V3 and V4 have opposite signs, the two segments are determined to intersect.

[0102] Finally, after performing a complete collision detection on each cached path, the present invention generates a path validity result. If any line segment in the path intersects with any obstacle line segment, the path is marked as "invalid". If none of the line segments in the path intersect with any obstacle line segment, the path is marked as "valid".

[0103] S33: The invalid coordinate pairs of the path are replanned using the A-star algorithm to obtain the valid path data.

[0104] In step S33, the present invention calls the A-star path planning algorithm to recalculate the path based on the current map resource data for the coordinate pairs marked as "path invalid" in the path validity results, and generates new path data that is compatible with the current obstacle distribution.

[0105] Step S33 further includes:

[0106] S331: Calculate the node evaluation function based on the spherical distance metric to obtain the total cost including the actual cost and the estimated cost.

[0107] Specifically, total cost Actual cost Estimated cost The actual cost The estimated cost is the actual path distance between the node and the starting point. The distance between a node and its destination is represented by a straight-line distance. The distance between any two points is calculated using the Haversine formula based on spherical trigonometry to eliminate errors caused by large-scale mine planar projection. The total value of each node in the final output reflects the comprehensive cost of that node in the optimal path search; the smaller the total value, the more likely the node is to be on the optimal path.

[0108] S332: The optimal path node sequence with the minimum total agent value is obtained by filtering the total agent value.

[0109] In step S332, the present invention maintains an open node list to store nodes to be expanded. Each node contains coordinates, total value, and parent node pointer information. The present invention selects the node with the smallest total value from the open node list as the current expanded node, removes it from the open list, and adds it to the visited node list. Subsequently, the present invention generates the adjacent nodes of the current node, which are the next nodes that the current node can directly reach in the lane direction. In addition, the present invention performs obstacle collision detection on each adjacent node. If an adjacent node collides with an obstacle, the node is discarded. For adjacent nodes that pass the collision detection, the present invention determines the path based on the total value calculated in step S331. If the adjacent node is not in the open list, it is directly added to the open list. If the adjacent node is already in the open list, the total value is compared and the optimal path is updated.

[0110] This invention repeatedly executes the node expansion process until the target node is added to the open list, indicating that a complete path has been found. Finally, this invention backtracks from the target node to the starting node using the parent node pointer to generate a complete path node sequence. The path node sequence is arranged in order from the starting point to the ending point, and each node contains latitude and longitude coordinates. The total path distance is obtained by summing the distances between adjacent nodes, forming the optimal path node sequence.

[0111] The neighborhood generation and collision detection process of this invention will be explained below with reference to a specific process.

[0112] In the neighborhood generation and collision detection process of this invention, the current node is generated along the lane travel direction. The set of adjacent nodes For any If node If a node fails obstacle collision detection or is not already in the searched node set, then ignore it. If node If the node is not in the set of nodes to be searched, then the node will be... Add nodes to the set to be searched and calculate their total cost. Actual cost and estimated costs If node If the set of nodes to be searched is incomplete, then check the current node. To the node The actual cost Is it smaller? If so, update the node. The actual cost and estimated costs Termination condition test, test endpoint If the path exists in the set of nodes to be searched, output the path and distance. Otherwise, check if the set of nodes to be searched is empty. If it is, the path does not exist; otherwise, return and continue calculating the total cost. Update.

[0113] S4: Make a closed-loop decision on the battery swapping demand based on the effective path data, and obtain the battery swapping scheduling instruction.

[0114] In step S4, the present invention receives the valid path data output in step S3 and the equipment status data obtained in step S1. Through the initial battery swapping decision, deviation status monitoring and closed-loop flag correction sub-steps, the static battery swapping demand judgment is transformed into a dynamic closed-loop scheduling mechanism, and finally outputs a battery swapping scheduling instruction that is consistent with the actual execution status on site.

[0115] Step S4 further includes:

[0116] S41: Make an initial decision on battery swapping demand based on the effective path data and the equipment status data, and obtain battery swapping flag data.

[0117] The battery swapping flag data in step S41 includes a True flag indicating that a battery swap is needed and a False flag indicating that a battery swap is not needed.

[0118] Furthermore, in step S41, the present invention generates initial battery swapping flag data for each mining truck to be scheduled based on the mining truck scheduling status information in the equipment status data and the calculation results of the battery swapping decision model. The battery swapping flag data adopts Boolean value form, with the True flag indicating that the mining truck needs to perform the battery swapping task and the False flag indicating that the mining truck does not need to perform the battery swapping task.

[0119] Step S41 specifically includes: forcibly marking the scheduled mining trucks in the battery swapping execution state as True flag bits; determining the timing of battery swapping based on the remaining power and path distance of the scheduled mining trucks in the operation state through the battery swapping decision model, and obtaining battery swapping flag bit data.

[0120] This invention reads the scheduling status field of each online mining truck from the equipment status data. The scheduling status field records the task type currently being executed by the mining truck. This invention classifies the scheduling status and identifies mining trucks in the battery swapping execution state. The battery swapping execution state includes two status values: "heading to battery swapping station" and "battery swapping in progress". When the scheduling status field value of a mining truck matches these two statuses, this invention marks the mining truck as being in forced battery swapping mode. This invention directly assigns a True flag bit to mining trucks in forced battery swapping mode without further decision calculation, forming a set of forced battery swapping flag bits. For mining trucks whose scheduling status is not in the battery swapping execution state, this invention classifies them as mining trucks in the operation state. A battery swapping decision model is needed to determine the timing of battery swapping. Specifically, the battery swapping decision model receives the remaining battery power data and effective path data of the mining truck as input parameters. The remaining battery power data is extracted from the equipment status data and expressed as a percentage of the current battery power level of the mining truck. The effective path data contains the path distance information of the mining truck to the next task target. After obtaining the input parameters, the battery swapping decision model uses a power consumption prediction algorithm to calculate the power consumption required to complete the task based on the path distance and the power consumption rate per unit distance of the mining truck. The predicted power consumption value is compared with the current remaining battery power. When the remaining battery power is lower than the predicted power consumption plus a safety margin, the battery swapping decision model outputs a battery swapping demand determination result of True; otherwise, it outputs False. Finally, this invention merges the forced battery swapping flag set with the output result of the battery swapping decision model to form battery swapping flag data containing the battery swapping status of all mining trucks. The storage format of the battery swapping flag data is a key-value pair structure of mining truck identifier and Boolean value.

[0121] S42: By real-time status monitoring, deviations in the battery swapping flag data are detected to obtain execution deviation results.

[0122] This invention establishes a real-time status monitoring mechanism, continuously receiving scheduling status update data from mining trucks via a long TCP connection. The status update data includes the mining truck identifier, timestamp, and new scheduling status value. This invention maintains a mining truck status history record table, recording the sequence of scheduling status changes for each mining truck. When status update data is received, this invention compares the new status value with the historical record to identify status transition events. A status transition event indicates that the mining truck has changed from one scheduling status to another. This invention defines deviation detection rules based on the consistency between the battery swapping flag data and the actual scheduling status. The first type of deviation is a positive deviation: the mining truck's battery swapping flag is True, but the actual scheduling status changes to a non-battery swapping related status such as "heading to loading," "loading," or "heading to unloading," indicating that a battery swapping decision has not been adopted on-site. The second type of deviation is a negative deviation: the mining truck's battery swapping flag is False, but the actual scheduling status changes to a battery swapping related status such as "returning to parking lot," "heading to battery swapping station," or "battery swapping," indicating that a battery swapping decision has been added on-site. This invention performs deviation detection rule matching on each state transition event, identifies the deviation type, and records the time of deviation occurrence. The deviation execution result includes the minecart identifier where the deviation occurred, the deviation type (positive or negative), and the deviation detection timestamp.

[0123] S43: Based on the execution deviation result, perform closed-loop correction on the battery swapping flag data to obtain the battery swapping scheduling instruction.

[0124] Specifically, step S43 includes: when the battery swapping flag is True and the scheduled mining truck is performing a non-battery swapping task, the True flag is corrected to the False flag; when the battery swapping flag is False and the mining truck is performing a battery swapping task, the False flag is adjusted to the True flag.

[0125] Further, in step S43, the present invention receives the deviation type information from the execution deviation results and performs corresponding flag correction operations according to the deviation type. For positive deviations, i.e., when the mine car's battery swapping flag is True but a non-battery swapping task is being performed, the present invention determines that the mine car has been changed to a work task by on-site manual or system scheduling and no longer needs to perform a battery swapping. The present invention corrects the mine car's battery swapping flag from True to False, and simultaneously marks the correction reason as "on-site cancellation of battery swapping" and the correction timestamp in the correction record. For negative deviations, i.e., when the mine car's battery swapping flag is False but a battery swapping task is being performed, the present invention determines that an additional battery swapping decision has been made on-site according to actual production needs. The present invention adjusts the mine car's battery swapping flag from False to True, and simultaneously marks the correction reason as "on-site addition of battery swapping" and the correction timestamp in the correction record. After the correction operation is completed, the present invention updates the battery swapping flag database to ensure that the battery swapping flags of all mine cars are consistent with the actual execution status.

[0126] Finally, this invention generates a battery swapping scheduling instruction. The instruction includes a list of mining trucks that need to perform battery swapping, the battery swapping priority of each mining truck, and the expected battery swapping time window. The battery swapping scheduling instruction is encapsulated in JSON format and includes metadata information such as instruction generation time, validity period, and execution parameters.

[0127] S5: Dispatch the mining truck to be dispatched based on the battery swapping dispatch instruction.

[0128] This invention converts battery swapping dispatch instructions into a data format compatible with the mining platform and sends the instruction data to the interface endpoint of the mining platform via an HTTP POST request. After receiving the instructions, the mining platform dispatches and arranges the mining vehicles on site according to the instructions, including route allocation, battery swapping station resource reservation, and operation time coordination. This invention simultaneously monitors the execution status of the instructions, records the issuance time, execution feedback, and completion status of the dispatch instructions, forming a complete dispatch execution record.

[0129] The following describes the overall implementation process of the battery swapping scheduling method for unmanned mining trucks according to the present invention, using a specific process as an example.

[0130] A: Map and device information acquisition.

[0131] The map information acquisition includes the following steps:

[0132] A1: Map Feature Extraction: Extract high-precision data from the mine and analyze map resources such as obstacles, loading areas, unloading areas, battery swapping stations, and lane centerlines to provide a unified and updatable spatial benchmark for subsequent path planning.

[0133] The process of acquiring device information includes the following steps:

[0134] A2: Equipment Information Acquisition: Key status parameters such as the latitude and longitude of mining trucks, remaining power, and the latitude and longitude of excavators are obtained through real-time data streams, providing dynamic input for path planning and battery swapping decisions.

[0135] B: Select an online mining vehicle and construct its normalized key relative to the task objective:

[0136] Select an online mining truck based on rules such as the mining truck serial number, obtain the specific latitude and longitude coordinates of its task target, and construct a normalized key between it and the task target for querying the cache path in the hash table.

[0137] C: Determine whether the key exists in the pre-cached hash table. If yes, proceed to step D; otherwise, proceed to step F.

[0138] D: Determine whether the cache path passes obstacle collision detection. If yes, proceed to step E; otherwise, proceed to step F.

[0139] The obstacle collision detection includes the following steps:

[0140] D1: Obstacle Decomposition: Obtain the number of obstacles based on map information. Among them, obstacles For the reason A closed polygon formed by the edges. The obstacle is broken down, generating a total of... Line segment units provide geometric primitives for subsequent path-obstacle collision detection.

[0141] D2: Collision Detection: A planar line segment intersection detection algorithm is used to determine whether there is an intersection between the cache path and any obstacle line segment unit. If there is no intersection, the collision detection is passed and step E is executed; otherwise, the collision detection fails and step F is executed.

[0142] E: Read the cache path and distance from the pre-cached hash table.

[0143] F: Using the latitude and longitude coordinates of the starting point (mine car) and the ending point (task objective) as input, the A-star algorithm is used to determine the path and distance based on the latest map information.

[0144] G: Determine whether all minecart path planning is complete. If yes, proceed to step H; otherwise, return to step B.

[0145] H: Determine the battery swapping decision model:

[0146] By integrating with the existing battery swapping decision model, and based on the path planning results and vehicle status, a decision instruction on whether to swap batteries is output.

[0147] I: Determine the initial flag for the mine car battery swapping decision:

[0148] Mining vehicles with a scheduling status of "heading to battery swapping station" or "swapping battery" are considered to be required to go to battery swapping by the mining platform and are forcibly included in the battery swapping sequence, with the flag set to True; otherwise, the flag is set to False.

[0149] J: Determine if a mining car has completed battery swapping or unloading. If yes, proceed to step K; otherwise, proceed to step N.

[0150] K: Update the decision flag for mine truck battery swapping:

[0151] If the mining truck completes the battery swap, the flag changes from True to False; if the mining truck completes unloading, the battery swap decision model determines whether a battery swap is needed. If so, the flag changes from False to True; otherwise, the flag remains unchanged.

[0152] L: Determine whether the production site adopts the battery swapping decision. If yes, proceed to step N; otherwise, proceed to step M.

[0153] M: Correct battery swapping decision flag

[0154] If the mine car battery swapping demand flag is True and the mine car's subsequent status is "heading to loading", adjust the flag to False; if the mine car battery swapping demand flag is False and the mine car's subsequent status is "returning to parking lot" or "heading to battery swapping station", adjust the standard flag to True.

[0155] N: Synchronous battery swapping decision.

[0156] O: Determine whether the production site operation has ended. If yes, end the process; otherwise, return to step J.

[0157] like Figure 2 As shown, the present invention also provides a battery swapping scheduling system for unmanned mining trucks, comprising:

[0158] Module 100: Used to retrieve mining farm map information and equipment status information, and obtain map resource data and equipment status data;

[0159] Cache module 200: Used to perform hash pre-caching query on the coordinates of the task to be scheduled based on the device status data, and obtain cache path results;

[0160] Detection module 300: used to perform dynamic collision detection on the cached path results based on the map resource data to obtain valid path data;

[0161] Decision module 400: used to make closed-loop decisions on battery swapping demand based on the effective path data and obtain battery swapping scheduling instructions;

[0162] Scheduling module 500: Used to schedule the mining truck to be scheduled based on the battery swapping scheduling instruction.

[0163] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0164] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0165] This invention constructs an offline hash pre-caching mechanism to read long-term operating data from the mining platform and pre-store the cache path and cache distance of the start-end pair. This breaks through the computational bottleneck of traditional path planning in large-scale, high-frequency mining truck scheduling scenarios, accelerates the response during the operation phase, significantly reduces the delay in battery swapping decisions, and improves overall operational efficiency.

[0166] This invention introduces a dynamic obstacle verification method based on hash pre-caching. The query performs real-time collision detection between the cached path and the latest obstacles. If a conflict is found, the system will replan locally and update the cache. This overcomes the limitation that static maps cannot adapt to dynamic changes in the mining field, ensuring that path planning remains effective in scenarios with frequent changes in map resources, and significantly improving the system's robustness to complex environments.

[0167] This invention designs a simulation-real vehicle closed-loop feedback mechanism to continuously collect the actual vehicle scheduling status, maps execution deviations to the decision-making level, and promptly corrects the battery swapping demand flag, forming a dynamic closed loop of "decision-execution-feedback-adjustment". This overcomes the shortcomings of traditional open-loop battery swapping scheduling systems that cannot respond to on-site disturbances, and ensures that the battery swapping strategy is consistent with actual production in the long term.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A battery replacement scheduling method for unmanned mine cars, characterized in that, include: S1: Retrieve mining farm map information and equipment status information to obtain map resource data and equipment status data; S2: Based on the device status data, perform a hash pre-caching query on the coordinates of the task to be scheduled to obtain the cache path result; Step S2 further includes: S21: generating a normalized key value based on the coordinates of the task to be scheduled, and obtaining a hash query key; wherein the coordinates of the task to be scheduled include the latitude and longitude coordinates of the minecart to be scheduled and the latitude and longitude coordinates of the task target; S22: performing a pre-cached path retrieval on the hash query key through an offline hash table to obtain a cached path result; Step S21 further includes: S211: Extracting the coordinates of the task to be scheduled, and performing coordinate granularity standardization processing on the latitude and longitude coordinates of the task target to obtain standardized coordinate data; S212: Performing string concatenation processing on the standardized coordinate data to obtain the hash lookup key; Step S22 further includes: S221: Pre-calculating the regular start-end point path based on the historical operation data of the mining platform to obtain a path distance cache set; S222: Storing and managing the path distance cache set according to a hash index to obtain the cached path result; S3: Based on the map resource data, perform dynamic collision detection on the cached path results to obtain valid path data; Step S3 further includes: S31: dividing obstacles into line segments according to the map resource data to obtain a set of obstacle line segments; S32: using a collision detection algorithm to determine the intersection between the cached path result and the set of obstacle line segments to obtain a path validity result including invalid and valid paths; S33: using the A-star algorithm to replan the coordinate pairs of invalid paths to obtain the valid path data. S4: Make a closed-loop decision on the battery swapping demand based on the effective path data, and obtain a battery swapping scheduling instruction; Step S4 further includes: S41: making an initial decision on the battery swapping demand based on the effective path data and the equipment status data, and obtaining battery swapping flag data; S42: sensing the deviation of the battery swapping flag data through real-time status monitoring, and obtaining the execution deviation result; S43: performing closed-loop correction on the battery swapping flag data based on the execution deviation result, and obtaining the battery swapping scheduling instruction. The battery swapping flag data in step S41 includes a True flag indicating that a battery swap is needed and a False flag indicating that a battery swap is not needed. Step S41 specifically includes: forcibly marking the scheduled mining trucks in the battery swapping execution state as True flag bits; using the battery swapping decision model to determine the timing of battery swapping based on the remaining power and path distance of the scheduled mining trucks in the operation state, and obtaining battery swapping flag bit data; Step S43 specifically includes: When the battery swapping flag is True and the scheduled mining truck is performing a non-battery swapping task, the True flag is changed to the False flag; when the battery swapping flag is False and the mining truck is performing a battery swapping task, the False flag is changed to the True flag. S5: Dispatch the mining truck to be dispatched based on the battery swapping dispatch instruction.

2. The battery replacement scheduling method for unmanned mine cars according to claim 1, characterized in that, Step S1 specifically includes: Data is retrieved from the mining platform via API interface to obtain map resource data of obstacles, loading area, unloading area, battery swapping station and lane; Device information is collected in real time via a TCP long connection to obtain device status data including latitude and longitude coordinates, remaining power, and scheduling status.

3. The battery replacement scheduling method for unmanned mine cars according to claim 1, characterized in that, Step S33 further includes: S331: Calculate the node evaluation function based on the spherical distance metric to obtain the total cost including the actual cost and the estimated cost; S332: The optimal path node sequence with the minimum total agent value is obtained by filtering the total agent value.

4. A battery replacement scheduling system for unmanned mine cars, configured to perform a battery replacement scheduling method for unmanned mine cars according to any one of claims 1 to 3, characterized in that, include: Acquisition module: Used to retrieve mining farm map information and equipment status information, and obtain map resource data and equipment status data; Cache module: used to perform hash pre-caching query on the coordinates of the task to be scheduled based on the device status data, and obtain the cache path result; Detection module: used to perform dynamic collision detection on the cached path results based on the map resource data to obtain valid path data; Decision module: used to make closed-loop decisions on battery swapping demand based on the effective path data and obtain battery swapping scheduling instructions; Scheduling module: Used to schedule the mining trucks to be scheduled based on the battery swapping scheduling instructions.

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