A scheduling method and device for hybrid driving mine trucks

Abstract

The application discloses a scheduling method and device for mixed driving mine trucks, and relates to the technical field of mine truck scheduling. Network data in a mine area is acquired, and the mine area is divided to obtain a strong network area and a weak network area. The proportion of manned mine trucks in the weak network area is acquired to determine the total number of weak network manned mine trucks to be scheduled. The total number of weak network manned mine trucks is scheduled to the weak network area, and the unmanned mine trucks are limited to enter the weak network area. The proportion of unmanned mine trucks in the strong network area is acquired to determine the total number of strong network unmanned mine trucks to be scheduled. The total number of strong network unmanned mine trucks is scheduled to the strong network area. The unmanned mine trucks and the manned mine trucks are controlled to work cooperatively in the weak network area and the strong network area. The provided technical solution reduces the collision risk of the mine trucks.

Description

Technical Field

[0001] This application relates to the field of mining truck scheduling technology, specifically to a scheduling method and apparatus for hybrid driving mining trucks. Background Technology

[0002] In mining operations, frequent large-scale material transfers involve the transport of bulk materials such as ore, waste rock, and topsoil. Due to the unique nature of mining transportation—massive single-transport volumes and extremely complex operating environments—traditional transportation equipment struggles to meet the demands for efficient and safe material transfer. To address this challenge, mining trucks, with their ultra-heavy-duty load-bearing platforms and reinforced chassis structures, have become core equipment in the mining transportation system.

[0003] Traditional mining trucks typically operate in manned mode. Due to the dynamic and uncertain nature of mine road conditions, frequent responses to unexpected situations are necessary during operations. Human drivers, through real-time environmental perception and experience-based judgment, can perform obstacle avoidance, path correction, and emergency response in complex scenarios. However, in high-risk mining areas, personnel are directly exposed to the working environment, facing significant safety risks. To balance transportation efficiency and operational safety, unmanned mining trucks have emerged. With the maturity of unmanned driving technology, some mining areas have begun to adopt a hybrid driving mode, deploying both unmanned and manned mining trucks simultaneously within the same mining area. To ensure both transportation efficiency and operational safety, the scheduling system needs to coordinate the operations of both types of equipment. The current mainstream scheduling strategy is based on the "proximity allocation" principle, prioritizing the use of available mining trucks closest to the task's starting point. However, this strategy does not fully consider the differences in the operational characteristics of the two types of equipment: unmanned mining trucks rely on pre-set electronic fence communication for obstacle avoidance, while manned mining trucks have dynamic and random path selection. In overlapping operation areas, the lack of an effective coordination mechanism can easily lead to collisions due to path conflicts, thereby reducing overall transportation efficiency. Summary of the Invention

[0004] This application provides a scheduling method and apparatus for hybrid driving mining trucks, which solves the problems of collision risk and low transportation efficiency caused by the lack of coordination mechanism in cross-operation areas.

[0005] In a first aspect, this application provides a method for scheduling hybrid driving mining trucks, the method comprising: acquiring network data in a mining area; performing cluster analysis on the network data to obtain a first cluster center and a second cluster center; determining a boundary threshold based on the first cluster center and the second cluster center; dividing the mining area based on the boundary threshold to obtain a strong network area and a weak network area, wherein the signal strength of the strong network area is higher than that of the weak network area; acquiring the proportion of manned mining trucks in the weak network area; determining the total number of manned mining trucks to be scheduled in the weak network area based on the proportion of manned mining trucks; scheduling the total number of manned mining trucks to be scheduled in the weak network area to the weak network area, and restricting unmanned mining trucks from entering the weak network area; controlling manned and unmanned mining trucks to work collaboratively in the weak network area; acquiring the proportion of unmanned mining trucks in the strong network area; determining the total number of unmanned mining trucks to be scheduled in the strong network area based on the proportion of unmanned mining trucks; scheduling the total number of unmanned mining trucks to be scheduled in the strong network area to the strong network area; and controlling unmanned and manned mining trucks to work collaboratively in the strong network area.

[0006] By adopting the above technical solutions, boundary thresholds are determined based on cluster analysis. The mining area is then divided according to these thresholds, resulting in strong network areas and weak network areas. This enables scientific zoning and management of the mining area. Strong network areas ensure the communication needs of unmanned mining trucks, while weak network areas are matched to the characteristics of manned mining trucks. The proportion of manned mining trucks in weak network areas is obtained, and the total number of manned mining trucks to be scheduled in weak network areas is determined based on this proportion. Unmanned mining trucks are restricted from entering weak network areas to reduce the probability of path conflicts. Similarly, the proportion of unmanned mining trucks in strong network areas is obtained, and the total number of unmanned mining trucks to be scheduled in strong network areas is determined based on this proportion. The scheduling quantity is dynamically determined based on the regional proportions, rationally allocating the work tasks of unmanned and manned mining trucks. This avoids excessive concentration of a single vehicle, reduces waiting time caused by overlapping operations, avoids systemic risks based on differentiated scheduling, ensures operational safety through a collaborative mechanism, reduces the risk of mining truck collisions, and ultimately improves overall transportation efficiency.

[0007] Optionally, the total number of manned mining trucks to be scheduled in weak network conditions is determined based on the proportion of manned mining trucks. Specifically, this includes: when the proportion of manned mining trucks is less than a preset threshold for manned mining trucks, acquiring real-time terrain images of the weak network area, extracting multiple weak network roads from the terrain images, calculating the length of each weak network road in the weak network area; calculating the quotient of the weak network road length of each weak network road and the length of a single mining truck to obtain the number of mining trucks that each weak network road can accommodate, and using the sum of the number of mining trucks that each weak network road can accommodate as the total number of mining trucks that can be accommodated in the weak network area; using the difference between the total number of mining trucks that can be accommodated in the weak network area and the number of manned and unmanned mining trucks in the weak network area as the total number of manned mining trucks to be scheduled in weak network conditions; when the proportion of manned mining trucks is greater than or equal to the preset threshold for manned mining trucks, confirming that the total number of manned mining trucks to be scheduled in weak network conditions is zero.

[0008] By adopting the above technical solution, real-time topographic images of weak network areas are acquired and weak network roads are extracted, ensuring the timeliness and accuracy of the data. The number of mining trucks a road can accommodate is obtained by calculating the ratio of the actual area of ​​each weak network road to the length of a single mining truck. This precise calculation method based on actual terrain avoids errors that may arise from traditional empirical estimations. The number of mining trucks that can be accommodated on each weak network road is then added together to obtain the total number of mining trucks that can be accommodated in weak network areas. The relationship between the proportion of manned mining trucks and a preset threshold for manned mining trucks in weak network areas is compared to automatically determine whether additional scheduling is needed. When the proportion of manned mining trucks is less than the preset threshold, the difference in the number of manned mining trucks is determined as the total number of manned mining trucks to be scheduled in weak network areas. When the proportion of manned mining trucks is greater than or equal to the preset threshold, the total number of manned mining trucks to be scheduled in weak network areas is determined to be zero, preventing road congestion problems that may result from over-scheduling. This achieves unified optimization of road resource utilization and scheduling efficiency, improving scheduling efficiency.

[0009] Optionally, controlling manned and unmanned mining trucks to operate collaboratively in weak network areas includes: obtaining the weak network transportation location corresponding to the weak network transportation task in the weak network area, obtaining road condition information around the weak network transportation location, evaluating the road condition information, and obtaining a safety score; obtaining the weight and size of the transported materials from the weak network transportation task, obtaining road parameters from the road condition information, and weighting and summing the road parameters, transported material weight, and size to obtain an operation complexity score; obtaining the target signal strength corresponding to the weak network transportation location, and obtaining the total number of weak network dispatched mining trucks required to execute the weak network transportation task; filtering out the number of idle manned mining trucks and unmanned mining trucks in the weak network area; if the safety score, operation complexity score, and target signal strength all meet the weak network transportation conditions for unmanned mining trucks, and the number of idle unmanned mining trucks in the weak network area is less than the total number of weak network dispatched mining trucks, then subtracting the number of unmanned mining trucks in the weak network area from the total number of weak network dispatched mining trucks. The number of idle mining trucks is used to determine the required number of basic mining trucks for weak network conditions. From the weak network area, the required number of manned mining trucks for basic weak network conditions are retrieved. The number of unmanned mining trucks available for weak network conditions is combined with the number of manned mining trucks required for basic weak network conditions to form a basic weak network transport truck fleet. This fleet then travels to the weak network transport location to perform weak network transport tasks. If the safety score, operational complexity score, and target signal strength do not meet the weak network transport conditions for unmanned mining trucks, and the number of idle manned mining trucks in the weak network is less than the total number of mining trucks scheduled for the weak network, then the total number of mining trucks scheduled for the weak network is subtracted from the number of idle manned mining trucks in the weak network to obtain the required number of standard weak network mining trucks. From the weak network retrieval area, the required number of manned mining trucks for standard weak network conditions are retrieved. The number of unmanned mining trucks available for weak network conditions is combined with the number of unmanned mining trucks required for standard weak network conditions to form a standard weak network transport truck fleet. This fleet then travels to the weak network transport location to perform weak network transport tasks.

[0010] By adopting the above technical solution, the road condition information around the weak network transportation location is evaluated to obtain a safety score. Considering the actual needs of transportation operations, the weight, size, and road parameters of the materials are weighted and summed to obtain an operation complexity score. Then, the target signal strength of the weak network transportation location is obtained. The safety score, operation complexity score, and target signal strength are compared with the weak network transportation conditions of the unmanned mining trucks. When the conditions are met, available unmanned mining trucks are used first, and manned mining trucks are supplemented according to actual needs to form a mixed fleet. When the conditions are not met, manned mining trucks are prioritized, and they can also be dispatched from the weak network area for supplementation. Based on accurate task evaluation and vehicle allocation, transportation efficiency is improved, unnecessary vehicle dispatching is reduced, and resource utilization is optimized through flexible fleet combination strategies.

[0011] Optionally, a weak-network basic transportation truck fleet is formed by combining the number of unmanned mining trucks available in the weak-network unmanned mining truck fleet with the number of manned mining trucks required for the weak-network basic transportation truck fleet. Specifically, this includes: obtaining the location of each unmanned mining truck in the weak-network unmanned mining truck fleet based on the available number of unmanned mining trucks; calculating the transportation distance between the location of the unmanned mining truck in the weak-network unmanned mining truck fleet and the transportation location in the weak-network unmanned mining truck fleet; selecting the maximum value from multiple transportation distances as the theoretical maximum transportation distance; determining the maximum transportation distance that the unmanned mining truck can normally travel; if the maximum transportation distance is greater than or equal to the theoretical maximum transportation distance, it is confirmed that the unmanned mining truck can normally complete the weak-network transportation task, and the unmanned mining truck in the weak-network unmanned mining truck fleet is then deployed. The number of unmanned mining trucks available is combined with the number of manned mining trucks required for the weak network basic mining truck fleet to obtain the weak network basic transportation mining truck fleet. If the maximum transportation distance is less than the theoretical maximum transportation distance, the minimum value among the confirmed multiple transportation distances is selected as the theoretical minimum transportation distance. If the maximum transportation distance is greater than or equal to the theoretical minimum transportation distance, the target unmanned mining truck corresponding to the theoretical minimum transportation distance is retrieved. The total number of mining trucks scheduled for the weak network is subtracted from the number of target unmanned mining trucks to obtain the number of mining trucks required for the weak network correction. The target unmanned mining trucks are combined with the number of manned mining trucks required for the weak network correction to obtain the weak network basic transportation mining truck fleet.

[0012] By adopting the above technical solution, the real-time location of unmanned mining trucks in weak network conditions is obtained, the transportation distance between each vehicle and the weak network transportation location is calculated, and a matching mechanism between vehicle capacity and task requirements is established by judging the maximum transportation distance. Based on the evaluation method of actual transportation distance, the feasibility of task allocation is ensured. When the maximum transportation distance is greater than or equal to the theoretical maximum transportation distance, the number of unmanned mining trucks available in weak network conditions is directly combined with the number of manned mining trucks required for the basic transportation trucks in weak network conditions to form a basic transportation truck fleet in weak network conditions. When the maximum transportation distance is less than the theoretical maximum transportation distance, the system automatically switches to the minimum theoretical minimum transportation distance. When the maximum transportation distance is greater than or equal to the theoretical minimum transportation distance, the unmanned mining truck corresponding to the theoretical minimum transportation distance is selected, and then the target unmanned mining truck is combined with the number of manned mining trucks required for the weak network correction mining trucks to form a basic transportation truck fleet in weak network conditions. This ensures that the optimal fleet combination scheme is found. By rationally allocating unmanned and manned mining trucks, the optimal allocation of transportation capacity is achieved, and the success rate of task completion is improved.

[0013] Optionally, the total number of unmanned mining trucks to be scheduled in the strong network is determined based on the proportion of unmanned mining trucks. Specifically, this includes: when the proportion of unmanned mining trucks is less than a preset threshold for unmanned mining trucks in the strong network area, real-time acquisition of terrain information within the strong network area is performed, multiple strong network roads are parsed from the terrain information, and the length of each strong network road in the strong network area is calculated; the quotient of the strong network road length of each strong network road and the length of a single mining truck is calculated to obtain the number of mining trucks that each strong network road can accommodate, and the sum of the number of mining trucks that each strong network road can accommodate is taken as the total number of strong network mining trucks that can be accommodated in the strong network area; the difference between the total number of strong network mining trucks and the number of manned and unmanned mining trucks in the strong network area is taken as the total number of unmanned mining trucks to be scheduled in the strong network area; when the proportion of unmanned mining trucks is greater than or equal to the preset threshold for unmanned mining trucks in the strong network area, the total number of unmanned mining trucks to be scheduled in the strong network area is confirmed to be zero.

[0014] By adopting the above technical solution, based on real-time terrain information, and by analyzing the strong network roads and calculating the actual occupied area, an accurate road capacity assessment system was established. The number of mining trucks that a road can accommodate was obtained by calculating the ratio of the actual area of ​​each strong network road to the length of a single mining truck. The total number of mining trucks that each strong network road can accommodate was then added together. The relationship between the proportion of unmanned mining trucks and a preset unmanned mining truck threshold was compared to automatically determine whether additional unmanned mining truck scheduling was needed. When the proportion of unmanned mining trucks was less than the preset threshold for unmanned mining trucks in the strong network area, the total number of unmanned mining trucks to be scheduled was accurately calculated based on the difference between the total number of strong network mining trucks in the strong network roads and the number of existing vehicles in the strong network area. When the proportion of unmanned mining trucks was greater than or equal to the preset threshold for unmanned mining trucks in the strong network area, scheduling was automatically stopped, effectively preventing resource waste caused by over-scheduling.

[0015] Optionally, control unmanned and manned mining trucks to work collaboratively in the strong network area. Specifically, this includes: obtaining the strong network transportation location corresponding to the strong network transportation task in the strong network area, obtaining road information around the strong network transportation location, evaluating the road information, and obtaining a road score; obtaining multiple weather parameters from the current weather information in the strong network area, weighting and summing the multiple weather parameters to obtain an impact score; obtaining the material type from the strong network transportation task, and determining the total number of strong network dispatched mining trucks required to execute the strong network transportation task; filtering out the number of idle manned mining trucks and unmanned mining trucks in the strong network area; if the road score, impact score, and material type all meet the strong network transportation conditions for unmanned mining trucks, and the number of idle unmanned mining trucks in the strong network area is less than the total number of strong network dispatched mining trucks, then subtract the strong network... The number of idle unmanned mining trucks is used to determine the required number of basic mining trucks for the strong network. The required number of unmanned mining trucks for the strong network's basic mining trucks is then retrieved from the strong network's retrieval area. These unmanned mining trucks, along with the number of idle unmanned mining trucks for the strong network's basic mining trucks, form a strong network basic transport mining truck fleet. This fleet then travels to the strong network transport location to perform strong network transport tasks. If the road score, impact score, and material type all meet the strong network transport conditions for unmanned mining trucks, and the number of idle manned mining trucks for the strong network is less than the total number of mining trucks scheduled for the strong network, then the required number of unmanned mining trucks for the strong network's standard mining trucks is retrieved from the number of idle unmanned mining trucks for the strong network's unmanned mining trucks. These manned mining trucks, along with the number of idle manned mining trucks for the strong network's standard mining trucks, form a strong network standard transport mining truck fleet. This fleet then travels to the strong network transport location to perform strong network transport tasks.

[0016] By employing the aforementioned technical solution, a road score and impact score are obtained by weighted summation of road conditions and weather parameters around the power grid transportation location. Then, the material type is acquired. The suitability of unmanned mining trucks is determined by evaluating the matching degree between the road score, impact score, and material type and the power grid transportation conditions. Based on the current number of available vehicles and the power grid transportation task requirements, the optimal fleet combination can be automatically selected. When the number of unmanned vehicles is insufficient, they can be replenished from the power grid area; when the number of manned vehicles is insufficient, they can be supplemented with unmanned vehicles. The fleet composition can be dynamically adjusted according to the actual situation, fully utilizing the advantages of unmanned technology while ensuring the smooth completion of the task through reasonable resource allocation.

[0017] Optionally, a standard transportation truck fleet is formed by combining the number of available manned mining trucks in the strong network with the number of unmanned mining trucks required for the strong network standard mining truck fleet. Specifically, this includes: obtaining the location of each unmanned mining truck in the strong network under the available number of unmanned mining trucks; planning the target route between the location of the unmanned mining trucks and the strong network transportation location; dividing the transportation length of each target route by the driving speed of the unmanned mining trucks to obtain the transportation time; selecting a target number of transportation times less than or equal to a preset transportation time from multiple transportation times, where the preset transportation time is the transportation time between the candidate manned mining trucks in the strong network area and the strong network transportation location; if the number of targets is less than the total number of strong network scheduled mining trucks, and the sum of the number of targets and the number of available manned mining trucks in the strong network is equal to or greater than the total number of strong network scheduled mining trucks, then the target number is confirmed as the required number of unmanned mining trucks for the strong network standard mining truck fleet, and the number of available manned mining trucks in the strong network with the number of unmanned mining trucks required for the strong network standard mining truck fleet is formed.

[0018] By adopting the above technical solution, and based on the real-time location information of unmanned mining trucks, an evaluation system centered on transportation timeliness was established through route planning and time calculation. By comparing the calculated transportation time with the preset transportation time, unmanned mining trucks that meet the timeliness requirements can be selected. This timeliness-based selection mechanism not only ensures transportation efficiency but also provides an optimization basis for fleet composition. Especially when considering the matching degree between the number of targets and scheduling needs, precise quantity calculations ensure the rationality of the final standard transportation truck fleet composition. By selecting vehicles with optimal transportation times, the overall transportation time is reduced.

[0019] A second aspect of this application provides a scheduling device for hybrid-driven mining trucks. The device includes an acquisition unit, a processing unit, and a control unit. The acquisition unit acquires network data in a mining area and performs cluster analysis on the network data to obtain a first cluster center and a second cluster center. The processing unit divides the mining area based on a boundary threshold to obtain a strong network area and a weak network area, where the signal strength of the strong network area is higher than that of the weak network area. It determines the boundary threshold based on the first and second cluster centers. It acquires the proportion of manned mining trucks in the weak network area and, based on the manned... The system determines the total number of manned mining trucks to be scheduled in weak network areas based on the proportion of unmanned mining trucks; it then schedules all manned mining trucks scheduled for weak network areas to the weak network region and restricts unmanned mining trucks from entering the weak network region; it obtains the proportion of unmanned mining trucks in strong network areas and determines the total number of unmanned mining trucks to be scheduled in strong network areas based on this proportion; it then schedules all unmanned mining trucks scheduled for strong network areas to the strong network region; it controls the control unit to ensure that manned and unmanned mining trucks work collaboratively in weak network areas; and it controls the collaborative operation of unmanned and manned mining trucks in strong network areas.

[0020] In a third aspect, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory, causing the electronic device to perform any of the methods described above in this application.

[0021] In a fourth aspect, this application provides a computer-readable storage medium storing instructions that, when executed, perform any of the methods described above in this application.

[0022] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Based on cluster analysis, boundary thresholds are determined, and the mining area is divided into strong network areas and weak network areas according to these thresholds. This enables scientific zoning and management of the mining area. Strong network areas ensure the communication needs of unmanned mining trucks, while weak network areas are matched to the characteristics of manned mining trucks. The proportion of manned mining trucks in weak network areas is obtained, and the total number of manned mining trucks to be scheduled in weak network areas is determined based on this proportion. Unmanned mining trucks are restricted from entering weak network areas to reduce the probability of path conflicts. Similarly, the proportion of unmanned mining trucks in strong network areas is obtained, and the total number of unmanned mining trucks to be scheduled in strong network areas is determined based on this proportion. The scheduling quantity is dynamically determined based on the regional proportions, rationally allocating the work tasks of unmanned and manned mining trucks. This avoids excessive concentration of a single vehicle, reduces waiting time caused by overlapping operations, avoids systemic risks through differentiated scheduling, ensures operational safety through a collaborative mechanism, reduces the risk of mining truck collisions, and ultimately improves overall transportation efficiency. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for scheduling hybrid driving mining trucks provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a scheduling device for hybrid driving mining trucks provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0024] Explanation of reference numerals in the attached drawings: 201, acquisition unit; 202, processing unit; 203, control unit; 300, electronic device; 301, processor; 302, memory; 303, user interface; 304, network interface; 305, communication bus. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0026] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0027] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0028] Therefore, how to solve the problems of collision risk and low transportation efficiency caused by the lack of coordination mechanisms in cross-operation areas is a challenge. This application provides a scheduling method for hybrid driving mining trucks, applied in a server. Figure 1 This is a flowchart illustrating a method for scheduling hybrid driving mining trucks provided in an embodiment of this application. (Refer to...) Figure 1 The method includes the following steps S101-S107.

[0029] S101: Obtain network data in the mining area, perform cluster analysis on the network data, and obtain the first cluster center and the second cluster center.

[0030] In step S101 above, multiple data collection points are deployed within the mining area. These points can be fixed network monitoring devices (such as base stations or sensors) or mobile devices, such as network terminals on drones or mining trucks. Network data is acquired from each collection point, including signal strength, network latency, and packet loss rate. The geographical location of each collection point is recorded, with one collection point corresponding to one set of network data. The monitoring data from each collection point is then processed to obtain multiple sets of network data. Since different network data have different units (e.g., signal strength in dBm and latency in ms), the data needs to be standardized to ensure they have the same magnitude. Here, the mining area refers to the area where mining trucks continue to be used for transportation after mining operations have commenced.

[0031] Furthermore, after collecting network data from various collection points in the mining area, cluster analysis is performed on the network data to obtain the first and second cluster centers. Specifically, this involves: obtaining multiple location coordinates and multiple signal strengths from the network data, with one location coordinate corresponding to one signal strength; combining the location coordinates of the unmanned mining truck with the first signal strength to obtain the initial first cluster center; and combining the location coordinates of the unmanned mining truck with the second signal strength to obtain the initial second cluster center. The location coordinates of the unmanned mining truck with the weak network and the location coordinates of the unmanned mining truck with the strong network are any two of the multiple location coordinates, and the first signal strength is the location of the unmanned mining truck with the weak network. The signal strength corresponding to the location coordinates is calculated, and the second signal strength is the signal strength corresponding to the location coordinates of the unmanned mining truck with strong network. The first distance from the target acquisition point to the initial first cluster center and the second distance from the target acquisition point to the initial second cluster center are calculated. When the first distance is greater than the second distance, the target acquisition point is assigned to the first cluster, which is the cluster corresponding to the initial first cluster center. When the first distance is less than the second distance, the target acquisition point is assigned to the second cluster, which is the cluster corresponding to the initial second cluster center. The average value of the acquisition points in the first and second clusters is calculated to obtain the first cluster center and the second cluster center.

[0032] Specifically, when recording data at each collection point, both the location coordinates and signal strength of each point must be recorded simultaneously. Location coordinates refer to the geographical location of the collection point within the mining area, and signal strength is the network signal strength at that location, expressed in dBm. The data for each collection point is organized into a set format: (location coordinates, signal strength). For example, if there are 5 collection points within the mining area, the collected network data is as follows: Collection point A, location coordinates (10,20), signal strength -60dBm; Collection point B, location coordinates (15,25), signal strength -70dBm; Collection point C, location coordinates (30,4), signal strength -85dBm; Collection point D, location coordinates (50,60), signal strength -85dBm; Collection point E, location coordinates (70,80), signal strength -95dBm. First, arbitrarily select two location coordinates from the collection points as the initial weak-network unmanned mining truck location coordinates and the initial strong-network unmanned mining truck location coordinates, and obtain the corresponding signal strengths. The coordinates of the unmanned mining truck with weak network coverage are then combined with the first signal strength to obtain the initial first cluster center. The coordinates of the unmanned mining truck with strong network coverage are combined with the second signal strength to obtain the initial second cluster center. In the example above, if sampling points A and C are selected, the coordinates of the initial first cluster center are (10, 20), with a signal strength of -60 dBm, and the coordinates of the initial second cluster center are (30, 40), with a signal strength of -85 dBm. Then, a sampling point is selected sequentially from other sampling points as the target sampling point, excluding the two sampling points at the initial cluster centers. The distances from the target sampling point to the two initial cluster centers are obtained based on the terrain image.

[0033] For example, a target acquisition point is B, with coordinates (15, 25) and a signal strength of -70 dBm. The distance from target acquisition point B to the initial first cluster center A is obtained from the terrain image. If the first distance is 12.25, the distance from target acquisition point B to the initial second cluster center C is obtained from the terrain image. If the second distance is 25.98, the first and second distances are compared. If the distance from the target acquisition point to the initial first cluster center is less than the distance to the initial second cluster center, then target acquisition point B is assigned to the first cluster, which is the cluster containing the initial first cluster center. If the distance from the target acquisition point to the initial first cluster center is greater than the distance to the initial second cluster center, then it is assigned to the second cluster, which is the cluster containing the initial second cluster center. This acquisition and assignment process is then repeated for other acquisition points in the mining area. For example, regarding target acquisition point B, since the distance from target acquisition point B to the initial first cluster center is 12.25, which is less than the distance to the initial second cluster center is 25.98, target acquisition point B is assigned to the first cluster. The final allocation result is as follows: the first cluster contains acquisition points A and B, and the second cluster contains acquisition points C, D, and E. Then, the average values ​​of the position coordinates and signal strengths are calculated for the acquisition points in the first and second clusters respectively. These calculated average values ​​are then used as the new first and second cluster centers. This process continues until the cluster centers no longer change, at which point the first cluster center corresponding to the first cluster and the second cluster center corresponding to the second cluster can be output. For example, averaging the signal strength and coordinates of the first clusters A and B yields an average coordinate value of (12.5, 22.5) and a signal strength of -65 dBm. The center of the first cluster then also has coordinates of (12.5, 22.5) and a signal strength of -65 dBm. Similarly, averaging the signal strength and coordinates of the second clusters C, D, and E yields an average coordinate value of (50, 60) and a signal strength of -90 dBm. The center of the second cluster then also has coordinates of (50, 60) and a signal strength of -90 dBm. This cluster analysis is used for subsequent precise subdivision of the mining area.

[0034] S102: Determine the boundary threshold based on the first cluster center and the second cluster center.

[0035] In S102 above, after multiple collection points in the mining area are grouped into two classes, the boundary threshold is calculated based on the signal strength values ​​of the first cluster center and the second cluster center. At this time, the boundary threshold can be the intermediate value between the first cluster center and the second cluster.

[0036] Furthermore, since weather factors can affect signal strength, after determining the initial boundary threshold based on the first and second cluster centers, it is necessary to correct the initial boundary threshold based on weather data to obtain the corrected boundary threshold. Determining the boundary threshold based on the first and second cluster centers specifically includes: obtaining the third signal strength from the first cluster center and the fourth signal strength from the second cluster center; calculating the average of the third and fourth signal strengths to obtain the initial boundary threshold; obtaining weather data corresponding to the mining area; determining influencing factors based on the weather data, including rain, fog, wind speed, temperature, and humidity; obtaining the detection values ​​corresponding to each influencing factor; comparing the detection values ​​with preset detection values; determining the signal attenuation value based on the comparison; summing multiple signal attenuation values ​​to obtain the weather correction value; and calculating the initial boundary threshold based on the weather correction value to obtain the boundary threshold.

[0037] Specifically, the third signal strength is first obtained from the first cluster center, which is the average signal strength of the first cluster center. Then, the fourth signal strength is obtained from the second cluster center, which is the average signal strength of the second cluster center. The average of the third and fourth signal strengths is then calculated as the initial signal strength benchmark for dividing the strong and weak network areas. The initial boundary threshold is calculated as (third signal strength + fourth signal strength) / 2. For example, if the third signal strength (first cluster center) is -60 dBm and the fourth signal strength (second cluster center) is -85 dBm, the initial boundary threshold is calculated as (-60 + (-85)) / 2 = -72.5 dBm. Weather monitoring equipment (such as temperature and humidity sensors, anemometers, etc.) is deployed within the mining area to collect weather data in real time. Weather data, including rain, fog, wind speed, temperature, and humidity, can also be obtained through third-party meteorological services. Weather data for the mining area is acquired, with each weather factor corresponding to a specific detection value: rainfall for rain, visibility for fog, wind speed for wind, air temperature for temperature, and air humidity for humidity. Detection values ​​for each influencing factor are collected separately. A pre-defined normal range (preset detection value) is set for each weather factor. The detection values ​​for each influencing factor are then compared to the preset detection value to calculate the signal attenuation value. The signal attenuation value can be set using the following formulas: Rain = 1 * (Rainfall - 5), Fog = 0.5 * (500 - Visibility) / 100, Wind speed = 0.8 * (Wind Speed ​​- 3), Temperature = 0.3 * |Temperature - 22.5|, Humidity = 0.2 * (Humidity - 60) / 10. The signal attenuation values ​​for all weather factors are then summed to obtain the total signal attenuation value. For example, considering factors such as rain (10 mm / h rainfall), attenuation = 1 × (10−5) = 5; fog (200m visibility), attenuation = 0.5 × (500−200) / 100 = 1.5; wind speed (5m / s), attenuation = 0.8 × (5−3) = 1.6; temperature (30°C), attenuation = 0.3 × |30−22.5| = 2.25; humidity (80% humidity), attenuation = 0.2 × (80−60) / 10 = 0.4. The total signal attenuation is 5 + 1.5 + 1.6 + 2.25 + 0.4 = 10.75 dBm. The weather correction value is determined based on this total signal attenuation value, and is used to correct the initial boundary threshold. The initial boundary threshold is then adjusted based on the weather correction value. If the weather is severe and the signal attenuation is large, the boundary threshold is adjusted to adapt to the current signal environment. Boundary threshold = Initial boundary threshold + Weather correction value. For example, if the initial boundary threshold is -72.5dBm and the weather correction value is 10.75dBm, the calculated corrected boundary threshold is -61.75dBm.It dynamically adapts to weather changes, improves the accuracy of network area division, provides real-time signal distribution information support, and optimizes mine transportation scheduling.

[0038] S103: The mining area is divided based on the boundary threshold to obtain strong network area and weak network area. The signal strength of the strong network area is higher than that of the weak network area.

[0039] In step S103 above, after obtaining the boundary threshold, the mining area is divided according to the boundary threshold. Areas with signal strength higher than the boundary threshold are classified as strong network areas, and areas with signal strength lower than the boundary threshold are classified as weak network areas. Multiple acquisition points can be deployed within the mining area to record the target signal strength of each acquisition point. The signal data for each acquisition point is in the form of: acquisition point number, geographical location, and signal strength. The signal strength of each acquisition point is compared with the boundary threshold. If the signal strength is greater than or equal to the boundary threshold, the acquisition point is classified as a strong network area; if the signal strength is less than the boundary threshold, the acquisition point is classified as a weak network area. To facilitate subsequent viewing of strong and weak network areas, different colors can be assigned for marking, such as marking strong network areas as green and weak network areas as red, so that the distribution of strong and weak network areas in the mining area can be determined based on the colors.

[0040] For example, the signal strength at sampling point A is -60dBm, the signal strength at sampling point B is -70dBm, the signal strength at sampling point C is -80dBm, the signal strength at sampling point D is -85dBm, and the signal strength at sampling point E is -65dBm. If the boundary threshold is -75dBm, the signal strength of each sampling point is compared with the boundary threshold in turn. The signal strengths of sampling points A and B are greater than the boundary threshold, so they belong to the strong network area. The signal strengths of sampling points C, D, and E are less than the boundary threshold, so they belong to the weak network area.

[0041] S104: Obtain the percentage of manned mining trucks in the weak network area, and determine the total number of manned mining trucks to be scheduled in the weak network based on the percentage of manned mining trucks.

[0042] In S104 above, after dividing the mining area into strong network areas and weak network areas, the "mining truck" mentioned in this embodiment refers to a mining truck. The location information and transportation status of all mining trucks in the weak network area can be collected in real time through the on-board terminal. Through statistical analysis, the ratio of the number of manned mining trucks in the weak network area to the total number of mining trucks in the weak network area is calculated, thus obtaining the manned mining truck percentage. The manned mining truck percentage refers to the ratio of manned mining trucks to the total number of mining trucks in the weak network area. This real-time statistical mechanism ensures that the server can accurately grasp the vehicle composition in the weak network area.

[0043] Furthermore, to ensure transportation safety and efficiency in areas with weak network coverage, the total number of manned mining trucks to be scheduled in weak network areas needs to be determined based on the proportion of manned mining trucks. Specifically, this includes: when the proportion of manned mining trucks is less than a preset threshold for manned mining trucks in weak network areas, acquiring real-time terrain images of the weak network areas, extracting multiple weak network roads from the terrain images, calculating the length of each weak network road in the weak network area; calculating the quotient of the weak network road length of each weak network road and the length of a single mining truck to obtain the number of mining trucks that each weak network road can accommodate, and using the sum of the number of mining trucks that each weak network road can accommodate as the total number of mining trucks that can be accommodated in the weak network area; using the difference between the total number of mining trucks that can be accommodated in the weak network area and the number of manned and unmanned mining trucks in the weak network area as the total number of manned mining trucks to be scheduled in weak network areas; when the proportion of manned mining trucks is greater than or equal to the preset threshold for manned mining trucks in weak network areas, confirming that the total number of manned mining trucks to be scheduled in weak network areas is zero.

[0044] Specifically, a preset threshold for manned mining trucks can be set for weak network areas. This threshold serves as a baseline for manned mining truck configuration in weak network areas, representing the proportion of manned mining trucks within the total number of mining trucks that can be accommodated in the weak network area. The preset threshold can be set based on the area and signal strength of the weak network area. When the proportion of manned mining trucks is less than the preset threshold, it indicates an insufficient number of manned mining trucks in the weak network area, requiring supplementary scheduling. At this point, a real-time terrain analysis program can be initiated to acquire terrain images of the weak network area. Using image processing technology, the contour information of multiple weak network roads is extracted from the terrain image, and the length of each weak network road within the weak network area is calculated. After obtaining the road lengths, the length of each weak network road is divided by the length of a single mining truck to obtain the number of mining trucks that each weak network road can accommodate. If the quotient is not an integer, it is rounded down. For example, if the quotient is 6.5, it is rounded to 6, meaning the road can accommodate 6 mining trucks. The length of a single mining truck is calculated by multiplying its base length and width. For example, if a mining truck is 8m long, 4m wide, and has a base area of ​​32m², then the length of a single mining truck is 32m. This calculation method, based on actual road capacity, ensures the scientific validity and feasibility of the dispatch quantity. Subsequently, the number of mining trucks that can be accommodated on all roads with weak network coverage is summed to obtain the total number of mining trucks that can be accommodated in the weak network area. To accurately determine the number to be dispatched, the number of existing manned and unmanned mining trucks in the weak network area is counted in real time. The difference between the total number of mining trucks that can be accommodated in the weak network area and the number of existing vehicles is calculated, and this difference is used as the total number of manned mining trucks to be dispatched in the weak network area. This dynamic calculation mechanism ensures that the dispatch quantity neither causes road congestion nor fails to meet transportation needs.

[0045] For example, in a weak network area, there are 10 manned mining trucks and 3 unmanned mining trucks. At this time, the proportion of manned mining trucks is 0.78. If the preset threshold for manned mining trucks in a weak network area is set to 0.85, the proportion of manned mining trucks is less than the preset threshold. If the total number of weak network mining trucks that can be normally accommodated in the weak network area is 20, then the total number of weak network mining trucks is reduced by the number of manned and unmanned mining trucks, resulting in 7 trucks. At this time, the difference in the number of weak network trucks is 7, so 7 manned mining trucks need to be scheduled to enter the weak network area.

[0046] Furthermore, when the proportion of manned mining trucks is greater than or equal to the preset threshold for manned mining trucks in weak network areas, it indicates that the configuration of manned mining trucks in the weak network area has met the demand. At this time, it is necessary to confirm that the total number of manned mining trucks to be scheduled in weak network areas is zero to avoid over-scheduling and resource waste. For example, if there are 17 manned mining trucks and 3 unmanned mining trucks in a weak network area, the proportion of manned mining trucks is 0.85. If the preset threshold for manned mining trucks is set to 0.85, the proportion of manned mining trucks is equal to the preset threshold, and the total number of manned mining trucks to be scheduled in weak network areas is 0, meaning there is no need to schedule any more manned mining trucks in weak network areas. By monitoring and calculating the proportion of manned mining trucks in real time, the problem of insufficient vehicle configuration in weak network areas can be detected in a timely manner. Furthermore, the road capacity calculation method based on terrain image analysis ensures the scientific validity and feasibility of the scheduling quantity, allowing for flexible adjustment of the scheduling strategy according to the actual situation, avoiding over-scheduling or under-scheduling.

[0047] S105: Dispatch the total number of manned mining trucks to the weak network area, restrict unmanned mining trucks from entering the weak network area, and control the coordinated operation of manned and unmanned mining trucks in the weak network area.

[0048] In step S105 above, after determining the total number of manned mining trucks to be dispatched to the weak network area, when these manned mining trucks enter the weak network area, unmanned mining trucks are restricted from entering. This is achieved by setting up an electronic fence to restrict unmanned mining trucks from entering the weak network area. Specifically, a virtual barrier is established at the boundary of the weak network area. When an unmanned mining truck approaches this boundary, a warning signal is automatically issued, and an emergency braking procedure is initiated to ensure that the unmanned mining truck does not mistakenly enter the weak network area. This proactive protection mechanism effectively avoids security risks caused by unstable network signals.

[0049] Furthermore, when a transportation task exists in a weak network area, how to control the collaborative operation of manned and unmanned mining trucks in the weak network area specifically includes: obtaining the weak network transportation location corresponding to the weak network transportation task in the weak network area, obtaining road condition information around the weak network transportation location, evaluating the road condition information, and obtaining a safety score; obtaining the weight and size of the transported materials from the weak network transportation task, obtaining road parameters from the road condition information, and weighting and summing the road parameters, transported material weight, and size to obtain an operation complexity score; obtaining the target signal strength corresponding to the weak network transportation location, and obtaining the total number of weak network dispatched mining trucks required to execute the weak network transportation task; filtering out the number of idle manned mining trucks and unmanned mining trucks in the weak network area; if the safety score, operation complexity score, and target signal strength all meet the weak network transportation conditions for unmanned mining trucks, and the number of idle unmanned mining trucks in the weak network is less than the total number of weak network dispatched mining trucks, then the total number of weak network dispatched mining trucks is increased. Subtracting the number of idle unmanned mining trucks in the weak network area from the quantity yields the required number of basic mining trucks for the weak network. Manned mining trucks are then retrieved from the weak network area to meet the required number of basic mining trucks. These unmanned mining trucks (those with the number of idle unmanned mining trucks) are combined with the required number of manned mining trucks to form a basic transportation fleet for the weak network, enabling the basic transportation fleet to travel to the weak network transportation location to perform weak network transportation tasks. If the safety score, operational complexity score, and target signal strength do not meet the weak network transportation conditions for unmanned mining trucks, and the number of idle manned mining trucks in the weak network is less than the total number of mining trucks scheduled for the weak network, then the total number of scheduled mining trucks in the weak network is subtracted from the number of idle manned mining trucks to obtain the required number of standard mining trucks for the weak network. These manned mining trucks are then retrieved from the weak network area to meet the required number of standard mining trucks for the weak network, and these unmanned mining trucks (those with the number of idle manned mining trucks) are combined with the required number of unmanned mining trucks to form a standard transportation fleet for the weak network, enabling the standard transportation fleet to travel to the weak network transportation location to perform weak network transportation tasks.

[0050] Specifically, the system acquires information on transportation tasks within weak network areas. The task management system locates the specific coordinates of the transportation location within the weak network and simultaneously activates the surrounding environmental perception system, including road surveillance cameras and road surface sensors, to collect real-time road condition information around the transportation location. This road condition information includes multi-dimensional parameters such as road surface condition, slope change, line-of-sight conditions, and weather impact. The collected road condition information is analyzed. First, all road condition parameters are standardized to a range of 0-1. Then, a pre-defined safety assessment algorithm is used to weight the standardized parameters. Road surface condition has a weight of 0.4, slope change has a weight of 0.3, line-of-sight conditions have a weight of 0.2, and weather impact has a weight of 0.1. This weighted calculation yields a safety score, ranging from 0-100, with higher scores indicating better safety. When acquiring transportation task details, specific parameters of the transported materials are extracted from the task management database, including key information such as material weight (in tons) and material dimensions (length, width, and height). Simultaneously, road parameters, including road width, turning radius, and maximum load capacity, are extracted from road condition information. These parameters are then weighted and summed to calculate the operational complexity score. The specific weight allocation is as follows: material weight 0.35, material size 0.25, road width 0.2, turning radius 0.1, and load capacity 0.1. The final operational complexity score is also calculated using a 0-100 scale. Signal strength detectors deployed at weak network transportation locations are used to monitor and record the network signal strength in real time. Signal strength data is sampled every second, and the average value over the most recent 10 minutes is used as the target signal strength. Furthermore, based on the total amount of transported materials and the load capacity of each vehicle, the total number of mining trucks required to complete the weak network transportation task is calculated. The working status of all mining trucks in the weak network area is monitored in real time through a vehicle management platform. Each mining truck is equipped with a status monitoring sensor that can report real-time data such as the vehicle's working status, location information, and load condition. Based on this data, vehicles in an idle state were selected. Idle status refers to mining trucks not scheduled for operation. The number of idle manned mining trucks in weak network conditions and the number of idle unmanned mining trucks in weak network conditions were then calculated separately. The safety score, operation complexity score, and target signal strength obtained earlier were compared with the weak network transportation conditions. The weak network transportation conditions were set for unmanned mining trucks in weak network areas. These conditions included: safety score ≥ 85 points, operation complexity score ≤ 60 points, and target signal strength ≥ -85dBm.When all three indicators meet the conditions and the number of idle unmanned mining trucks in the weak network is less than the total number of mining trucks scheduled in the weak network, the first plan is activated: Calculate the required number of basic mining trucks for the weak network (total number of mining trucks scheduled in the weak network minus the number of idle unmanned mining trucks in the weak network), and retrieve the required number of manned mining trucks from the vehicle management system of the weak network area. When assembling the basic transportation mining truck fleet for the weak network, first evaluate the performance indicators of each candidate vehicle, including remaining battery power, equipment status, and historical operation records, and select the most suitable vehicles to form the fleet. Develop a detailed operation plan for the basic transportation mining truck fleet for the weak network, including driving routes, operation sequences, and communication protocols, to ensure that the number of idle unmanned mining trucks in the weak network and the number of manned mining trucks required for the basic transportation mining truck fleet can coordinate efficiently.

[0051] Furthermore, a fleet of unmanned mining trucks with available slots and the required number of manned mining trucks for basic network transportation is formed. This includes: obtaining the location of each unmanned mining truck within the available slots; calculating the transportation distance between the unmanned mining truck location and the transportation point, selecting the maximum value from multiple distances as the theoretical maximum transportation distance, and determining the maximum transportation distance that the unmanned mining trucks can normally travel; if the maximum transportation distance is greater than or equal to the theoretical maximum transportation distance, it is confirmed that the unmanned mining truck can normally complete the network transportation task, and the unmanned mining trucks are then... The number of unmanned mining trucks available is combined with the number of manned mining trucks required for the weak network basic mining truck fleet to obtain the weak network basic transportation mining truck fleet. If the maximum transportation distance is less than the theoretical maximum transportation distance, the minimum value among the confirmed multiple transportation distances is selected as the theoretical minimum transportation distance. If the maximum transportation distance is greater than or equal to the theoretical minimum transportation distance, the target unmanned mining truck corresponding to the theoretical minimum transportation distance is retrieved. The total number of mining trucks scheduled for the weak network is subtracted from the number of target unmanned mining trucks to obtain the number of mining trucks required for the weak network correction. The target unmanned mining trucks are combined with the number of manned mining trucks required for the weak network correction to obtain the weak network basic transportation mining truck fleet.

[0052] Specifically, the real-time location information of each unmanned mining truck in a weak network is obtained through the vehicle-mounted GPS module and the mine positioning system, based on the number of available trucks. Each unmanned mining truck is equipped with a high-precision positioning device, achieving centimeter-level positioning accuracy. The acquired location information is converted into a standard coordinate format and recorded as the location of the unmanned mining truck in a weak network. This high-precision positioning mechanism provides an accurate data foundation for subsequent distance calculations. Then, a path planning algorithm is used to calculate the transportation distance between the location of each unmanned mining truck in a weak network and the transportation point in a weak network. During the calculation, not only the straight-line distance is considered, but also the actual road network conditions in the mine area are taken into account. The optimal drivable path length is calculated using Dijkstra's shortest path algorithm. At the same time, the influence of factors such as road slope, curves, and road conditions on the transportation distance is considered, and the base distance is corrected by a weighted coefficient to obtain a transportation distance value that is more in line with the actual situation. After obtaining the transportation distances of all unmanned mining trucks, a comparison algorithm is used to select the maximum value from multiple transportation distances, which is determined as the theoretical maximum transportation distance. This distance represents the longest travel distance required to complete the transportation task in a weak network. Simultaneously, based on the technical parameters of the unmanned mining trucks, including battery range and power system performance, and combined with historical operating data, the maximum transport distance that the unmanned mining trucks can normally travel under current operating conditions is calculated. The maximum transport distance is compared with the theoretical maximum transport distance. When the maximum transport distance is greater than or equal to the theoretical maximum transport distance, it indicates that the unmanned mining trucks in the weak network area that are idle are capable of completing the weak network transport task. At this point, the fleet combination program is initiated to intelligently match the number of unmanned mining trucks idle in the weak network with the number of manned mining trucks required for the weak network basic transport fleet. The matching process considers multiple factors such as vehicle performance, load capacity, and remaining battery power to ensure that the formed weak network basic transport truck fleet has optimal operating efficiency. When the maximum transport distance is less than the theoretical maximum transport distance, the minimum value is selected from several previously calculated transport distances as the theoretical minimum transport distance. This selection process is implemented using a quicksort algorithm to ensure that the most suitable transport solution can be found quickly. The maximum transport distance is compared again with the theoretical minimum transport distance. When the maximum transport distance is greater than or equal to the theoretical minimum transport distance, it indicates that only a portion of the unmanned mining trucks in the weak network area can participate in the weak network transport task. In this case, target unmanned mining trucks corresponding to the theoretical minimum transport distance are identified. These vehicles are more suitable for performing the current weak network transport task because of their shorter distance. The required number of mining trucks for weak network correction is then calculated by subtracting the number of target unmanned mining trucks from the total number of mining trucks scheduled for weak network operation. This dynamic adjustment mechanism ensures the rationality of capacity allocation. Finally, the selected target unmanned mining trucks are combined with the required number of manned mining trucks for weak network correction to form the final basic transportation fleet for mining trucks in the weak network area.During the assembly process, detailed collaborative operation plans are developed, including vehicle platooning sequence, communication protocols, and contingency plans, to ensure the safety and efficiency of fleet operations. By tracking the fleet's operational status in real time and collecting various operational data, fleet assembly strategies are continuously optimized to improve overall transportation efficiency.

[0053] Furthermore, when the above three indicators fail to meet the weak network transportation conditions, and the number of idle manned mining trucks in the weak network is less than the total number of mining trucks scheduled in the weak network, the second plan is activated: calculate the required number of standard mining trucks in the weak network (total number of mining trucks scheduled in the weak network minus the number of idle manned mining trucks in the weak network), and retrieve the corresponding number of vehicles from the weak network area. The weak network retrieval area refers to the location of the mining truck dispatch center closest to the weak network area within the mining region. A similar vehicle selection and plan development process is adopted when forming a standard transportation fleet in the weak network to ensure that the number of unmanned mining trucks with available manned mining trucks in the weak network and the number of unmanned mining trucks required for the standard transportation fleet can safely and efficiently complete the transportation task.

[0054] In addition, the signal strength of each location within the weak network area can be acquired. Based on the signal strength required by the unmanned mining truck, a weak network area signal threshold can be set. The signal strength of each location can be compared with the weak network area signal threshold in turn. When the signal strength of a location is greater than or equal to the weak network area signal threshold, the location is marked as having a good weak network signal; when the signal strength of a location is less than the weak network area signal threshold, the location is marked as having a poor weak network signal. When a subsequent operation and transportation needs to be carried out at a certain location, if the location has a good weak network signal, the unmanned mining truck will be dispatched first; if the location has a poor weak network signal, the manned mining truck will be dispatched first.

[0055] S106: Obtain the percentage of unmanned mining cards in the strong network area, and determine the total number of unmanned mining cards to be scheduled in the strong network area based on the percentage of unmanned mining cards.

[0056] In S106 above, a vehicle management platform deployed in the strong network area collects real-time operational data from all mining trucks within the area. Each mining truck is equipped with an intelligent identification tag and status monitoring device, capable of reporting vehicle type, location information, and working status in real time. The data statistics module calculates the ratio of the number of unmanned mining trucks within the strong network area to the total number of mining trucks in the area, thus obtaining the percentage of unmanned mining trucks.

[0057] Furthermore, the total number of unmanned mining trucks to be scheduled in the strong network is determined based on the proportion of unmanned mining trucks. Specifically, this includes: when the proportion of unmanned mining trucks is less than the preset threshold for unmanned mining trucks in the strong network area, real-time acquisition of terrain information within the strong network area is performed, multiple strong network roads are parsed from the terrain information, and the length of each strong network road in the strong network area is calculated; the quotient of the strong network road length of each strong network road and the length of a single mining truck is calculated to obtain the number of mining trucks that each strong network road can accommodate, and the sum of the number of mining trucks that each strong network road can accommodate is taken as the total number of strong network mining trucks that can be accommodated in the strong network area; the difference between the total number of strong network mining trucks and the number of manned and unmanned mining trucks in the strong network area is taken as the total number of unmanned mining trucks to be scheduled in the strong network area; when the proportion of unmanned mining trucks is greater than or equal to the preset threshold for unmanned mining trucks in the strong network area, the total number of unmanned mining trucks to be scheduled in the strong network area is confirmed to be zero.

[0058] Specifically, after calculating the proportion of unmanned mining trucks within the strong network area, a preset threshold for unmanned mining trucks is set as a reference standard for unmanned mining truck configuration to ensure optimal transportation efficiency in the strong network area. When the proportion of unmanned mining trucks is less than the preset threshold for unmanned mining trucks in the strong network area, it indicates that the number of unmanned mining trucks in the strong network area is insufficient, and supplementary scheduling is required. The preset threshold for unmanned mining trucks in the strong network area can be set based on the area of ​​the strong network area and road rating. Real-time terrain information of the strong network area can be obtained, including 3D point cloud data obtained by the LiDAR scanning system, high-precision satellite map data, and real-time terrain change data collected by ground monitoring stations. Intelligent image processing algorithms are used to analyze the acquired terrain information, road outlines are identified through edge detection algorithms, and then multiple strong network roads are separated from the terrain information using road feature extraction algorithms. To improve the accuracy of identification, historical road data is also used for cross-validation to ensure the completeness and accuracy of road identification. For each identified strong network road, the length of the strong network road it occupies in the strong network area is calculated through a precise measurement algorithm. The calculation process considers factors such as the actual shape of the road, slope variations, and road surface undulations. The network road is divided into several small units, the actual area of ​​each unit is calculated, and then the total area of ​​the entire road is obtained through integration. Three-dimensional modeling technology is used to ensure that the area calculation results fully reflect the actual road conditions. Next, the length of a single mining truck, i.e., the footprint of the vehicle itself, is obtained. The number of mining trucks that each network road can accommodate is calculated by dividing the length of the network road by the length of a single mining truck. If the quotient is not an integer, it is rounded down. This calculation method considers the safe distance requirements for vehicle operation, ensuring the rationality of the capacity calculation. The number of mining trucks that can be accommodated on all network roads is summed to obtain the total number of mining trucks that the network area can accommodate. Next, the number of manned and unmanned mining trucks currently in the network area is counted in real time. Real-time status information for each vehicle, including location, working status, and task allocation, is obtained through the vehicle management system. The difference between the total number of mining trucks that can be accommodated in the strong network and the number of existing vehicles in the strong network area is calculated to determine the total number of unmanned mining trucks to be dispatched in the strong network. For example, if there are 3 manned mining trucks and 15 unmanned mining trucks in the strong network area, the proportion of unmanned mining trucks is 0.83. If the total number of mining trucks that can be accommodated in the strong network area is 23, and the preset threshold for unmanned mining trucks in the strong network area is set to 0.9, then the proportion of unmanned mining trucks is less than the preset threshold. Subtracting the number of manned and unmanned mining trucks from the total number of mining trucks that can be accommodated in the strong network yields a total of 5 unmanned mining trucks that can be dispatched in the strong network area. At this point, 5 more unmanned mining trucks can be dispatched to the strong network area.

[0059] When the proportion of unmanned mining trucks detected is greater than or equal to the preset threshold for unmanned mining trucks in the strong network area, it indicates that the number of unmanned mining trucks in the strong network area has reached the optimal level. At this time, the total number of unmanned mining trucks to be scheduled in the strong network area is automatically set to zero to avoid resource waste caused by over-scheduling. This dynamic adjustment mechanism ensures the rationality and efficiency of vehicle configuration in the strong network area. By continuously monitoring the scheduling effect, including data such as road utilization, transportation efficiency, and safety indicators, the road capacity calculation model and scheduling strategy can be continuously optimized. This continuous optimization mechanism ensures that the scheduling system can always maintain an optimal operating state.

[0060] S107: Dispatch the total number of unmanned mining trucks to the strong network area; control the coordinated operation of unmanned and manned mining trucks in the strong network area.

[0061] In the above S107, it is determined that there are still a total number of unmanned mining trucks that can be dispatched to the strong network area. Suitable vehicles are selected from the dispatchable unmanned mining truck center, and the optimal dispatch route is planned for each unmanned mining truck to be dispatched based on real-time road conditions.

[0062] Furthermore, when a transportation task exists within a strong network area, how to control the collaborative operation of unmanned and manned mining trucks within that area specifically includes: obtaining the strong network transportation location corresponding to the strong network transportation task within the strong network area, obtaining road information around the strong network transportation location, evaluating the road information, and obtaining a road score; obtaining multiple weather parameters from the current weather information of the strong network area, weighting and summing the multiple weather parameters to obtain an impact score; obtaining the material type from the strong network transportation task, and determining the total number of strong network dispatched mining trucks required to execute the strong network transportation task; filtering out the number of idle manned mining trucks and unmanned mining trucks in the strong network area; if the road score, impact score, and material type all meet the strong network transportation conditions for unmanned mining trucks, and the number of idle unmanned mining trucks is less than the total number of strong network dispatched mining trucks, then the strong network dispatched mining trucks will be... The total number of unmanned mining trucks in the strong network is subtracted from the number of idle unmanned mining trucks in the strong network to obtain the required number of basic mining trucks in the strong network. The required number of unmanned mining trucks in the strong network retrieval area are retrieved, and the unmanned mining trucks in the number of idle unmanned mining trucks in the strong network are combined with the required number of unmanned mining trucks in the strong network to form a strong network basic transportation mining truck fleet, so that the strong network basic transportation mining truck fleet can go to the strong network transportation location to perform strong network transportation tasks. If the road score, impact score, and material type all meet the strong network transportation conditions of unmanned mining trucks, and the number of idle manned mining trucks in the strong network is less than the total number of mining trucks in the strong network scheduling, then the required number of unmanned mining trucks in the strong network standard mining truck fleet is retrieved from the number of idle unmanned mining trucks in the strong network, and the manned mining trucks in the number of idle manned mining trucks in the strong network are combined with the required number of unmanned mining trucks in the strong network standard mining truck fleet, so that the strong network standard transportation mining truck fleet can go to the strong network transportation location to perform strong network transportation tasks.

[0063] Specifically, the system acquires the network transportation tasks within the network-protected area, determining the precise coordinates of the transportation locations using GPS positioning and electronic maps. The environmental perception module then activates, utilizing a network of intelligent sensors deployed around the transportation locations to collect real-time road information, including key parameters such as road surface conditions, slope, width, and curvature. The collected road information is analyzed, first standardizing the road parameters to a uniform range of 0-1. Then, a pre-defined evaluation algorithm is used to weight the standardized parameters: road surface condition 0.35, slope 0.25, width 0.25, and curvature 0.15. This weighted calculation yields a road score, ranging from 0-100, with higher scores indicating better road conditions. Simultaneously, real-time weather information for the network-protected area is obtained from meteorological monitoring stations. These stations are equipped with various sensors that collect multiple weather parameters, including visibility, wind speed, precipitation, and temperature. After standardizing these parameters, a weighted summation algorithm was used: visibility weight 0.4, wind speed weight 0.3, precipitation weight 0.2, and temperature weight 0.1, resulting in an impact score. Material type information for the high-grid transportation task was extracted from the task management database, including detailed parameters such as physical properties, hazard level, and transportation requirements. Simultaneously, based on material characteristics and single-vehicle load capacity, the total number of high-grid dispatched mining trucks required to complete the high-grid transportation task was calculated. The calculation process considered transportation timeliness requirements and safety factors to ensure the rationality of the dispatched number. The real-time monitoring function of the vehicle management platform was used to filter out vehicles in an idle state within the high-grid area. Each mining truck is equipped with a status monitoring device that can report working status, location information, and other data in real time. Through data analysis, the number of idle manned mining trucks and unmanned mining trucks in the high-grid area were statistically determined. The road score, impact score, and material type were compared with the high-grid transportation conditions, which were set for unmanned mining trucks in the high-grid area. The conditions for strong network transportation include: road score ≥ 80 points, impact score ≥ 75 points, and material type safety level ≤ 2. When these indicators meet the strong network transportation conditions and the number of idle strong network unmanned mining trucks is less than the total number of strong network dispatched mining trucks, the required number of strong network basic mining trucks is calculated (total number of strong network dispatched mining trucks minus the number of idle strong network unmanned mining trucks). Then, the corresponding number of unmanned mining trucks are retrieved from the strong network retrieval area. During the retrieval process, factors such as vehicle performance and remaining battery power are considered to select the most suitable vehicles. The strong network retrieval area refers to the location of the mining truck dispatch center closest to the strong network area. The number of idle strong network unmanned mining trucks and the required number of strong network basic mining trucks form the strong network basic transportation mining truck fleet, and a detailed operation plan is developed for the fleet, including driving routes and operation sequences.When the above conditions are met, but the number of idle manned mining trucks in the network is less than the total number of mining trucks scheduled in the network, the required number of unmanned mining trucks for the standard network mining trucks is calculated and retrieved from the number of idle unmanned mining trucks in the network. The retrieval process uses an intelligent matching algorithm to ensure that the selected vehicles are best suited to the current task requirements. The number of idle manned mining trucks in the network and the required number of unmanned mining trucks for the standard network mining trucks are combined to form a standard network transportation mining truck fleet. After the fleet is formed, a dedicated collaborative control scheme is configured for each fleet. Real-time communication and status synchronization within the fleet are achieved through a 5G network, ensuring that all vehicles can coordinate and complete transportation tasks consistently. By continuously recording and analyzing operational data, including task completion status, energy consumption, and safety indicators, the evaluation model and scheduling strategy are continuously optimized. This continuous improvement mechanism ensures that the system can adapt to various complex operating conditions.

[0064] Furthermore, the number of available manned mining trucks in the strong network and the number of unmanned mining trucks required for the strong network standard mining truck fleet are combined to form a strong network standard transportation mining truck fleet. Specifically, this includes: obtaining the location of each unmanned mining truck in the strong network under the available number of unmanned mining trucks, and planning the target route between the location of the unmanned mining truck in the strong network and the strong network transportation location; dividing the transportation length of each target route by the driving speed of the unmanned mining truck to obtain the transportation time; selecting the number of targets less than or equal to the preset transportation time from multiple transportation times, where the preset transportation time is the transportation time between the candidate manned mining trucks in the strong network area and the strong network transportation location; if the number of targets is less than the total number of strong network scheduled mining trucks, and the sum of the number of targets and the number of available manned mining trucks in the strong network is equal to or greater than the total number of strong network scheduled mining trucks, then the number of targets is confirmed as the number of unmanned mining trucks required for the strong network standard mining truck fleet, and the number of available manned mining trucks in the strong network and the number of unmanned mining trucks required for the strong network standard mining truck fleet are combined to form a strong network standard transportation mining truck fleet.

[0065] Specifically, the real-time location information of each unmanned mining truck is obtained through an onboard positioning system, based on the available number of trucks. Each unmanned mining truck is equipped with a high-precision GPS module and an inertial navigation system, which converts the acquired location information into a standard coordinate format and records it as the truck's location. Then, a target route is planned for each unmanned mining truck from its location to the designated transportation point. The planning process employs an improved dynamic programming algorithm, comprehensively considering multiple factors: real-time road condition monitoring data, including road surface conditions, gradient changes, and traffic flow; weather conditions and visibility data provided by a meteorological system; and historical data analysis to consider road traffic efficiency at different times. These factors are input into a path optimization model to generate the optimal target route for each vehicle. After route planning, the actual transportation length of each target route is calculated. The calculation process considers not only the geometric length of the route but also the impact of road curvature and gradient on the actual travel distance. Using vector analysis, the three-dimensional path is projected onto a two-dimensional plane, taking into account the distance increment caused by elevation changes, to obtain more accurate transportation length data. The standard driving speed parameters of the unmanned mining trucks are obtained from the vehicle management database. These speed parameters are determined comprehensively based on vehicle performance characteristics, safety standards, and road speed limits. The theoretical transportation time is obtained by dividing the transportation length of each target route by the corresponding driving speed. To improve calculation accuracy, the theoretical time is also corrected based on real-time road conditions and weather conditions to obtain a more realistic transportation time. Simultaneously, the transportation time from candidate manned mining trucks in the strong network area to the strong network transportation location is calculated and set as the preset transportation time. This time calculation also considers actual road conditions and operating conditions, serving as a reference standard for selecting unmanned mining trucks. Through comparative analysis, the number of vehicles with transportation times less than or equal to the preset transportation time is selected from multiple transportation times and recorded as the target number. The selection process uses a quicksort algorithm to ensure efficient completion of time comparison and statistics. The target number is compared with the total number of mining trucks scheduled in the strong network, and it is also verified whether the sum of the target number and the number of idle manned mining trucks in the strong network satisfies the condition of being greater than or equal to the total number of mining trucks scheduled in the strong network. When the number of targets is less than the total number of mining trucks in the network scheduling, and the sum of the number of targets and the number of idle manned mining trucks in the network scheduling is equal to or greater than the total number of mining trucks in the network scheduling, the number of targets is confirmed as the required number of standard mining trucks in the network scheduling for unmanned mining trucks. This dynamic determination mechanism ensures the feasibility and efficiency of fleet formation. After determining the number of vehicles, the required number of standard mining trucks in the network scheduling are selected from the unmanned mining trucks that meet the transportation time requirements. The selection criteria include multiple indicators such as remaining battery power, equipment status, and historical performance. At the same time, vehicles with the required number of idle manned mining trucks in the network scheduling are selected from the manned mining trucks, taking into account factors such as driver experience and vehicle performance.Furthermore, a detailed collaborative operation plan was developed for the established standardized mining truck fleet, a unified communication protocol was established to ensure real-time information sharing within the fleet, the relative positions and speeds of vehicles were optimized, and emergency plans were formulated. By tracking the operational status of the standardized mining truck fleet in real time and collecting various performance indicators, the fleet formation strategy and collaborative operation plan were continuously optimized to improve transportation efficiency and safety.

[0066] Furthermore, if the number of targets is greater than or equal to the total number of mining trucks scheduled for the strong network, then the number of unmanned mining trucks corresponding to the number of targets will be prioritized to form a strong network standard transport mining truck fleet, so that the strong network standard transport mining truck fleet can go to the strong network transport location to perform strong network transport tasks. To ensure the smooth completion of strong network transport tasks, manned mining trucks that are idle in the strong network area can also be used as backup vehicles. Once it is detected that an unmanned mining truck in the strong network standard transport mining truck fleet cannot perform a transport task normally, a backup manned mining truck can be dispatched to support the malfunctioning unmanned mining truck.

[0067] In one possible implementation, network data within the mining area is collected in real time. The collected network data is then compared with historical network data to determine the network data change rate. This change rate is then compared with a change rate threshold. If the change rate exceeds the threshold, a new boundary threshold is determined based on the current network data, and the mining area is then reclassified into strong and weak network areas based on this new threshold. This can also be understood as follows: due to the instability of network data in the mining area, the network data change rate needs to be recorded in real time. When the change rate exceeds the threshold, the mining area needs to be reclassified into strong and weak network areas. Subsequently, based on the aforementioned collaborative scheduling method for strong and weak network areas, the collaborative operation of unmanned and manned mining trucks in strong or weak network areas is controlled. Furthermore, when the network data change rate is less than or equal to the change rate threshold, it is confirmed that the network data within the mining area is stable, and in this case, there is no need to reclassify the mining area into strong and weak network areas. The change rate threshold can be set based on actual conditions and historical data; no further limitations are imposed here.

[0068] This application also provides a scheduling device for hybrid driving mining trucks. Figure 2 This is a schematic diagram of a scheduling device for hybrid driving mining trucks provided in an embodiment of this application. (Refer to...) Figure 2 The device includes an acquisition unit 201, a processing unit 202, and a control unit 203. Unit 201 acquires network data in the mining area, performs cluster analysis on the network data, and obtains the first cluster center and the second cluster center; Processing unit 202 divides the mining area into strong network areas and weak network areas based on boundary thresholds, where the signal strength of the strong network area is higher than that of the weak network area; determines the boundary threshold based on the first cluster center and the second cluster center; obtains the proportion of manned mining trucks in the weak network area, and determines the total number of manned mining trucks to be scheduled in the weak network area based on the proportion of manned mining trucks; schedules the total number of manned mining trucks scheduled in the weak network area to the weak network area, and restricts unmanned mining trucks from entering the weak network area; obtains the proportion of unmanned mining trucks in the strong network area, and determines the total number of unmanned mining trucks to be scheduled in the strong network area based on the proportion of unmanned mining trucks; schedules the total number of unmanned mining trucks scheduled in the strong network area to the strong network area. Control unit 203 controls manned and unmanned mining trucks to work together in areas with weak network coverage; controls unmanned and manned mining trucks to work together in areas with strong network coverage.

[0069] In one possible implementation, the acquisition unit 201 is used to acquire a terrain image of a weak network area in real time when the proportion of manned mining trucks is less than a preset threshold for manned mining trucks in a weak network area, extract multiple weak network roads from the terrain image, and calculate the length of each weak network road in the weak network area; the processing unit 202 is used to calculate the quotient of the weak network road length of each weak network road and the length of a single mining truck to obtain the number of mining trucks that each weak network road can accommodate, and use the sum of the number of mining trucks that each weak network road can accommodate as the total number of weak network mining trucks that can be accommodated in the weak network area; the difference between the total number of weak network mining trucks that can be accommodated and the number of manned and unmanned mining trucks in the weak network area is used as the total number of manned mining trucks to be scheduled in the weak network area; when the proportion of manned mining trucks is greater than or equal to the preset threshold for manned mining trucks in a weak network area, it is confirmed that the total number of manned mining trucks to be scheduled in the weak network area is zero.

[0070] In one possible implementation, the acquisition unit 201 is used to acquire the weak network transportation location corresponding to the weak network transportation task in the weak network area, and acquire the road condition information around the weak network transportation location, evaluate the road condition information, and obtain a safety score; acquire the weight and size of the transported materials from the weak network transportation task, acquire road parameters from the road condition information, and perform a weighted summation of the road parameters, the weight and size of the transported materials to obtain an operation complexity score; acquire the target signal strength corresponding to the weak network transportation location, and acquire the total number of weak network dispatch mining trucks required to execute the weak network transportation task; the processing unit 202 is used to filter out the number of idle manned mining trucks and the number of idle unmanned mining trucks in the weak network area; if the safety score, operation complexity score, and target signal strength all meet the weak network transportation conditions of the unmanned mining trucks, and the number of idle unmanned mining trucks in the weak network is less than the total number of weak network dispatch mining trucks, then the total number of weak network dispatch mining trucks is subtracted from the number of idle unmanned mining trucks in the weak network to obtain the basic weak network mining trucks. The required quantity; the control unit 203 is used to retrieve the required number of manned mining trucks for the weak network basic mining trucks from the weak network area, and to form a weak network basic transportation mining truck fleet with the number of unmanned mining trucks available in the weak network and the number of manned mining trucks required for the weak network basic mining trucks, so that the weak network basic transportation mining truck fleet can go to the weak network transportation location to perform weak network transportation tasks; the processing unit 202 is used to subtract the number of unmanned manned mining trucks available in the weak network from the total number of weak network scheduled mining trucks if the safety score, operation complexity score and target signal strength do not meet the weak network transportation conditions of unmanned mining trucks, and the number of unmanned manned mining trucks available in the weak network is less than the total number of weak network scheduled mining trucks, so as to obtain the required number of weak network standard mining trucks; the control unit 203 is used to retrieve the required number of manned mining trucks for the weak network standard mining trucks from the weak network retrieval area, and to form a weak network standard transportation mining truck fleet with the number of unmanned manned mining trucks available in the weak network and the number of unmanned mining trucks required for the weak network standard mining trucks, so that the weak network standard transportation mining truck fleet can go to the weak network transportation location to perform weak network transportation tasks.

[0071] In one possible implementation, the acquisition unit 201 is used to acquire the location of each unmanned mining truck in the weak network unmanned mining truck idle quantity; the processing unit 202 is used to calculate the transportation distance between the location of the unmanned mining truck in the weak network and the weak network transportation location, select the maximum value from multiple transportation distances as the theoretical maximum transportation distance, and determine the maximum transportation distance that the unmanned mining truck can travel normally; the control unit 203 is used to confirm that the unmanned mining truck can normally complete the weak network transportation task if the maximum transportation distance is greater than or equal to the theoretical maximum transportation distance, and connect the unmanned mining trucks in the weak network unmanned mining truck idle quantity with the weak network basic mining trucks. The required number of manned mining trucks are combined to obtain a basic transportation mining truck fleet for weak network; the processing unit 202 is used to select the minimum value among multiple transportation distances as the theoretical minimum transportation distance if the maximum transportation distance is less than the theoretical maximum transportation distance; the control unit 203 is used to retrieve the target unmanned mining truck corresponding to the theoretical minimum transportation distance if the maximum transportation distance is greater than or equal to the theoretical minimum transportation distance, subtract the number of target unmanned mining trucks from the total number of mining trucks scheduled for weak network to obtain the required number of mining trucks for weak network correction, and combine the target unmanned mining trucks with the required number of manned mining trucks for weak network correction to obtain a basic transportation mining truck fleet for weak network.

[0072] In one possible implementation, the acquisition unit 201 is used to acquire terrain information in the strong network area in real time when the proportion of unmanned mining trucks is less than the preset threshold for unmanned mining trucks in the strong network area, parse multiple strong network roads from the terrain information, and calculate the length of each strong network road in the strong network area; the processing unit 202 is used to calculate the quotient of the strong network road length of each strong network road and the length of a single mining truck to obtain the number of mining trucks that each strong network road can accommodate, and use the sum of the number of mining trucks that each strong network road can accommodate as the total number of strong network mining trucks that can be accommodated in the strong network area; the difference between the total number of strong network mining trucks and the number of manned and unmanned mining trucks in the strong network area is used as the total number of strong network unmanned mining trucks to be scheduled; when the proportion of unmanned mining trucks is greater than or equal to the preset threshold for unmanned mining trucks in the strong network area, it is confirmed that the total number of strong network unmanned mining trucks to be scheduled is zero.

[0073] In one possible implementation, the acquisition unit 201 is used to acquire the strong network transportation location corresponding to the strong network transportation task in the strong network area, and acquire road information around the strong network transportation location, evaluate the road information, and obtain a road score; acquire multiple weather parameters from the current weather information of the strong network area, and perform a weighted summation of the multiple weather parameters to obtain an impact score; acquire the material type from the strong network transportation task, and determine the total number of strong network dispatch mining trucks required to execute the strong network transportation task; the processing unit 202 is used to filter out the number of idle manned mining trucks and the number of idle unmanned mining trucks in the strong network area; if the road score, impact score, and material type all meet the strong network transportation conditions for unmanned mining trucks, and the number of idle unmanned mining trucks is less than the total number of strong network dispatch mining trucks, then the total number of strong network dispatch mining trucks is subtracted from the number of idle unmanned mining trucks. The idle quantity is used to obtain the required number of basic mining cards for the strong network; the control unit 203 is used to retrieve the required number of unmanned mining cards for the basic mining cards for the strong network from the strong network retrieval area, and to form a strong network basic transportation mining truck fleet with the number of unmanned mining cards in the idle quantity of strong network unmanned mining cards and the number of unmanned mining cards required for the basic mining cards for the strong network, so that the strong network basic transportation mining truck fleet can go to the strong network transportation location to perform the strong network transportation task; if the road score, impact score and material type all meet the strong network transportation conditions of unmanned mining cards, and the number of idle strong network manned mining cards is less than the total number of strong network dispatched mining cards, then the required number of unmanned mining cards for the standard mining cards for the strong network are retrieved from the number of idle strong network unmanned mining cards, and the number of idle strong network manned mining cards and the number of unmanned mining cards required for the standard mining cards for the strong network are formed into a strong network standard transportation mining truck fleet, so that the strong network standard transportation mining truck fleet can go to the strong network transportation location to perform the strong network transportation task.

[0074] In one possible implementation, the acquisition unit 201 is used to acquire the location of each unmanned mining truck in the strong network under the number of idle unmanned mining trucks, and plan the target route between the location of the unmanned mining truck in the strong network and the strong network transportation location; the processing unit 202 is used to divide the transportation length of each target route by the driving speed of the unmanned mining truck to obtain the transportation time; select the number of targets less than or equal to the preset transportation time from multiple transportation times, wherein the preset transportation time is the transportation time between the candidate manned mining truck in the strong network area and the strong network transportation location; if the number of targets is less than the total number of mining trucks scheduled in the strong network, and the sum of the number of targets and the number of idle manned mining trucks in the strong network is equal to or greater than the total number of mining trucks scheduled in the strong network, then the number of targets is confirmed as the required number of standard mining trucks in the strong network for unmanned mining trucks, and the number of manned mining trucks in the number of idle manned mining trucks in the strong network and the number of unmanned mining trucks required for the standard mining trucks in the strong network are combined to form a strong network standard transportation mining truck fleet.

[0075] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0076] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This application provides a schematic diagram of the structure of an electronic device. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 302, and at least one communication bus 305.

[0077] The communication bus 305 is used to enable communication between these components.

[0078] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0079] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0080] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 302, and by calling data stored in memory 302. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and application requests; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0081] The memory 302 may include random access memory (RAM) or read-only memory. Optionally, the memory 302 may include a non-transitory computer-readable storage medium. The memory 302 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 302 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc. The data storage area may store data involved in the various method embodiments described above. Optionally, the memory 302 may also be at least one storage device located remotely from the aforementioned processor 301.

[0082] like Figure 3 As shown, the memory 302, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a mobile phone-based application for monitoring abnormal temperature.

[0083] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call the application stored in the memory 302 based on the temperature anomaly monitoring of the mobile phone. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0084] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0085] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0087] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0088] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0089] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0090] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truths. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

Claims

1. A scheduling method for hybrid driving mining trucks, characterized in that, The method includes: Obtain network data from the mining area, perform cluster analysis on the network data, and obtain a first cluster center and a second cluster center; Determine the boundary threshold based on the first cluster center and the second cluster center; The mining area is divided based on the boundary threshold to obtain a strong network area and a weak network area, wherein the signal strength of the strong network area is higher than that of the weak network area. Obtain the percentage of manned mining trucks in the weak network area, and determine the total number of manned mining trucks to be scheduled in the weak network area based on the percentage of manned mining trucks. The total number of manned mining trucks in the weak network area are dispatched to the weak network area, and unmanned mining trucks are restricted from entering the weak network area. Control the manned mining truck and the unmanned mining truck to work together in the weak network area; Obtain the percentage of unmanned mining trucks in the strong network area, and determine the total number of unmanned mining trucks to be scheduled based on the percentage of unmanned mining trucks in the strong network area. Dispatch the total number of unmanned mining trucks in the strong network area to the strong network area; The unmanned mining truck and the manned mining truck are controlled to work together in the strong grid area.

2. The method according to claim 1, characterized in that, The total number of manned mining trucks to be scheduled in weak network conditions is determined based on the aforementioned proportion of manned mining trucks, specifically including: When the proportion of manned mining trucks is less than the preset threshold for manned mining trucks in weak network areas, the terrain image of the weak network area is acquired in real time, multiple weak network roads are extracted from the terrain image, and the length of each weak network road in the weak network area is calculated. Calculate the quotient of the length of each weak network road and the length of a single mining card to obtain the number of mining cards that each weak network road can accommodate. The sum of the number of mining cards that each weak network road can accommodate is taken as the total number of weak network mining cards that the weak network area can accommodate. The difference between the total number of weak network mining cards and the number of manned and unmanned mining cards in the weak network area is taken as the total number of manned mining cards to be scheduled in the weak network area. When the proportion of manned mining trucks is greater than or equal to the preset threshold for manned mining trucks in weak network areas, the total number of manned mining trucks to be scheduled in weak network areas is confirmed to be zero.

3. The method according to claim 1, characterized in that, The control of the manned mining truck and the unmanned mining truck to work together in the weak network area specifically includes: Obtain the weak network transportation location corresponding to the weak network transportation task in the weak network area, and obtain the road condition information around the weak network transportation location. Evaluate the road condition information to obtain a safety score. The weight and dimensions of the transported materials are obtained from the weak network transport task, and the road parameters are obtained from the road condition information. The road parameters, the weight of the transported materials, and the dimensions are weighted and summed to obtain the operation complexity score. Obtain the target signal strength corresponding to the weak network transportation location, and obtain the total number of weak network scheduling mining cards required to execute the weak network transportation task; Filter out the number of idle manned mining trucks and the number of idle unmanned mining trucks in the weak network area. If the safety score, the operation complexity score, and the target signal strength all meet the weak network transportation conditions for unmanned mining trucks, and the number of idle unmanned mining trucks in the weak network is less than the total number of mining trucks scheduled in the weak network, then the number of basic mining trucks required in the weak network is obtained by subtracting the number of idle unmanned mining trucks in the weak network from the total number of mining trucks scheduled in the weak network. The number of manned mining trucks required for the weak network basic mining trucks are retrieved from the weak network area. The number of unmanned mining trucks available in the weak network area are combined with the number of manned mining trucks required for the weak network basic mining trucks to form a weak network basic transportation mining truck fleet, so that the weak network basic transportation mining truck fleet can go to the weak network transportation location to perform the weak network transportation task. If the safety score, the operation complexity score, and the target signal strength do not meet the weak network transportation conditions for unmanned mining trucks, and the number of idle manned mining trucks in the weak network is less than the total number of mining trucks scheduled in the weak network, then the number of idle manned mining trucks in the weak network is subtracted from the total number of mining trucks scheduled in the weak network to obtain the required number of standard mining trucks in the weak network. The required number of manned mining trucks for the weak network standard mining truck are retrieved from the weak network retrieval area. The number of unmanned mining trucks available for the manned mining trucks in the weak network and the required number of unmanned mining trucks for the weak network standard mining truck are combined to form a weak network standard transport mining truck fleet, so that the weak network standard transport mining truck fleet can go to the weak network transport location to perform the weak network transport task.

4. The method according to claim 3, characterized in that, The step of forming a basic transportation mining truck fleet for weak network mining by combining the number of unmanned mining trucks available in the weak network with the number of manned mining trucks required for the basic weak network mining truck fleet specifically includes: Obtain the location of each of the unmanned mining cards in the weak network under the number of idle unmanned mining cards; Calculate the transportation distance between the location of the unmanned mining truck in the weak network and the transportation location in the weak network, select the maximum value from multiple transportation distances as the theoretical maximum transportation distance, and determine the maximum transportation distance that the unmanned mining truck can travel normally. If the maximum transport distance is greater than or equal to the theoretical maximum transport distance, it is confirmed that the unmanned mining truck can complete the weak network transport task normally. The unmanned mining trucks with the number of idle unmanned mining trucks in the weak network are combined with the number of manned mining trucks required by the basic mining trucks in the weak network to obtain the basic transport mining truck fleet in the weak network. If the maximum transport distance is less than the theoretical maximum transport distance, then the minimum value among the multiple transport distances is selected as the theoretical minimum transport distance. If the maximum transport distance is greater than or equal to the theoretical minimum transport distance, then the target unmanned mining truck corresponding to the theoretical minimum transport distance is retrieved, and the number of target unmanned mining trucks is subtracted from the total number of mining trucks scheduled for weak network to obtain the number of mining trucks required for weak network correction. The target unmanned mining trucks are combined with the number of manned mining trucks required for weak network correction to obtain the basic transport mining truck fleet for weak network.

5. The method according to claim 1, characterized in that, The determination of the total number of unmanned mining trucks to be scheduled based on the proportion of unmanned mining trucks specifically includes: When the proportion of unmanned mining trucks is less than the preset threshold for unmanned mining trucks in the strong network area, the terrain information in the strong network area is obtained in real time, multiple strong network roads are parsed from the terrain information, and the length of the strong network road occupied by each strong network road in the strong network area is calculated. Calculate the quotient of the length of each strong network road and the length of a single mining card to obtain the number of mining cards that each strong network road can accommodate. The sum of the number of mining cards that each strong network road can accommodate is taken as the total number of strong network mining cards that can be accommodated in the strong network area. The difference between the total number of strong network mining trucks and the number of manned and unmanned mining trucks in the strong network area is taken as the total number of strong network unmanned mining trucks to be scheduled. When the proportion of unmanned mining trucks is greater than or equal to the preset threshold for unmanned mining trucks in the strong network area, the total number of unmanned mining trucks to be scheduled in the strong network area is confirmed to be zero.

6. The method according to claim 1, characterized in that, The control of the unmanned mining truck and the manned mining truck to work together in the strong network area specifically includes: Obtain the strong network transportation location corresponding to the strong network transportation task in the strong network area, and obtain the road information around the strong network transportation location. Evaluate the road information to obtain a road score. Multiple weather parameters are obtained from the current weather information of the strong network area, and the multiple weather parameters are weighted and summed to obtain an impact score; Obtain the material type from the strong network transportation task and determine the total number of strong network dispatch mining cards required to execute the strong network transportation task; The number of idle manned mining trucks and the number of idle unmanned mining trucks in the strong network area are selected from the strong network area. If the road score, impact score, and material type all meet the strong network transportation conditions for unmanned mining trucks, and the number of idle unmanned mining trucks in the strong network is less than the total number of mining trucks in the strong network scheduling, then the number of basic mining trucks in the strong network is obtained by subtracting the number of idle unmanned mining trucks in the strong network from the total number of mining trucks in the strong network scheduling. The required number of unmanned mining trucks for the strong network basic mining truck are retrieved from the strong network retrieval area. The number of unmanned mining trucks available for the strong network basic mining truck are combined with the number of unmanned mining trucks required for the strong network basic mining truck to form a strong network basic transportation mining truck fleet, so that the strong network basic transportation mining truck fleet can go to the strong network transportation location to perform the strong network transportation task. If the road score, impact score, and material type all meet the strong network transportation conditions for unmanned mining trucks, and the number of idle manned mining trucks in the strong network is less than the total number of mining trucks scheduled in the strong network, then the required number of unmanned mining trucks for the strong network standard mining trucks will be retrieved from the number of idle unmanned mining trucks in the strong network. The number of idle manned mining trucks in the strong network and the required number of unmanned mining trucks for the strong network standard mining trucks will be combined to form a strong network standard transportation mining truck fleet, so that the strong network standard transportation mining truck fleet can go to the strong network transportation location to perform the strong network transportation task.

7. The method according to claim 6, characterized in that, The step of forming a standard transport mining truck fleet by combining the number of available manned mining trucks in the strong network with the number of unmanned mining trucks required by the strong network standard mining truck fleet specifically includes: Obtain the location of each unmanned mining truck under the number of available unmanned mining trucks in the network, and plan the target route between the location of the unmanned mining truck and the network transportation point; The transportation time is obtained by dividing the transportation length of each target route by the speed of the unmanned mining truck. Select a target number of transport times that are less than or equal to a preset transport time from a plurality of transport times, wherein the preset transport time is the transport time between the candidate manned mining truck in the strong network area and the strong network transport location; If the number of targets is less than the total number of mining trucks in the strong network scheduling, and the sum of the number of targets and the number of idle manned mining trucks in the strong network is equal to or greater than the total number of mining trucks in the strong network scheduling, then the number of targets is confirmed as the required number of standard mining trucks in the strong network for unmanned mining trucks, and the number of idle manned mining trucks in the strong network and the number of unmanned mining trucks required for the standard mining trucks in the strong network are combined to form the standard transportation mining truck fleet in the strong network.

8. A scheduling device for hybrid driving mining trucks, characterized in that, The device includes an acquisition unit (201), a processing unit (202), and a control unit (203). The acquisition unit (201) acquires network data in the mining area, performs cluster analysis on the network data, and obtains a first cluster center and a second cluster center. The processing unit (202) divides the mining area into strong network areas and weak network areas based on the boundary threshold, wherein the signal strength of the strong network area is higher than that of the weak network area; determines the boundary threshold based on the first cluster center and the second cluster center; obtains the proportion of manned mining trucks in the weak network area, and determines the total number of manned mining trucks to be scheduled in the weak network area based on the proportion of manned mining trucks; schedules the total number of manned mining trucks scheduled in the weak network area to the weak network area, and restricts unmanned mining trucks from entering the weak network area; obtains the proportion of unmanned mining trucks in the strong network area, and determines the total number of unmanned mining trucks to be scheduled in the strong network area based on the proportion of unmanned mining trucks; schedules the total number of unmanned mining trucks scheduled in the strong network area to the strong network area. The control unit (203) controls the manned mining truck and the unmanned mining truck to work together in the weak network area; and controls the unmanned mining truck and the manned mining truck to work together in the strong network area.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

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