Iot-based intelligent mine auxiliary transportation comprehensive scheduling management and control method and system

Abstract

The present application relates to a kind of wisdom mine auxiliary transport comprehensive scheduling management and control method and system based on Internet of Things.The method includes: obtaining the estimated driving time of target truck to multiple gravel stations, estimated queuing time and single truck unloading duration estimate value;For the multiple gravel stations, the time cost of each gravel station is calculated respectively, the time cost includes the sum of the estimated driving time of target truck to gravel station, estimated queuing time and target truck unloading duration estimate value;Select the gravel station with the minimum time cost as the target gravel station;Wherein, estimated queuing time is 0 or the difference between the third unloading completion estimated time point of all in-transit trucks and the estimated arrival time of target truck;In-transit truck refers to the truck arriving at the estimated time earlier than target truck.The method of the present application helps to improve the efficiency of ore transfer and crushing.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of material transportation. More particularly, the present application relates to a smart mine auxiliary transportation comprehensive scheduling and control method and system based on Internet of Things. BACKGROUND

[0002] Smart mine is a mine production and management mode based on modern information technology, automation technology and intelligent equipment, aiming to realize efficient development of mine resources, safe production, green environmental protection and lean management through digitalization, networking and intelligent means. Its core is to deeply integrate artificial intelligence, Internet of Things, big data, cloud computing, 5G communication, robots and other technologies into the whole process of mine exploration, mining, transportation, processing and safety monitoring, and promote the upgrading of traditional mining to intelligent, unmanned and sustainable.

[0003] In recent years, with the rapid development of Internet of Things, 5G, artificial intelligence and other technologies, smart mine construction has become an important direction of digital transformation in the mining field. Through the deep integration of advanced technologies and mine production, smart mine has realized the efficiency, safety and intelligence of mine production. Among them, the auxiliary transportation system is an important part of mine production, and its intelligent transformation demand is increasingly urgent.

[0004] The application of Internet of Things technology in smart mine provides technical support for the comprehensive scheduling and control of auxiliary transportation system. Through the deployment of sensors, positioning devices and communication networks, real-time monitoring and collaborative scheduling of path optimization of transportation equipment are realized.

[0005] The mine area of a mine usually has multiple gravel stations, multiple quarries, multiple loaders, multiple trucks for transporting ore and a scheduling device of a scheduling center. The loader performs mining and collection operations in the quarry, the worker loads the collected ore into the truck, after a truck is full of ore, the scheduling device first selects a target gravel station for it and plans a driving route to send to the truck driver, the truck driver drives the truck to pull the ore from the quarry to the target gravel station based on the driving route planned by the scheduling device, and the gravel station crushes the ore transported by the loaded truck. However, when selecting a target gravel station, the scheduling device usually only considers the distance from the gravel station to the truck, and does not consider whether queuing is needed to reach the gravel station and the length of the queuing time, so that the time cost of the truck pulling the ore from the quarry to the gravel station for crushing may not be the minimum time cost, resulting in low processing efficiency of the ore. SUMMARY

[0006] To solve the technical problem of low processing efficiency of ore in the existing mine auxiliary transportation comprehensive scheduling and control method, the present application provides a solution in the following aspects.

[0007] In a first aspect, the present application provides a smart mine auxiliary transportation comprehensive scheduling management and control method based on Internet of Things, comprising: obtaining an estimated driving time length of a target truck to a plurality of gravel stations, an estimated queuing time length, and a single truck unloading time length estimate value;

[0008] For the plurality of gravel stations, the time cost of each gravel station is calculated respectively, and the time cost comprises the sum of the estimated driving time length of the target truck to the gravel station, the estimated queuing time length, and the target truck unloading time length estimate value;

[0009] The gravel station with the minimum time cost is selected as the target gravel station; wherein the estimated queuing time length is 0 or the difference between the third unloading completion estimated time point of all in-transit trucks and the estimated arrival time of the target truck; the in-transit truck refers to a truck that arrives at the gravel station earlier than the target truck.

[0010] The method has the beneficial effect that when calculating the estimated queuing time length of the target truck to the gravel station, not only the influence of the unloading completion time of the truck queuing at the gravel station at the current time on the unloading completion time of the in-transit truck is considered, but also the influence of the unloading completion time of the in-transit truck on the estimated queuing time length of the target truck is considered, so that the calculation of the estimated queuing time length is more accurate, and the efficiency of ore transfer and crushing is further improved.

[0011] Preferably, the method for obtaining the estimated queuing time length comprises: obtaining a first unloading completion estimated time point of a truck that is unloading at the current time; obtaining a second unloading completion estimated time point of all trucks in the truck queuing and waiting area at the current time based on the first unloading completion estimated time point, in combination with the number of trucks in the truck queuing and waiting area at the current time and the single truck unloading time length estimate value;

[0012] The third unloading completion estimated time point of all in-transit trucks is obtained based on the second unloading completion estimated time point and the estimated arrival time of each in-transit truck; if the third unloading completion estimated time point is earlier than the estimated arrival time of the current truck, the estimated queuing time length is 0, otherwise, the estimated queuing time length is the difference between the third unloading completion estimated time point of all in-transit trucks and the estimated arrival time of the target truck.

[0013] The method has the beneficial effect that when calculating the estimated queuing time length of the target truck to the gravel station, not only the influence of the unloading completion time of the truck queuing at the gravel station at the current time on the unloading completion time of the in-transit truck is considered, but also the influence of the unloading completion time of the in-transit truck on the estimated queuing time length of the target truck is considered, so that the calculation of the estimated queuing time length is more accurate, and the efficiency of ore transfer and crushing is further improved.

[0014] Preferably, the second unloading completion estimated time point of all the trucks in the truck queuing area at the current time comprises: comparing the estimated time point of the truck arriving at the service point of the stone crusher with the idle estimated time point of the stone crusher, and taking the larger one as the starting service time point of the stone crusher;

[0015] The sum of the starting service time point of the stone crusher and the estimated unloading time of a single truck is taken as the new idle estimated time point;

[0016] For the trucks in the truck queuing area, the new next idle estimated time point is iteratively obtained according to the queuing order, and the last obtained next idle estimated time point is taken as the second unloading completion estimated time point; the initial value of the idle estimated time point is the first unloading completion estimated time point.

[0017] The method has the advantages that: when the time point of all the trucks in the truck queuing area completing unloading is obtained, after the starting unloading time point and the unloading completion time point of a truck are obtained, the starting unloading time point and the unloading completion time point of the next truck are obtained according to the unloading completion time point of the truck, the iteration is performed according to the queuing order, and the unloading completion time point of the last truck is taken as the second unloading completion estimated time point, so that the second unloading completion estimated time point of all the trucks in the truck queuing area at the current time is more accurately calculated.

[0018] Preferably, the third unloading completion estimated time point of all the in-transit trucks comprises: judging whether the idle estimated time point of the stone crusher is earlier than the truck arrival time point; if the idle estimated time point is earlier than the truck arrival time point, the truck arrival time point is taken as the starting service time point of the stone crusher, otherwise, the sum of the estimated time length of the truck from the queuing area to the service point of the stone crusher and the second unloading completion estimated time point is taken as the starting service time point of the stone crusher; and the sum of the starting service time point of the stone crusher and the estimated unloading time of a single truck is taken as the new idle estimated time point.

[0019] For each in-transit truck, the new idle estimated time point is iteratively obtained according to the order of the estimated arrival time point, and the last obtained new idle estimated time point is taken as the third unloading completion estimated time point; the initial value of the idle estimated time point is the second unloading completion estimated time point.

[0020] Preferably, the method for determining the in-transit truck comprises: obtaining the estimated arrival time point of the early departure truck at the stone crushing station, and comparing the estimated arrival time point with the estimated arrival time point of the target truck; the early departure truck whose estimated arrival time point at the stone crushing station is earlier than the estimated arrival time point of the target truck is taken as the in-transit truck; the early departure truck refers to the truck whose departure time point is earlier than the current time point and whose destination is the same stone crushing station as that of the target truck.

[0021] Preferably, the method for determining the on-the-way truck further comprises: judging whether the current time is in a traffic busy time period, and if so, re-estimating the estimated time for the early departure truck to arrive at the gravel station.

[0022] The beneficial effect is that the traffic busy degree of the mine area section may change, the estimated arrival time at the station in the case of low traffic busy degree is earlier than that in the case of high traffic busy degree, and the estimated time for the early departure truck to arrive at the gravel station is re-estimated under the condition that the current time is in the time period of high traffic busy degree, thereby effectively improving the accuracy of the estimated time for the early departure truck to arrive at the gravel station.

[0023] Preferably, the method for obtaining the estimated driving time length of the target truck to a gravel station comprises:

[0024] planning a driving path of the target truck to the gravel station;

[0025] calculating the estimated driving time length according to the length of the driving path and the average driving speed of the target truck.

[0026] Preferably, the method further comprises correcting the estimated driving time length, and the correction method comprises:

[0027] calculating a traffic busy degree index of the mine area according to the number of trucks running in the mine area at the current time, and calculating a first estimated meeting delay time length of each kilometer path under the current traffic busy degree according to the traffic busy degree index of the mine area; the estimated meeting delay time length is positively correlated with the traffic busy degree index of the mine area;

[0028] taking the product of the first estimated meeting delay time length of each kilometer path under the current traffic busy degree and the total length of the driving path as a first compensation amount, and correcting the estimated driving time length according to the first compensation amount.

[0029] The beneficial effect is that the greater the traffic busy degree of the mine area, the greater the possibility of meeting between different trucks, and the application quantifies the delay time length of arriving at the gravel station caused by the meeting of each kilometer path according to the traffic busy degree of the mine area when correcting the estimated driving time length, and obtains the total delay time length of the entire driving path by combining the total length of the path, thereby improving the accuracy of the estimated driving time length.

[0030] Preferably, the traffic busy degree index of the mine area is the ratio of the number of trucks running in the mine area at the current time to the upper limit of the total number of trucks that can be accommodated in the mine area, or the ratio of the number of trucks running in the mine area at the current time to the total road network length of the mine area.

[0031] The beneficial effects are that: the more the number of trucks running in the mining area, the more the traffic in the mining area is busy, by taking the ratio of the number of trucks running in the mining area to the upper limit of the total number of trucks that the mining area can accommodate as the traffic busy degree index of the mining area, thereby realizing more accurate quantification of the traffic busy degree index of the mining area; in addition, the longer the total road network length of the mining area, the better the traffic conditions of the mining area, and the smaller the probability of passing between different trucks, by taking the ratio of the number of trucks running in the mining area at the current time to the total road network length of the mining area as the traffic busy degree index of the mining area, thereby realizing more accurate quantification of the traffic busy degree index of the mining area.

[0032] Preferably, the method for obtaining the estimated driving time of the target truck to the certain macadam station comprises:

[0033] Planning a driving path of the target truck to the macadam station;

[0034] Calculating the estimated driving time according to the length of the driving path and the average driving speed of the target truck.

[0035] Preferably, the method further comprises correcting the estimated driving time, and the correction method comprises:

[0036] Calculating a traffic busy degree index of the mining area according to the number of trucks running in the mining area at the current time; and calculating a first estimated passing delay time per kilometer path under the current busy degree according to the traffic busy degree index of the mining area; the estimated passing delay time is positively correlated with the traffic busy degree index of the mining area;

[0037] Taking the product of the first estimated passing delay time per kilometer path under the current busy degree and the total length of the driving path as a first compensation amount; and correcting the estimated driving time according to the first compensation amount.

[0038] In a second aspect, the present application provides a comprehensive scheduling and control system for intelligent mine auxiliary transportation based on Internet of Things, comprising a processor and a memory, the memory stores computer program instructions, when the computer program instructions are executed by the processor, the comprehensive scheduling and control method for intelligent mine auxiliary transportation based on Internet of Things of the present application is realized.

[0039] The beneficial effects of the present application are that: the comprehensive scheduling and control method for intelligent mine auxiliary transportation based on Internet of Things of the present application can effectively improve the efficiency of ore transfer and crushing. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is a flow chart of the comprehensive scheduling and control method for intelligent mine auxiliary transportation based on Internet of Things according to the embodiment of the present application;

[0041] Figure 2is a first time axis schematic diagram according to an embodiment of the present application;

[0042] Figure 3 is a second time axis schematic diagram according to an embodiment of the present application;

[0043] Figure 4 is a structure schematic diagram of an Internet of Things-based smart mine auxiliary transportation comprehensive scheduling and control system according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

[0045] An Internet of Things-based smart mine auxiliary transportation comprehensive scheduling and control method embodiment:

[0046] As shown in Figure 1 , the Internet of Things-based smart mine auxiliary transportation comprehensive scheduling and control method of the present application comprises:

[0047] S1, obtaining an estimated driving duration of the target truck to the multiple gravel stations, an estimated queuing duration, and a single-truck unloading duration estimate value;

[0048] The obtaining manner of the single-truck unloading duration estimate value can be: counting the unloading durations of multiple trucks that arrive at the gravel station at historical time points, and averaging the unloading durations of each truck to take the average as the single-truck unloading duration estimate value.

[0049] S2, calculating the time cost of each gravel station, specifically: for the multiple gravel stations, the time cost of each gravel station is calculated respectively, and the time cost includes the sum of the estimated driving duration of the target truck to the gravel station, the estimated queuing duration, and the target truck unloading duration estimate value;

[0050] S3, obtaining the target gravel station according to the time cost of each gravel station, specifically: selecting the gravel station with the minimum time cost as the target gravel station; wherein the estimated queuing duration is 0 or the difference between the third unloading completion estimated time point of all in-transit trucks and the estimated arrival time point of the target truck; the in-transit truck refers to a truck that arrives at the gravel station earlier than the current truck.

[0051] After the scheduling center obtains the target gravel station of the target truck, it will send it to the driver of the current truck, so that the driver transports the ore according to the driving path of the target gravel station.

[0052] The method of the present invention first calculates the time cost of each crushing station when selecting the target crushing station, and selects the crushing station with the lowest time cost as the target crushing station for the target truck. When calculating the time cost, not only the travel time required for the truck to reach the crushing station is taken into account, but also the queuing waiting time after arriving at the crushing station; thereby effectively improving the efficiency of ore transfer and crushing.

[0053] In one embodiment, the method further includes: determining a set of candidate crushing stations within the target mining area, the set including the plurality of crushing stations corresponding to the target truck.

[0054] The working status of a crushing plant includes: shutdown, malfunction, and normal operation. Crushing plants in the normal operation status are selected as elements of the set of candidate crushing plants.

[0055] In one embodiment, the method for obtaining the estimated queuing time includes:

[0056] S101. Obtain the estimated time point of the first unloading completion of the truck currently unloading;

[0057] In this embodiment, as Figure 2 As shown, the estimated first unloading completion time can be obtained based on the time when the trucks currently unloading begin unloading and the estimated unloading time for a single truck. The truck dispatch center can obtain the time when the trucks currently unloading at each crushing station begin unloading through the Internet of Things.

[0058] S102. Based on the estimated time point of completion of the first unloading, combined with the number of trucks in the truck queuing area at the current moment and the estimated unloading time of a single truck, obtain the estimated time point of completion of the second unloading when all trucks in the truck queuing area have unloaded at the current moment.

[0059] In this embodiment, the second estimated unloading completion time can be obtained by first multiplying the number of trucks in the truck queuing area by the estimated unloading time of a single truck, and then adding this product to the first estimated unloading completion time. In other embodiments, other methods can also be used for estimation.

[0060] S103. Based on the second estimated unloading completion time and the estimated arrival time of each truck in transit, obtain the third estimated unloading completion time when all trucks in transit have completed unloading. If the third estimated unloading completion time is earlier than the estimated arrival time of the current truck, the estimated queuing time is 0; otherwise, the estimated queuing time is the difference between the third estimated unloading completion time of all trucks in transit and the estimated arrival time of the target truck.

[0061] In one embodiment, obtaining the estimated second unloading completion time point when all trucks in the truck queue area have completed unloading includes:

[0062] S201. Compare the estimated time when the truck arrives at the crusher service point with the estimated time when the crusher is idle, and take the larger of the two as the crusher start service time.

[0063] There are several ways to calculate the estimated time for a truck to arrive at the crusher service point. For example, the estimated time for the truck to arrive at the crusher service point can be the sum of the time required for the truck to travel from the queuing area to the crusher service point and the time when the previous truck finished unloading.

[0064] S202. The sum of the crusher’s start-of-service time and the estimated unloading time of a single truck is used as the new estimated idle time point.

[0065] S203. For trucks in the truck queuing area, according to the queuing order, iteratively obtain the next estimated idle time point, and use the last obtained next estimated idle time point as the second estimated unloading completion time point; the initial value of the estimated idle time point is the first estimated unloading completion time point.

[0066] like Figure 2 As shown, if the estimated time for truck A to arrive at the crusher service point is t1, and the estimated time for the crusher to be idle is t2 (later than t1), then t2 is the time when the crusher starts service, and t3, which is after t2 and a time T away from t2, is the new estimated time for idle. Figure 2 The "T" represents the estimated unloading time for a single truck, and the arrow indicates the direction of future moments.

[0067] like Figure 3 As shown, if the estimated time for truck B to arrive at the crusher service point is t4, and the estimated time for the crusher to be idle is t5 (earlier than t4), then t4 is the time when the crusher starts service, and t6, which is after t4 and a time T away from t4, is the new estimated time for idle. Figure 3 The "T" represents the estimated unloading time for a single truck, and the arrow indicates the direction of future moments.

[0068] In one embodiment, obtaining the third estimated time point for completion of unloading of all en route trucks includes:

[0069] S301. Determine whether the estimated idle time of the crusher is earlier than the arrival time of the truck. If the estimated idle time is earlier than the arrival time of the truck, then the arrival time of the truck shall be taken as the start time of the crusher's service. Otherwise, the sum of the estimated time of the truck from the queuing area to the crusher service point and the estimated time of the second unloading completion shall be taken as the start time of the crusher's service.

[0070] S302, taking the sum of the stone crusher start service time and the single truck unloading duration estimate value as a new idle estimate time point;

[0071] S303, for each truck in transit, iteratively obtaining the new idle estimate time point according to the order of the estimated arrival time point, taking the last obtained new idle estimate time point as the third unloading completion estimate time point; the initial value of the idle estimate time point is the second unloading completion estimate time point.

[0072] In one embodiment, the method for determining the truck in transit includes: obtaining the estimated arrival time point of the early departure truck at the stone crushing station, and comparing it with the estimated arrival time point of the target truck; the early departure truck whose estimated arrival time point at the stone crushing station is earlier than the estimated arrival time point of the target truck is taken as the truck in transit; the early departure truck refers to the truck whose departure time is earlier than the current time and whose destination is the same stone crushing station as the target truck.

[0073] In one embodiment, the method for determining the truck in transit further includes: judging whether the current time is in a traffic busy period, and if it is in the traffic busy period, re-estimating the estimated arrival time point of the early departure truck at the stone crushing station.

[0074] In one embodiment, the method for obtaining the estimated driving duration of the target truck to a certain stone crushing station includes:

[0075] S401, planning the driving path of the target truck to the stone crushing station;

[0076] The A* algorithm or other suitable methods can be used to plan the driving path of the target truck to the stone crushing station.

[0077] S402, calculating the estimated driving duration according to the length of the driving path and the average driving speed estimate value of the target truck.

[0078] In one embodiment, the estimated driving duration is further corrected, and the correction method includes:

[0079] S501, calculating the mine area traffic busy degree index according to the number of trucks running in the mine area at the current time; and calculating the first estimated meeting delay duration generated per kilometer of path according to the mine area traffic busy degree index; the first estimated meeting delay duration is positively correlated with the mine area traffic busy degree index.

[0080] In this embodiment, the calculation expression of the first estimated meeting delay duration is:

[0081] ;

[0082] In the formula, K represents the proportionality coefficient, and M represents the traffic congestion index in the mining area. The specific value of K can be determined experimentally.

[0083] In this embodiment, the traffic congestion index of the mining area is the ratio of the number of trucks operating in the mining area at the current time to the upper limit of the total number of trucks that the mining area can accommodate, or the ratio of the number of trucks operating in the mining area at the current time to the total road network length of the mining area.

[0084] S502, The product of the first estimated meeting delay time per kilometer of path under the current busy level and the total length of the travel path is used as the first compensation amount; the estimated travel time is corrected based on the first compensation amount.

[0085] In the above embodiments, the estimated travel time is corrected based on the total length of the travel route. In another embodiment, the method for correcting the estimated travel time includes:

[0086] S601. Mark all road sections in the mining area and identify key passing risk areas, including key passing risk points and key passing risk road sections.

[0087] There are several ways to identify key areas of risk for passing vehicles. For example, intersections can be used as key points of risk for passing vehicles; road segments with a minimum width less than a preset threshold can be used as key road segments of risk for passing vehicles.

[0088] S602. Calculate the traffic congestion index of the mining area based on the number of trucks operating in the mining area at the current time; calculate the second estimated meeting delay time for each truck passing through a key meeting risk area based on the traffic congestion index of the mining area; the second estimated meeting delay time is positively correlated with the traffic congestion index of the mining area.

[0089] In this embodiment, the second estimated meeting delay time The calculation expression is:

[0090] ;

[0091] In the formula, denoted by , where M represents the traffic congestion index in the mining area.

[0092] S603. Based on the driving path of the target truck to the gravel station, obtain the number of key passing risk areas it passes through. Multiply the number by the second estimated passing delay time as the second compensation amount. Sum the second compensation amount with the estimated driving time as the corrected estimated driving time.

[0093] As the traffic of the mining area is more busy, the probability of the truck meeting the oncoming truck when passing through the key meeting risk area is higher, and the current truck also meets more oncoming trucks when passing through the key meeting risk area, so that the delay time caused by the meeting is longer, the second estimated meeting delay time is positively correlated with the traffic busy degree index of the mining area, and the number of key meeting risk areas that the current truck needs to pass through to reach the gravel station is combined to more accurately calculate the compensation amount of the estimated driving time, so that the estimated driving time obtained is more accurate.

[0094] The above two embodiments are respectively based on the total length of the driving path and the number of passing through the key meeting risk area to correct the estimated driving time, in another embodiment, the method for correcting the estimated driving time comprises:

[0095] According to the traffic busy degree index of the mining area, the first estimated meeting delay time generated by each kilometer path is calculated , and the second estimated meeting delay time generated by each key meeting risk area passed by the truck ; the first estimated meeting delay time and the second estimated meeting delay time are positively correlated with the traffic busy degree index of the mining area; and then the corrected estimated driving time is calculated , and the calculation expression is:

[0096] ;

[0097] In the formula, indicates the estimated driving time before correction, and are the first weight coefficient and the second weight coefficient respectively, indicates the total length of the driving path, indicates the number of key meeting risk areas passed by the target truck according to the driving path.

[0098] The method of the present application weights and sums the meeting delay time caused by the path length and the meeting delay time caused by the number of passing through the key meeting risk area, and compensates the estimated driving time according to the sum obtained, so that the influence of the path length and the number of passing through the key meeting risk area on the meeting delay time is repeatedly considered when correcting the estimated driving time, so that the corrected value of the estimated driving time calculated is more accurate.

[0099] The embodiment of the intelligent mine auxiliary transportation comprehensive scheduling and control system based on the Internet of Things:

[0100] The present application also provides an intelligent mine auxiliary transportation comprehensive scheduling and control system based on the Internet of Things. Figure 4As shown, the Internet of Things based smart mine auxiliary transportation comprehensive scheduling management and control system comprises a processor and a memory, and the memory stores computer program instructions which, when executed by the processor, implement the Internet of Things based smart mine auxiliary transportation comprehensive scheduling management and control method in the above embodiments.

[0101] The Internet of Things based smart mine auxiliary transportation comprehensive scheduling management and control system further comprises a communication bus and a communication interface and other components well known to those skilled in the art, the settings and functions of which are known in the art, and thus will not be described here.

[0102] Although the present specification has shown and described a number of embodiments of the application, it will be apparent to those skilled in the art that many modifications, variations, and substitutions can be made thereunto in the course of implementation. It should be understood that various alternatives to the embodiments of the application described herein can be employed in practicing the application.

Claims

1. An Internet of Things-based intelligent mine auxiliary transportation comprehensive scheduling management and control method, characterized in that, The application relates to a method for selecting a target aggregate station for a target truck. The method comprises the following steps: determining a set of candidate aggregate stations from a target mining area, the set comprising a plurality of aggregate stations corresponding to the target truck, the working state of the aggregate stations comprising shutdown, fault and normal working, and taking the aggregate stations with normal working state as elements of the set of candidate aggregate stations; obtaining estimated driving time, estimated queuing time and estimated unloading time of the target truck to the plurality of aggregate stations; calculating the time cost of each aggregate station, the time cost comprising the sum of the estimated driving time, the estimated queuing time and the estimated unloading time of the target truck; the method for obtaining the estimated queuing time comprises the following steps: 2.The IoT-based intelligent mine auxiliary transportation comprehensive scheduling management and control method of claim 1, wherein obtaining a first estimated unloading completion time point of the truck currently unloading; obtaining a second estimated unloading completion time point of the truck in the queuing area based on the first estimated unloading completion time point, the number of trucks in the queuing area and the estimated unloading time of a single truck; obtaining a third estimated unloading completion time point of all the trucks in the queuing area based on the second estimated unloading completion time point and the estimated arrival time of each truck; if the third estimated unloading completion time point is earlier than the estimated arrival time of the target truck, the estimated queuing time is 0, otherwise, the estimated queuing time is the difference between the third estimated unloading completion time point of all the trucks in the queuing area and the estimated arrival time of the target truck; obtaining the third estimated unloading completion time point of all the trucks in the queuing area comprises the following steps: judging whether the idle estimated time point of the aggregate station is earlier than the arrival time of the truck; if the idle estimated time point is earlier than the arrival time of the truck, the arrival time of the truck is taken as the starting service time of the aggregate station, otherwise, the sum of the estimated time of the truck from the queuing area to the service point of the aggregate station and the second estimated unloading completion time point is taken as the starting service time of the aggregate station; the sum of the starting service time of the aggregate station and the estimated unloading time of a single truck is taken as the new idle estimated time point; for each truck in the queuing area, the new idle estimated time point is iteratively obtained according to the order of the estimated arrival time of the truck, and the last obtained new idle estimated time point is taken as the third estimated unloading completion time point; the initial value of the idle estimated time point is the second estimated unloading completion time point; selecting the aggregate station with the minimum time cost as the target aggregate station; the estimated queuing time is 0 or the difference between the third estimated unloading completion time point of all the trucks in the queuing area and the estimated arrival time of the target truck; the truck in the queuing area refers to the truck with an estimated arrival time earlier than that of the target truck. the method for obtaining the second estimated unloading completion time point of all the trucks in the queuing area comprises the following steps: comparing the estimated arrival time of the truck at the service point of the aggregate station with the idle estimated time point of the aggregate station, and taking the larger one as the starting service time of the aggregate station; the sum of the starting service time of the aggregate station and the estimated unloading time of a single truck is taken as the new idle estimated time point. For the truck in the truck queuing area, iteratively obtain a new next idle estimated time point according to the queuing order, and take the last obtained next idle estimated time point as the second unloading completion estimated time point; the initial value of the idle estimated time point is the first unloading completion estimated time point. 3.The IoT-based intelligent mine auxiliary transportation comprehensive scheduling management and control method of claim 1, wherein, The method for determining the in-transit truck comprises: obtaining an estimated arrival time of the early-departure truck at the gravel station, and comparing the estimated arrival time with an estimated arrival time of the target truck; if the estimated arrival time of the early-departure truck at the gravel station is earlier than the estimated arrival time of the target truck, the early-departure truck is determined as the in-transit truck; the early-departure truck refers to a truck that departs earlier than the current time and has the same destination as the target truck. 4.The IoT-based intelligent mine auxiliary transportation comprehensive scheduling management and control method of claim 3, wherein, The method for determining the in-transit truck further comprises: judging whether the current time is in a traffic rush hour period; if the current time is in the traffic rush hour period, re-estimating the estimated arrival time of the early-departure truck at the gravel station. 5.The IoT-based intelligent mine auxiliary transportation comprehensive scheduling management and control method of claim 1, wherein, The method for obtaining the estimated driving duration of the target truck to a gravel station comprises: planning a driving path of the target truck to the gravel station; calculating the estimated driving duration according to the length of the driving path and the average driving speed of the target truck. 6.The Internet of Things based intelligent mine auxiliary transportation comprehensive scheduling management and control method according to any one of claims 1 to 5, characterized in that, The method further comprises correcting the estimated driving duration, and the correction method comprises: calculating a mine area traffic congestion index according to the number of trucks running in the mine area at the current time; and calculating a first estimated meeting delay duration per kilometer of the path under the current congestion according to the mine area traffic congestion index; the estimated meeting delay duration is positively correlated with the mine area traffic congestion index; multiplying the first estimated meeting delay duration per kilometer of the path under the current congestion by the total length of the driving path to obtain a first compensation amount; and correcting the estimated driving duration according to the first compensation amount.

7. The Internet of Things-based intelligent mine auxiliary transportation integrated scheduling management and control method of claim 6, wherein The mine area traffic congestion index is a ratio of the number of trucks running in the mine area at the current time to an upper limit of the total number of trucks that can be accommodated in the mine area, or a ratio of the number of trucks running in the mine area at the current time to the total road network length of the mine area.

8. An Internet of Things-based intelligent mine auxiliary transportation comprehensive scheduling management and control system, characterized in that, The system comprises a processor and a memory, and the memory stores computer program instructions that, when executed by the processor, implement the method for comprehensively scheduling and controlling auxiliary transportation of a smart mine based on the Internet of Things according to any one of claims 1-7.

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