Mine area vehicle dispatching method and system

Abstract

The present disclosure provides a mining area vehicle dispatching method and system. The method includes: obtaining real-time positioning data of a current vehicle on a mining area map and recording real-time driving data of the current vehicle, generating current multidimensional travel data for the current vehicle on a traveled sub-segment of a current route; finding a historical multidimensional travel data set from a historical travel data set, so that the current multidimensional travel data matches the historical multidimensional travel data; and estimating the required travel time for the current vehicle on an untraveled sub-segment of the current route based on the found historical multidimensional travel data.

Description

Technical Field

[0001] The present invention relates to the field of mining, and in particular to a mining vehicle dispatching method and system. Background Art

[0002] With China's vigorous promotion of intelligent mining, the field of 5G-based autonomous driving in smart mines is developing rapidly. Currently, open-pit mine maps are updated rapidly, data volumes are large, and most path planning algorithms are primarily focused on urban roads. Few methods have been studied for autonomous path planning in complex scenarios. In open-pit mines, mine truck drivers operate in hot and cold environments, and accidents are frequent, posing a significant threat to safe operations and the lives of miners. The implementation of autonomous vehicles in mines is a future trend in open-pit mine applications. To achieve this, global static path planning capabilities for these vehicles are crucial. Summary of the Invention

[0003] The present disclosure aims to provide a mining vehicle dispatching method and system, which can reasonably dispatch mining vehicles and combine the operation of loading and unloading positions, such as forklifts, so that the mining vehicles can maintain high efficiency and minimize costs.

[0004] In one aspect, a mining vehicle dispatching method includes:

[0005] Acquiring data of a mining area map, determining a plurality of routes for traveling from a first location toward a second location on the mining area map, and dividing each of the plurality of routes into a corresponding plurality of sub-segments;

[0006] Acquiring vehicle positioning data, obtaining and recording real-time data of the vehicle traveling on a plurality of corresponding sub-segments of each of the plurality of routes, thereby forming historical multi-dimensional travel data of the vehicle to construct a historical travel set;

[0007] Acquiring real-time positioning data of the current vehicle on the mining area map and recording real-time travel data of the current vehicle, and generating current multi-dimensional travel data of the current vehicle on a traveled sub-segment of a plurality of sub-segments of a current route of the plurality of routes;

[0008] Finding a piece of historical multidimensional travel data from the historical travel set so that the current multidimensional travel data of the current vehicle matches a corresponding portion of the historical multidimensional travel data;

[0009] The required travel time of the untraveled sub-segment of the current route of the current vehicle is estimated based on the found matching historical multi-dimensional travel data.

[0010] In one embodiment, a time vector is used to represent historical multidimensional travel data of a vehicle on each of a plurality of routes, as well as current multidimensional travel data of the vehicle on a traveled sub-segment of a plurality of sub-segments of the current route. The cosine value of the angle between the time vector representing the historical multidimensional travel data of the plurality of routes and the time vector representing the current multidimensional travel data is calculated based on a cosine similarity formula, and the historical multidimensional travel data corresponding to the maximum value of the calculated cosine value is determined as the historical multidimensional travel data that matches the corresponding portion of the current multidimensional travel data.

[0011] In one embodiment, the route segment vector representing the historical multi-dimensional travel data is The route segment vector representing the corresponding route segment of the current multi-dimensional travel data is The cosine similarity formula is:

[0012]

[0013] in and

[0014] R i1 ,R i2 ,R ij Respectively represent the travel time of the current vehicle in the 1st, 2nd and jth sections of route i, R' k1 ,R' k2 ,R' km represents the driving time of the vehicle on the 1st, 2nd and mth sections of the kth route in the historical travel set, n represents the number of historical multidimensional travel data, and θ represents the angle between the time vector representing the historical multidimensional travel data of multiple routes and the time vector representing the current multidimensional travel data.

[0015] In one embodiment, each of the historical multi-dimensional travel data or the current multi-dimensional travel data includes at least one or more of the following data: distance, average travel time, average travel speed, average fuel consumption, and load capacity of multiple sub-segments corresponding to the route.

[0016] In one embodiment, the mining area vehicle dispatching method further includes:

[0017] For a plurality of vehicles on the mine map, the required travel time of the untraveled sub-section of the current route of each of the plurality of vehicles is estimated, so as to calculate the time when the plurality of vehicles arrive at the second location.

[0018] In one embodiment, the mining area vehicle dispatching method further includes:

[0019] The time required for the multiple vehicles to complete the task at the second location is calculated, and the calculated stay time is combined with the statistics to determine in real time the number of vehicles to be put into operation, so that the determined number of vehicles can arrive at the second location from the first location in sequence and complete the task at the second location, and the vehicles have a shortened waiting time at the first location and / or the second location.

[0020] In one embodiment, the mining area vehicle scheduling method further includes: adjusting the number of currently operating vehicles based on the determined number of vehicles.

[0021] In one embodiment, the mining area vehicle scheduling method further includes: storing the current multi-dimensional travel data of the current vehicle into a historical travel set, so as to update the dynamic historical travel set.

[0022] In one embodiment, the mining vehicle scheduling method further includes: obtaining and updating the current position of the vehicle in real time through a vehicle-mounted positioning device, thereby updating the current multi-dimensional travel data of the traveled sub-segments of the vehicle's current route, and dynamically estimating the travel time required for the untraveled sub-segments of the current route of the current vehicle.

[0023] In one embodiment, the mine vehicle dispatching method further comprises: determining the number of first devices at the first location and / or the number of second devices at the second location based on the determined number of vehicles so that the vehicles have shortened waiting time at the first location and / or the second location.

[0024] In one embodiment, one of the first device and the second device is a loading device configured to load items into a vehicle, and the other of the first device and the second device is an unloading device configured to unload items from the vehicle;

[0025] The vehicle scheduling method includes: obtaining the time required for the loading device to complete loading of the vehicle, and obtaining the time required for the unloading device to complete unloading of the vehicle, thereby determining the number of the loading devices and the unloading devices.

[0026] In one embodiment, the vehicle completes the trip from the first location to the second location without any human intervention.

[0027] Another aspect of the present disclosure provides a mining vehicle dispatching system, comprising:

[0028] a map module configured to import data of a mine map, determine a plurality of (fixed) routes for traveling from a first location toward a second location on the mine map, and divide each of the plurality of routes into a corresponding plurality of (fixed) sub-segments;

[0029] a device management module configured to communicate with a vehicle positioning module to obtain vehicle positioning data; and

[0030] A big data analysis and scheduling module, wherein the big data analysis and scheduling module is configured to:

[0031] Acquiring the mining area map information from the map module and receiving the vehicle positioning data acquired by the equipment management module, obtaining and recording real-time data of the vehicle traveling on the corresponding multiple sub-segments of each of the multiple routes, thereby forming historical multi-dimensional travel data of the vehicle to construct a dynamic historical travel set;

[0032] Acquiring real-time positioning data of the current vehicle on the mine map and recording real-time driving data of the current vehicle, and generating current multi-dimensional travel data of the current vehicle on multiple sub-segments of the current route;

[0033] Finding a piece of historical multidimensional travel data from the historical travel set so that the current multidimensional travel data of the current vehicle matches a corresponding portion of the historical multidimensional travel data; and

[0034] The required travel time of the untraveled sub-segment of the current route of the current vehicle is estimated based on the found matching historical multi-dimensional travel data.

[0035] In one embodiment, the big data analysis and scheduling module is configured to:

[0036] A time vector is used to represent historical multidimensional travel data of a vehicle on each of multiple routes, as well as current multidimensional travel data of the current vehicle on multiple sub-segments of the current route that have been traveled. The cosine value of the angle between the time vector representing the historical multidimensional travel data of the multiple routes and the time vector representing the current multidimensional travel data is calculated based on a cosine similarity formula. The historical multidimensional travel data corresponding to the maximum value of the calculated cosine value is determined as the historical multidimensional travel data that matches the corresponding portion of the current multidimensional travel data.

[0037] In one embodiment, the route segment vector representing the historical multi-dimensional travel data is The route segment vector representing the corresponding route segment of the current multi-dimensional travel data is The cosine similarity formula is:

[0038]

[0039] in and

[0040] R i1 ,R i2 ,Rij Respectively represent the travel time of the current vehicle in the 1st, 2nd and jth sections of route i, R' k1 ,R' k2 ,R' km represents the driving time of the vehicle on the 1st, 2nd and mth sections of the kth route in the historical travel set, n represents the number of historical multidimensional travel data, and θ represents the angle between the time vector representing the historical multidimensional travel data of multiple routes and the time vector representing the current multidimensional travel data.

[0041] In one embodiment, each of the historical multi-dimensional travel data or the current multi-dimensional travel data includes at least one or more of the following data: distance, average travel time, average travel speed, average fuel consumption, and load capacity of multiple sub-segments corresponding to the route.

[0042] In one embodiment, the big data analysis and scheduling module is configured to:

[0043] For a plurality of vehicles on the mine map, the required travel time of the untraveled sub-section of the current route of each of the plurality of vehicles is estimated, so as to calculate the time when the plurality of vehicles arrive at the second location.

[0044] In one embodiment, the big data analysis and scheduling module is configured to:

[0045] The time required for the multiple vehicles to complete the task at the second location is calculated, and the calculated stay time is combined with the statistics to determine in real time the number of vehicles to be put into operation, so that the determined number of vehicles can arrive at the second location from the first location in sequence and complete the task at the second location, and the vehicles have a shortened waiting time at the first location and / or the second location.

[0046] In one embodiment, the big data analysis and scheduling module is configured to:

[0047] Based on the determined number of vehicles, the number of vehicles currently operating is adjusted.

[0048] In one embodiment, the big data analysis and scheduling module is configured to:

[0049] The current multi-dimensional travel data of the current vehicle is stored in the historical travel set so as to update the dynamic historical travel set.

[0050] In one embodiment, the big data analysis and scheduling module is configured to:

[0051] The vehicle's current position is acquired and updated in real time through the onboard positioning device, thereby updating the current multi-dimensional travel data of the traveled sub-segments of the vehicle's current route, and dynamically estimating the travel time required for the untraveled sub-segments of the vehicle's current route.

[0052] In one embodiment, the big data analysis and scheduling module is configured to:

[0053] Based on the determined number of vehicles, the number of first devices at the first location and / or the number of second devices at the second location is determined so that the vehicles have a shortened waiting time at the first location and / or the second location.

[0054] In one embodiment, one of the first device and the second device is a loading device configured to load items into a vehicle, and the other of the first device and the second device is an unloading device configured to unload items from the vehicle;

[0055] The vehicle scheduling method includes: obtaining the time required for the loading device to complete loading of the vehicle, and obtaining the time required for the unloading device to complete unloading of the vehicle, thereby determining the number of the loading devices and the unloading devices.

[0056] In one embodiment, the vehicle completes the trip from the first location to the second location without any human intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a schematic block diagram of a mining area vehicle dispatching system according to one embodiment of the present disclosure;

[0058] Figure 2 is a roadmap according to one embodiment of the present disclosure;

[0059] Figure 3 is a roadmap according to one embodiment of the present disclosure;

[0060] Figure 4 is a roadmap according to one embodiment of the present disclosure. Specific embodiments

[0061] To more clearly illustrate the objectives, technical solutions, and advantages of the present disclosure, embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the following description of the embodiments is intended to explain and illustrate the overall concept of the present disclosure and should not be construed as limiting the present disclosure. In the specification and drawings, the same or similar reference numerals refer to the same or similar parts or components. For the sake of clarity, the drawings are not necessarily drawn to scale, and some well-known parts and structures may be omitted in the drawings.

[0062] Unless otherwise defined, technical or scientific terms used in this disclosure should have the ordinary meaning understood by a person of ordinary skill in the art to which this disclosure belongs. The terms "first," "second," and similar terms used in this disclosure do not denote any order, quantity, or importance, but are simply used to distinguish different components. The terms "a" or "an" do not exclude a plurality. Terms such as "include" or "comprise" mean that the element or object preceding the term includes the elements or objects listed after the term and their equivalents, but do not exclude other elements or objects. Terms such as "connected" or "connected" are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," "right," "top," or "bottom" are used only to indicate relative positional relationships; if the absolute position of the described objects changes, the relative positional relationship may also change accordingly. When an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element, or intervening elements may be present.

[0063] One embodiment of the present disclosure provides a mining area vehicle scheduling method, which includes obtaining mining area map data, such as existing three-dimensional map data of a mining area, which may include ground conditions, such as vehicle passable areas, impassable areas, loading positions, unloading positions, etc., so as to determine multiple routes from the loading position to the unloading position.

[0064] In one embodiment, Figure 2 As shown in the figure, the following Figure 2 The multiple routes 1-4 shown from the first position (e.g., loading position) to the second position (e.g., unloading position) can be considered as fixed here. The vehicle needs to select a route from the multiple routes from the first position to the second position instead of driving arbitrarily. This is consistent with the actual situation of complex mining areas or areas that need to be avoided; and Figure 2In the embodiment shown in FIG, intersections between the multiple planned routes are avoided, thereby preventing vehicles from crossing each other. Due to the terrain, the multiple routes are tortuous and are divided into multiple segments. In this embodiment, each route is divided into multiple sub-segments. Each route can be segmented according to the road surface conditions. For example, in route 1, sub-segments 1.1, 1.3, and 1.4 (indicated next to the sub-segments of the route in the figure) are curved sub-segments, and sub-segments 1.2, 1.5, and 1.6 are substantially straight sub-segments. For example, in route 4, sub-segment 4.1 is a normal flat section, sub-segments 4.2, 4.3, and 4.4 are uphill sub-segments, and sub-segments 4.5 and 4.6 are downhill sub-segments, and the slopes of the sub-segments are different. The route can be divided into multiple sub-segments based on the speed of the vehicle on each sub-segment, so that the average speed of the vehicle on each sub-segment varies less. Each route can also be segmented based on factors such as different fuel consumption on each sub-segment.

[0065] Figure 3 FIG. 4 shows a plurality of routes 1-3 determined in a mine map according to another embodiment of the present disclosure. Figure 2 Unlike the embodiment shown in Figure 3 Routes 1-3 in the example intersect. Figure 3 The route in can be determined according to the actual road conditions.

[0066] In an embodiment of the present disclosure, the route may be determined, wherein the segments of each route are also determined. In such an embodiment, if a vehicle travels, for example, sub-segments 1.1 and 1.2, the vehicle will continue along route 1 to travel sub-segments 1.3, 1.4, and so on until the entire route 1. Even in the case of Figure 3 In the route shown, Route 1 and Route 2 intersect. However, when the vehicle reaches sub-segment 1.2, it does not turn onto Route 2 at the intersection of Route 1 and Route 2, but continues to travel along sub-segment 1.2.

[0067] In other embodiments of the present disclosure, Figure 4 As shown, routes can intersect, and in these intersecting routes, regardless of whether each route intersects or even overlaps with other routes, the vehicle will follow Figure 4 In other words, the vehicle will travel along the sub-segments of the determined route, regardless of whether there are intersections or forks. Figure 4In the illustrated embodiment, Route 2 includes sub-segments 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, and 2.7. As can be seen, sub-segment 2.1 overlaps with sub-segment 1.1 of Route 1, and sub-segment 2.2 partially overlaps with sub-segment 1.2 of Route 1. In this embodiment, when a vehicle travels along Route 2, it will follow sub-segments 2.1, 2.2, 2.3, and 2.7. In other words, when a vehicle reaches the end of sub-segment 2.2 and faces a fork in the road, it will continue along Route 2 to sub-segment 2.3, ignoring Route 1. In this scenario, if the vehicle travels along Route 1, it will take the left route at the fork in the road, while if the vehicle travels along Route 2, it will take the right route at the fork in the road. In short, whether the vehicle takes the left or right route is predetermined.

[0068] exist Figure 2-4 The small circles in the diagram are to help illustrate the sub-segments and are not any other markings on the actual route or other features with any significance related to the actual road surface.

[0069] It should be understood that in other embodiments of the present disclosure, multiple routes such as 5, 6, 7, etc. may be planned in the mine map, and the number of routes may be determined according to the actual situation of the mine. More routes may be beneficial for arranging the distribution of vehicles on different routes, reducing possible intersections or even traffic jams. However, whether it is, for example Figure 2 and 3 Whether there are fewer routes or more routes, the above principles of the present disclosure are applicable.

[0070] For example, in one embodiment, each vehicle can be assigned or specified a route upon departure, along which the vehicle will travel from a first location to a second location. This is advantageous because each vehicle is assigned a specific route upon departure and can subsequently travel along a fixed route. This approach reduces the computational effort required to dispatch vehicles and can proactively control the distribution of vehicles along all routes, thereby improving vehicle efficiency and utilization.

[0071] In another embodiment, the mining vehicle scheduling method further includes starting to schedule vehicles when the vehicles are distributed on different routes. In other words, even if the vehicle route is not specified at the beginning, the vehicle's driving data on the untraveled sub-section can be estimated based on the vehicle's already traveled data.

[0072] The mining area vehicle dispatching method also includes obtaining vehicle positioning data. Since the map data of the mining area is available, the route of the vehicle in the mining area can be obtained and recorded, e.g. Figure 2-4The data for the vehicle traveling along the route shown in FIG. A vehicle travels along multiple routes multiple times, thereby obtaining data for the vehicle traveling on multiple routes and multiple times. The data for each travel is recorded to form historical multi-dimensional travel data, thereby constructing a dynamic historical travel set. The historical travel set can be dynamic, that is, subsequent vehicle travel data can be used to continuously update the historical travel set.

[0073] According to an embodiment, the vehicle's driving data is multidimensional, including multiple aspects of the vehicle's driving data on each sub-segment of the corresponding route, such as, but not necessarily including, the sub-segment's distance, average driving time, average driving speed, average fuel consumption, and load. This data is related to road conditions, such as uphill, downhill, and curved routes, and may also be related to weather conditions. For example, on rainy days, the road surface is slippery, so the vehicle's driving speed will be slower. In other words, the vehicle's driving speed, average fuel consumption, and load on each sub-segment indirectly reflect the road conditions and weather conditions. Only when the multidimensional driving data including these data are completely consistent or close, the vehicle's driving is substantially the same or close.

[0074] Accordingly, each of the historical multi-dimensional travel data or the current multi-dimensional travel data in the present disclosure includes at least one or more of the data listed above: the distance of multiple sub-segments corresponding to the route, the average driving time, the average driving speed, the average fuel consumption, and the load capacity, etc.; it should be understood that other relevant data may also be included, such as the age of the vehicle, performance indicators such as power, power per liter, torque, etc.

[0075] The mining area vehicle dispatching method further includes obtaining the real-time positioning data of the current vehicle on the mining area map and recording the real-time data of the current vehicle's travel, generating the current multi-dimensional travel data of the current vehicle on the multiple sub-segments of the current route. Figure 1 In the embodiment shown, the current vehicle has traveled the sub-segments 1.1 and 1.2, for example, and its travel data is acquired to form current multi-dimensional travel data of the sub-segments traveled by the current vehicle.

[0076] At this point, according to the mining area vehicle scheduling method of an embodiment of the present disclosure, a historical multidimensional travel data set is found from the historical travel data set, so that the current vehicle's current multidimensional travel data matches the corresponding portion of the historical multidimensional travel data set. In other words, a historical multidimensional travel data set is found in which the vehicle's travel data on sub-segments 1.1 and 1.2 is most similar to the current vehicle's travel data on sub-segments 1.1 and 1.2. In this case, according to the mining area vehicle scheduling method of the present disclosure, it is possible to estimate that the current vehicle's current route is the same as the route in the found historical multidimensional travel data set. In other words, it is possible to estimate the sub-segments that the current vehicle has not traveled and the travel time required to travel on the remaining sub-segments.

[0077] According to an embodiment of the present disclosure, since the multidimensional trip data includes multiple aspects such as the sub-segment of vehicle travel, travel speed, travel time, fuel consumption, and load, when two travel trips match or have the highest degree of proximity, the two travel trips will be substantially similar or identical, at least the route and speed of the vehicle travel, and the average travel time are very close or even the same; in other words, if one or more of the above factors exist in the historical multidimensional trip data found, such as different loads for the same vehicle, the historical multidimensional trip data found will be different from the current multidimensional trip data of the sub-segment of the current vehicle travel, and the speed and average travel time of the current vehicle will also be different. The historical multidimensional trip data found does not match, or the degree of proximity is not the highest, and the historical multidimensional trip data is discarded. Based on this, according to the mining area vehicle scheduling method disclosed in the present disclosure, it is possible to estimate how long the current vehicle will take to travel the untraveled portion of the current route based on the historical multidimensional trip data found that matches the corresponding portion of the current multidimensional trip data.

[0078] In one embodiment, a time vector is used to represent historical multidimensional travel data of a vehicle on each of multiple routes, as well as current multidimensional travel data of the vehicle on multiple sub-segments of the current route. The cosine value of the angle between the time vectors representing the historical multidimensional travel data for the multiple routes and the time vector representing the current multidimensional travel data is calculated based on a cosine similarity formula. The historical multidimensional travel data corresponding to the maximum calculated cosine value is determined as the historical multidimensional travel data that matches the corresponding portion of the current multidimensional travel data.

[0079] Each route can be marked with R i (i≥1), i is the route identifier, for example, if route 1, i is 1. Each route is subdivided into multiple sub-segments, denoted as R ij j is the sub-segment identifier, for example, R 12 It is sub-segment 2 of route 1.

[0080]

[0081] R above ij represents the travel time of a vehicle on the jth section of the i-th route.

[0082] The vehicle is on the jth section of route i (i.e., section R ij ) is recorded as S ij Drive the vehicle on R i The average driving time, vehicle operating status information, and vehicle speed are stored in the column database. The stored data TR ki Defined as:

[0083]

[0084] TR k (TR represents the vehicle, subscript k represents the vehicle number), G i The actual tonnage loaded, i represents the route number, the value of i is the same as R i The i in has the same meaning as represents the average travel time, V i represents the horizontal speed of the vehicle, Indicates the vehicle operating status.

[0085] Record vehicle TR ki The average driving time, average fuel consumption, average driving speed of the segments in the route, and TR k and the shift number as the row key value, TR kij Defined as:

[0086]

[0087] TR ki TR in the formula represents the vehicle, subscript k represents the vehicle number, i represents the route number, and the value of i is the same as R i have the same meaning), represents the average travel time, represents the horizontal speed of the vehicle, Indicates the average driving speed. ki and TR kij Store in the database. Get the current vehicle on the route R ij The time vector of the segment is filled with zeros for the remaining segments. Get all the time vectors of the historical segment.

[0088] The route segment vector representing the historical multi-dimensional travel data is The route segment vector representing the corresponding route segment of the current multi-dimensional travel data is The cosine similarity formula is:

[0089]

[0090] in and

[0091] R i1 , R i2 , R ij Respectively represent the driving time of the current vehicle on the 1st, 2nd, ..., jth sections of route i, R' k1 , R' k2 ,...R' kmrepresents the vehicle's travel time on segments 1, 2, ..., m of route k in the historical travel data set. n represents the index of the historical multidimensional travel data, and θ represents the angle between the time vector representing the historical multidimensional travel data for multiple routes and the time vector representing the current multidimensional travel data. n can be a natural number, for example, 1, 2, 3, ..., N (where N is the number of historical multidimensional travel data).

[0092] According to an embodiment of the present disclosure, the mining vehicle scheduling method further includes: for multiple vehicles on the mining map, estimating the required travel time of the untraveled sub-section of the current route of each of the multiple vehicles, thereby counting the time it takes for the multiple vehicles to arrive at the second location.

[0093] The routes, remaining sub-segments, and arrival times of all vehicles on all roads in the mining area can be estimated in a very short time. This allows real-time tracking of the order of all vehicles arriving at the second location. If a high concentration of vehicles arrive at the second location, vehicles may be waiting there (e.g., to unload ore), reducing vehicle utilization efficiency. This means that if there are too many vehicles, the number of vehicles may need to be reduced, or the intervals between vehicle departures or routes may need to be adjusted. If there are no vehicles at the second location, meaning that the operating mechanisms at the second location, such as forklifts, are idle, consideration may be given to increasing the number of vehicles, for example, by adding vehicles to routes with fewer vehicles.

[0094] In the present disclosure, the second location can be, for example, a loading or unloading location, such as a location for unloading ore. Upon arrival at the second location, vehicles need to unload ore, which requires a certain amount of time to complete. Vehicles unload ore in the order they arrive at the second location. If the time interval between vehicles arriving at the second location is shorter than the time it takes to complete unloading, vehicles arriving later will need to wait for the preceding vehicle to unload, which may be detrimental to efficiency. It should be understood that the first location and the second location can be other operational locations. In this embodiment, the time required for each of the multiple vehicles to complete, for example, loading or unloading at the second location is calculated. This time is then combined with the estimated arrival time of the vehicles at the second location to determine the order in which the multiple vehicles will arrive at the second location. The number of vehicles to be put into operation is then determined in real time, allowing the determined number of vehicles to arrive at the second location in sequence and complete their tasks at the second location. Furthermore, the vehicles experience a shortened waiting time at the first and / or second locations, for example, to zero.

[0095] In one embodiment of the present disclosure, the vehicle's current location can be obtained through an onboard positioning device to update the vehicle's current location in real time, thereby updating the current multi-dimensional travel data for the traveled sub-segments of the vehicle's current route and dynamically estimating the required travel time for the untraveled sub-segments of the vehicle's current route. This facilitates updating the estimated vehicle speed and travel time on the remaining sub-segments when the external environment changes. For example, if a sudden rain makes the road slippery, the vehicle's average speed may change. In this case, the historical travel data obtained in the historical travel data set may change, and the estimated travel time for the vehicle on the same route will also change.

[0096] In one embodiment of the present disclosure, the mining vehicle scheduling method further includes: determining, based on the determined number of vehicles, the number of first equipment at a first location and / or the number of second equipment at a second location so that the vehicles have a shortened waiting time at the first location and / or the second location. For example, in one embodiment, the first location is where a loading device loads ore, and a forklift requires a certain amount of time to load the ore for each vehicle; the second location is where an unloading device unloads ore, and a forklift requires a certain amount of time to unload the ore for each vehicle. A vehicle loads ore at a first location, then travels along a route to a second location, and unloads the ore at the second location. The time required to complete the entire process includes a fixed loading time for loading the ore at the first location and a fixed unloading time for unloading the ore at the second location, as well as a non-constant driving time on the route. By estimating the driving time, the time required for the entire vehicle process can be estimated, thereby enabling vehicle scheduling to be achieved, thereby shortening the waiting time of the vehicle at the first location and / or the second location. Here, the waiting time is the time outside of the vehicle loading or unloading operations; it can also shorten the idle time of the forklift at the first location and / or the second location, that is, the time the forklift at the first location and / or the second location waits for the arrival of the vehicle is shortened, thereby improving the output efficiency of the forklift and the entire mining area.

[0097] In another embodiment, the vehicle dispatching method may include: obtaining the time required by the loading device to complete loading of the vehicle, and obtaining the time required by the unloading device to complete unloading of the vehicle, thereby determining the number of loading devices and unloading devices. According to this embodiment, in order to shorten the idle time of the forklifts at the first location and / or the second location, the number of loading devices and unloading devices at the first location and / or the second location may be reduced. In this manner, the number of idle forklifts can be reduced, and optimally, the number of idle forklifts can be reduced to zero.

[0098] In the present disclosure, the vehicle can be an unmanned vehicle, and the journey from the first location to the second location is an unmanned process, which can achieve maximum economic benefits. Furthermore, a fixed driving route can be set, and a driving route can be assigned to the vehicle or allocated based on actual conditions. Once assigned a route, the vehicle will travel along the fixed route without changing routes at forks or intersections. This configuration can reduce the computational complexity of estimating the time it takes for an unmanned vehicle to complete the entire loading, driving, and unloading process using big data, while also ensuring high accuracy and efficiency.

[0099] One aspect of the present disclosure provides a mining area vehicle dispatching system 100, such as Figure 1 As shown, a map module 20 and a device management module 30 may be included.

[0100] The map module 20 is configured to import data from a mine map, determine multiple, e.g., fixed, routes from a first location to a second location on the mine map, and divide each of the multiple routes into corresponding, e.g., fixed, sub-segments. In other words, the routes may be pre-defined, and the sub-segments may also be pre-defined.

[0101] The equipment management module 30 is configured to communicate with the vehicle positioning module 50 to obtain vehicle positioning data. The equipment management module 30 can also be configured to, for example, count the number of vehicles and forklifts, and dispatch vehicles (causing some vehicles to begin transporting or stop transporting, etc.) to adjust the number of vehicles in use. The equipment management module 30 can also be configured to obtain vehicle-related data such as fuel consumption and load capacity from the vehicles, as well as forklift operating data such as unloaded and loaded loads per unit time.

[0102] The mining vehicle dispatching system 100 may further include a big data analysis and dispatching module 10. The big data analysis and dispatching module 10 may be configured to perform a variety of tasks.

[0103] For example, the big data analysis and scheduling module 10 can obtain the mining area map information from the mapping module 20 and receive the vehicle positioning data and vehicle-related data obtained by the equipment management module 30. It can then derive and record real-time data of the vehicle's multiple travels along the corresponding multiple sub-segments of each of the multiple routes, thereby forming historical multi-dimensional travel data for the vehicle to construct a dynamic historical travel set. Here, the dynamic historical travel set refers to the incorporation of subsequently acquired vehicle travel data into the historical travel set. Furthermore, the data obtained by the equipment management module 30 can be provided to the big data analysis and scheduling module 10.

[0104] For example, the big data analysis and scheduling module 10 can be configured to obtain the real-time positioning data of the current vehicle on the mine map and record the real-time data of the current vehicle's travel, generating the current multi-dimensional travel data of the current vehicle on multiple sub-segments of the current route.

[0105] For example, the big data analysis and scheduling module 10 may be configured to find a historical multidimensional travel data from the historical travel set so that the current multidimensional travel data of the current vehicle matches a corresponding portion of the historical multidimensional travel data.

[0106] For example, the big data analysis and scheduling module 10 may also be configured to estimate the required travel time of the untraveled sub-segment of the current route of the current vehicle based on the historical multidimensional travel data found to match the corresponding portion of the current multidimensional travel data.

[0107] In one embodiment, the big data analysis and dispatching module 10 of the mining vehicle dispatching system 100 is configured to:

[0108] A time vector is used to represent historical multidimensional travel data of a vehicle on each of multiple routes, as well as current multidimensional travel data of the current vehicle on multiple sub-segments of the current route that have been traveled. The cosine value of the angle between the time vector representing the historical multidimensional travel data of the multiple routes and the time vector representing the current multidimensional travel data is calculated based on a cosine similarity formula. The historical multidimensional travel data corresponding to the maximum value of the calculated cosine value is determined as the historical multidimensional travel data that matches the corresponding portion of the current multidimensional travel data.

[0109] Similar to the method embodiment described above in the present disclosure, using cosine similarity to perform vector operations can find a historical multi-dimensional trip data from the historical trip set, so that the vector representing the historical multi-dimensional trip data The route segment vector corresponding to the route segment representing the current multi-dimensional travel data is The cosine value is the largest.

[0110] The big data analysis and scheduling module 10 is also configured to implement all the steps or operations in the method described above.

[0111] The big data analysis and scheduling module 10 is configured to store the current multi-dimensional travel data of the current vehicle into the historical travel set so as to update the dynamic historical travel set.

[0112] In one embodiment, the big data analysis and scheduling module 10 is configured to obtain the time required for the loading equipment to complete loading of the vehicle and the time required for the unloading equipment to complete unloading of the vehicle, thereby determining the number of loading equipment and unloading equipment. The big data analysis and scheduling module 10 can input instructions to the equipment management module 30, and dispatch vehicles and forklifts, or other tools through communication between the equipment management module 30 and the vehicle.

[0113] In one embodiment, the vehicle is an unmanned vehicle.

[0114] The multiple embodiments of the present disclosure are not intended to limit the principles or structures of the present disclosure, but are intended to help understand the principles and technical solutions of the present disclosure. After reading the embodiments of the present disclosure, those skilled in the art will be able to think of other embodiments not described in the present disclosure, and these embodiments not described in the present disclosure should also be considered to be included in the present disclosure. The embodiments of the present disclosure and the embodiments not described in the present disclosure can be combined to derive other embodiments of the present disclosure. The scope of the present invention shall be based on the definition of the claims.

Claims

1. A mining area vehicle dispatching method, comprising: obtaining data of a mining area map, determining a plurality of routes for a vehicle to travel from a first location toward a second location on the mining area map, and dividing each of the plurality of routes into a corresponding plurality of sub-segments; Acquiring vehicle positioning data, obtaining and recording real-time data of the vehicle traveling multiple times on multiple sub-segments of each of the multiple routes, thereby forming historical multi-dimensional travel data of the vehicle to construct a historical travel set; Acquiring real-time positioning data of the current vehicle on the mining area map and recording real-time travel data of the current vehicle, and generating current multi-dimensional travel data of a traveled sub-segment of a current route among a plurality of sub-segments of the multiple routes; Find at least one historical multidimensional travel data from the historical travel set, so that the current multidimensional travel data of the current vehicle matches the corresponding part of the historical multidimensional travel data; estimating the required travel time of the untraveled sub-segment of the current route of the current vehicle based on the found matching historical multi-dimensional travel data; wherein a time vector is used to represent historical multidimensional travel data of the vehicle for each of the multiple routes, and current multidimensional travel data of the vehicle for a sub-segment traveled on the multiple sub-segments of the current route; a cosine value of an angle between the time vectors representing the historical multidimensional travel data for the multiple routes and the time vector representing the current multidimensional travel data is calculated based on a cosine similarity formula; and the historical multidimensional travel data corresponding to the maximum calculated cosine value is determined as the historical multidimensional travel data that matches the corresponding portion of the current multidimensional travel data; The route segment vector representing the historical multidimensional travel data is , the route segment vector representing the corresponding route segment of the current multi-dimensional travel data is , the cosine similarity formula is: in ,and , R i1 , R i2 ,R ij Respectively represent the travel time of the current vehicle in the 1st, 2nd and jth sections of route i, R' k1 , R' k2 ,R' km Indicates the travel time of the vehicle on the 1st, 2nd and mth sections of the kth route in the historical travel set. n is a natural number, indicating the number of the historical multidimensional travel data. The angle between the time vector representing the historical multidimensional travel data of multiple routes and the time vector representing the current multidimensional travel data; Each of the historical multi-dimensional travel data or the current multi-dimensional travel data includes at least one or more of the following data: distances, average travel time, average travel speed, average fuel consumption, and load capacity of multiple sub-segments corresponding to the route.

2. The mining area vehicle dispatching method according to claim 1, further comprising: For a plurality of vehicles on the mine map, the required travel time of the untraveled sub-section of the current route of each of the plurality of vehicles is estimated, so as to calculate the time when the plurality of vehicles arrive at the second location.

3. The mining area vehicle dispatching method according to claim 2, further comprising: The time required for the multiple vehicles to complete the task at the second location is calculated, and the calculated stay time is combined with the statistics to determine in real time the number of vehicles to be put into operation, so that the determined number of vehicles can arrive at the second location from the first location in sequence and complete the task at the second location, and the vehicles have a shortened waiting time at the first location and / or the second location.

4. The mining area vehicle dispatching method according to claim 3, further comprising: Based on the determined number of vehicles, the number of vehicles currently operating is adjusted.

5. The mining area vehicle dispatching method according to claim 1, further comprising: The current multi-dimensional travel data of the current vehicle is stored in the historical travel set so as to update the dynamic historical travel set.

6. The mining area vehicle dispatching method according to claim 1, further comprising: The vehicle's current position is acquired and updated in real time through the onboard positioning device, thereby updating the current multi-dimensional travel data of the traveled sub-segments of the vehicle's current route, and dynamically estimating the travel time required for the untraveled sub-segments of the vehicle's current route.

7. The mining area vehicle dispatching method according to claim 3, further comprising: Based on the determined number of vehicles, the number of first devices at the first location and / or the number of second devices at the second location is determined so that the vehicles have a shortened waiting time at the first location and / or the second location.

8. The mining vehicle dispatching method according to claim 7, wherein one of the first device and the second device is a loading device configured to load items into the vehicle, and the other of the first device and the second device is an unloading device configured to unload items from the vehicle; in, The vehicle dispatching method includes: obtaining the time required for the loading equipment to complete loading of the vehicle, and obtaining the time required for the unloading equipment to complete unloading of the vehicle, thereby determining the number of the loading equipment and the unloading equipment.

9. The mining vehicle dispatching method according to claim 1, wherein the vehicle completes the journey from the first location to the second location in an unmanned process.

10. A mining area vehicle dispatching system comprising: a map module configured to import data of a mine map, determine a plurality of routes for traveling from a first location to a second location on the mine map, and divide each of the plurality of routes into a corresponding plurality of sub-segments; a device management module configured to communicate with a vehicle positioning module to obtain vehicle positioning data; and A big data analysis and scheduling module, wherein the big data analysis and scheduling module is configured to: Acquire mining area map information from the map module and receive vehicle positioning data acquired by the equipment management module, derive and record real-time data of the vehicle traveling multiple times along the corresponding multiple sub-segments of each of the multiple routes, thereby forming historical multi-dimensional travel data of the vehicle to construct a dynamic historical travel set; Acquiring real-time positioning data of the current vehicle on the mining area map and recording real-time data of the current vehicle's travel, generating current multi-dimensional travel data of the current vehicle's traveled sub-segments in multiple sub-segments of the current route; Finding a piece of historical multidimensional travel data from the historical travel set so that the current multidimensional travel data of the current vehicle matches a corresponding portion of the historical multidimensional travel data; as well as estimating the required travel time of the untraveled sub-segment of the current route of the current vehicle based on the found matching historical multi-dimensional travel data; The big data analysis and scheduling module is configured to: use a time vector to represent historical multidimensional travel data of a vehicle on each of a plurality of routes, and current multidimensional travel data of a vehicle on a traveled sub-segment of a plurality of sub-segments of a current route; calculate, based on a cosine similarity formula, a cosine value of an angle between the time vectors representing the historical multidimensional travel data of the plurality of routes and the time vector representing the current multidimensional travel data; and determine the historical multidimensional travel data corresponding to the maximum calculated cosine value as the historical multidimensional travel data that matches the corresponding portion of the current multidimensional travel data; The route segment vector representing the historical multidimensional travel data is , the route segment vector representing the corresponding route segment of the current multi-dimensional travel data is , the cosine similarity formula is: in ,and , R i1 , R i2 ,R ij Respectively represent the travel time of the current vehicle in the 1st, 2nd and jth sections of route i, R' k1 , R' k2 ,R' km Indicates the travel time of the vehicle on the 1st, 2nd and mth sections of the kth route in the historical travel set. n is a natural number, indicating the number of the historical multidimensional travel data. The angle between the time vector representing the historical multidimensional travel data of multiple routes and the time vector representing the current multidimensional travel data; Each of the historical multi-dimensional travel data or the current multi-dimensional travel data includes at least one or more of the following data: distances, average travel time, average travel speed, average fuel consumption, and load capacity of multiple sub-segments corresponding to the route.

11. The mining vehicle dispatching system according to claim 10, wherein the big data analysis and dispatching module is configured to: For a plurality of vehicles on the mine map, the required travel time of the untraveled sub-section of the current route of each of the plurality of vehicles is estimated, so as to calculate the time when the plurality of vehicles arrive at the second location.

12. The mining vehicle dispatching system according to claim 11, wherein the big data analysis and dispatching module is configured to: The time required for the multiple vehicles to complete the task at the second location is calculated, and the calculated stay time is combined with the statistics to determine in real time the number of vehicles to be put into operation, so that the determined number of vehicles can arrive at the second location from the first location in sequence and complete the task at the second location, and the vehicles have a shortened waiting time at the first location and / or the second location.

13. The mining vehicle dispatching system according to claim 12, wherein the big data analysis and dispatching module is configured to: Based on the determined number of vehicles, the number of vehicles currently operating is adjusted.

14. The mining vehicle dispatching system according to claim 10, wherein the big data analysis and dispatching module is configured to: The current multi-dimensional travel data of the current vehicle is stored in the historical travel set so as to update the dynamic historical travel set.

15. The mining vehicle dispatching system according to claim 10, wherein the big data analysis and dispatching module is configured to: The vehicle's current position is acquired and updated in real time through the onboard positioning device, thereby updating the current multi-dimensional travel data of the traveled sub-segments of the vehicle's current route, and dynamically estimating the travel time required for the untraveled sub-segments of the vehicle's current route.

16. The mining vehicle dispatching system according to claim 12, wherein the big data analysis and dispatching module is configured to: Based on the determined number of vehicles, the number of first devices at the first location and / or the number of second devices at the second location is determined so that the vehicles have a shortened waiting time at the first location and / or the second location.

17. The mining vehicle dispatching system of claim 12, wherein one of the first device and the second device is a loading device configured to load items onto the vehicle, and the other of the first device and the second device is an unloading device configured to unload items from the vehicle; in, The big data analysis and scheduling module is configured to include: obtaining the time required for the loading equipment to complete loading of the vehicle, and obtaining the time required for the unloading equipment to complete unloading of the vehicle, thereby determining the number of the loading equipment and the unloading equipment.

18. The mine vehicle dispatching system of claim 10, wherein the vehicle completes the trip from the first location to the second location in an unmanned process.

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