CN115963794A - Intelligent real-time scheduling method and device for opencast mineral oil and electricity hybrid truck - Google Patents
Intelligent real-time scheduling method and device for opencast mineral oil and electricity hybrid truck Download PDFInfo
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Abstract
The invention discloses an intelligent real-time scheduling method and device for an open-pit mineral oil and electricity hybrid truck, wherein the method comprises the following steps: receiving dispatching applications of the trucks, including empty vehicle dispatching applications and heavy vehicle dispatching applications; carrying out optimal empty vehicle dispatching calculation on the truck set which sends the empty vehicle dispatching application; judging whether the remaining energy of each truck can complete the whole circulation of the empty vehicle dispatching and the next link heavy vehicle dispatching, if the truck set meets the energy requirement, generating a dispatching instruction for the truck to execute; if not, forming a new truck set by the trucks meeting the energy requirement; forming a dispatching truck set for the trucks which do not meet the energy requirement, and commanding energy charging; and aiming at the truck set which sends the heavy truck dispatching application, establishing a total objective function, solving an optimal dispatching result of the truck set which sends the heavy truck dispatching application, and generating a dispatching instruction for the heavy truck set to execute. By adopting the scheme provided by the embodiment of the invention, the scheduling time is accurately predicted, and the dynamic scheduling requirement of the strip mine truck can be met.
Description
Technical Field
The invention relates to the field of intelligent mines, in particular to an intelligent real-time scheduling method and device for an open-pit mineral oil and electricity hybrid truck.
Background
The mining of mines mostly adopts the open-pit mining mode, and the mine materials are generated through blasting, so that the mine materials are loaded, transported and unloaded. The shovel has the advantages of strong transportation flexibility and high equipment utilization rate due to the matching of the shovel (the transportation equipment is a truck, and the loading equipment is an electric shovel), and is widely applied to the links of loading, transportation and unloading.
The strip mine truck dispatching is firstly carried out by depending on the judgment of the experience of a dispatcher, has the defects of incomplete information acquisition, low efficiency, high cost and the like, and nowadays, the intelligent dispatching system can play a main role in dispatching the strip mine trucks due to the rapid development of computer and internet technologies. Common scheduling methods include single-stage scheduling and multi-stage scheduling, wherein the multi-stage scheduling equipment is high in utilization rate, high in yield, flexible in scheduling mode and strong in robustness, and is widely researched and developed. Multi-stage scheduling mostly includes: the method comprises three stages of path optimization, traffic flow planning and real-time scheduling, wherein the path optimization solves the optimal path between loading and unloading points through an acquired mine map for scheduling the running of a truck; and in the traffic flow planning stage, the optimal traffic flow rate of the path obtained in the first stage is solved according to information such as mine historical operation data, equipment quantity and production capacity constraints, and the overall operation of the mine is optimized. And in the real-time dispatching stage, the trucks are dynamically distributed by taking path optimization as a driving path and traffic flow planning as dispatching targets and combining mine real-time information.
At present, the research on mine scheduling mostly focuses on a framework of a scheduling system and a path optimization and traffic flow planning stage in multi-stage scheduling. The patent document with the publication number of CN109257510A discloses a mine dispatching communication system, the patent document with the publication number of CN112419703A provides an intelligent dispatching system for a mine intersection, the patent document with the publication number of CN114565133A generates a complete unloading and loading travel based on a traffic flow planning result and an equipment dispatching table, and dispatches a truck in real time.
However, the current real-time scheduling method is single in objective function and is difficult to be suitable for mine truck scheduling with multiple requirements. The real-time scheduling is optimized only aiming at the information of the truck at the current moment, the influence of subsequent scheduling of the truck is not considered, and the currently-made optimization is possibly invalid. The existing real-time dispatching research cannot fully consider the difference between a fuel truck and an electric truck, neglects the residual oil quantity or electric quantity of the truck, and therefore the dispatching cannot be realized due to insufficient energy, does not consider the delay time of the truck at an intersection, and cannot predict the dispatching time accurately. For the above reasons, the existing truck real-time scheduling algorithm cannot meet the dynamic scheduling requirement of the trucks in the strip mine.
Disclosure of Invention
The invention aims to provide a method and a device for intelligent real-time scheduling of a strip mine oil and electricity hybrid truck, which overcome or at least alleviate at least one of the above-mentioned defects of the prior art.
In order to achieve the aim, the invention provides an intelligent real-time scheduling method for a strip mine oil and electricity hybrid truck, which comprises the following steps:
and 3, performing optimal empty vehicle dispatching calculation on the truck set Ke sending the empty vehicle dispatching application, wherein the optimal empty vehicle dispatching calculation comprises the following steps: establishingA total objective function F (x), variables of the total objective function F (x) including sub-objective functions: time F wasted by the equipment 1 (x) Actual flow rate of loading task path and traffic flow planning deviation F 2 (x) And a truck driving cost F 3 (x) Solving the optimal scheduling result of the truck set Ke sending the empty vehicle scheduling application in a preset time period according to the total objective function F (x);
Preferably, in step 3:
wherein i represents an unloading point, j represents a loading point, k represents a kth truck, and I, J and Ke represent a total number of unloading points, a total number of loading points, and a total number of empty-vehicle-dispatched trucks, respectively; t is ijk Indicating the predicted equipment waste time, x, for dispatching truck k from unloading point i to loading point j ijk Represents decision variables of type 0-1;
wherein p is ij Loading the actual flow rate of the mission for path ij with the deviation from the traffic plan is calculated by:
wherein o is ij Representing the empty target flow rate for path ij in the flow plan, t representing the time when the current shift has started, s ij Indicating cumulative empty-vehicle dispatch payload, C kv Representing the actual load of truck k with mineral material of type v;
wherein M is ijk Representing the driving cost of the truck k from the unloading point i to the loading point j, comprising the energy consumption cost and the truck loss cost;
wherein the expression of the total objective function F (x) is as follows:
wherein, w 1 、w 2 And w 3 Is a predetermined weight coefficient, and w 1 +w 2 +w 3 ＝1，
And &>
Denotes the sub-target function, g (T), normalized by the min-max method ijk ) Correcting the coefficient for the total objective function to ensure that the priority of the idle loading point is highest;
wherein the content of the first and second substances,
wherein, F 1 Sub-targeting function F representing non-normalized processing 1 (x) Value of (A), F 1min And F 1max Sub-targeting function F representing all possible non-normalized processes separately 1 (x) Taking a minimum value and a maximum value in the values; f 2 Sub-goal function F representing unnormalized processing 2 (x) Value of (D), F 2min And F 2max Sub-targeting function F representing all possible non-normalized processes separately 2 (x) Taking a minimum value and a maximum value in values; f 3 Sub-targeting function F representing non-normalized processing 3 (x) Value of (A), F 3min And F 3max Sub-goal functions F representing all possible unnormalized processes separately 3 (x) The minimum and maximum values in the values.
Preferably, x ijk The following constraints are satisfied:
there are two scheduling cases for empty truck k: from unloading point i to loading point j or not, it is formulated as:
for an empty dispatch truck k, where only one loading point is going at the same time, it is formulated as:
for the unloading point i, at most one empty vehicle can participate in scheduling at a time, and the formula is as follows:
if the loading and unloading equipment has faults, the corresponding point is directly deleted from the working list, and the formula is expressed as follows:
where K ' represents a set of truck faults, I ' represents a set of unloader faults, and J ' represents a set of loader faults.
Preferably, step 4 may include:
distinguishing the fuel truck from the electric truck according to the truck number, and establishing an empty vehicle dispatching fuel truck set K y Electric truck set K for dispatching empty vehicles d ；
Set K for fuel truck y Medium truck k, energy requirement Q kmin1 And predicted residual energy quantity Q' rk1 The calculation formula is as follows:
Q 1 ＝Q ji'k1 +Q i'sk1
Q kmin1 ＝Q 1max
wherein Q is 1 Representing the sum of predicted fuel consumptions, Q ji'k1 Predicted fuel consumption, Q, for a truck k representing a heavy vehicle dispatch going from a loading point j to an unloading point i i'sk1 Representing the predicted fuel consumption of truck k from unloading point i' to gas station s; q 1max Represents all possible Q 1 The maximum value of (a);
Q' rk1 ＝Q rk1 -Q ijk1
wherein Q is rk1 Representing the current fuel hold, Q 'of the truck' rk1 Indicating the predicted fuel consumption of empty dispatching truck k from unloading point i to loading point j;
if the energy demand is Q kmin1 >Predicted residual energy quantity Q' rk1 If yes, the step 5 is judged, otherwise, the step 6 is carried out;
electric truck set K for dispatching empty vehicles d Medium truck k, energy requirement Q kmin2 And estimated remaining energy quantity Q' rk2 The calculation formula is as follows:
Q 2 ＝Q ji'k2 +Q i'pk2
Q kmin2 ＝Q 2max
wherein Q 2 Representing the sum of predicted power consumptions, Q ji'k2 Predicted electric energy consumption, Q, for a truck k representing a heavy vehicle dispatch from a loading point j to an unloading point i i'pk2 Representing the predicted amount of electric energy, Q, of the truck k from the unloading point i' to the charging station p 2max Represents all possible Q 2 Maximum value of (d);
Q' rk2 ＝Q rk2 -Q ijk2 +Q kc2
wherein Q is rk2 Indicating the current charge, Q, of the truck ijk2 Representing the predicted amount of electric energy consumption, Q, for an empty dispatching truck k going from unloading point i to loading point j kc2 Representing potential energy recovery electric quantity of the electric truck;
if the energy demand is Q kmin2 >Predicted residual energy quantity Q' rk2 If yes, the step 5 is judged, otherwise, the step 6 is carried out.
Preferably, step 6 may include:
for truck set K 0 Medium truck, set up fuel truck set K' y And electric truck set K' d ；
For fuel truck set K' y Truck k, dispatched to refueling with minimum expected waiting timeStation s, the calculation formula is as follows:
wherein, T isk Indicating the predicted waiting time, x, for the truck k to refuel from the unloading point i to the refuel station s isk Represents decision variables of type 0-1; when truck k goes to fueling station s, x isk The value of (1) is 1, otherwise 0 is taken; I. s and K' y Respectively representing the total number of unloading points, the total number of refueling stations and the total number of trucks in the fuel truck set;
wherein the predicted waiting time T for refueling isk The calculation formula of (a) is as follows:
T isk ＝max(t isk -t s ,0)
wherein, t isk Represents the estimated time of arrival, t, of truck k s Represents the time available at fueling station s;
estimated time of arrival t of truck k isk The calculation formula is as follows:
wherein, t now Indicates the current time, t ik Represents the expected unloading time, tt, of truck k at unloading point i isk Indicating the estimated time of travel, t, of the truck from the unloading point i to the filling station s iskn Indicating the congestion time of the truck k at the nth intersection of the road is, and N indicating the total number of intersections on the road is;
estimated driving time tt of truck isk The calculation formula is as follows:
wherein D is is Representing the optimal path distance, V, between the unloading point i and the filling station s k Represents the traveling speed of the truck k on the road is;
k 'for electric truck set' d And the truck k in the step (4) is dispatched to the parking lot for charging.
Preferably, step 7 may comprise:
deviation F of path actual flow rate from traffic flow planning 4 (x) The calculation formula of (a) is as follows:
wherein J, I and Kl represent the total number of load points, the total number of unload points, and the total number of trucks dispatched by the truck, respectively; p is a radical of formula ji Deviation of actual flow rate of mineral material from traffic planning, x, representing path ji jik Represents decision variables of type 0-1;
predicted waiting time F for truck 5 (x) The calculation formula is as follows:
wherein, T jik Represents the expected waiting time, x, for the unloading of truck k from loading point j to unloading point i jik Represents decision variables of type 0-1;
truck transportation cost F 6 (x) The calculation formula is as follows:
wherein M is jik Representing the cost, x, of the transport of the truck k from the loading point j to the unloading point i jik Represents decision variables of type 0-1;
the calculation formula of the total target function F' (x) of the heavy vehicle dispatching is as follows:
wherein, w 4 、w 5 And w 6 Is a weight coefficient, and w 4 +w 5 +w 6 ＝1，
And &>
Expressing a sub-target function normalized by a min-max method;
wherein, the first and the second end of the pipe are connected with each other,
and &>
The normalized calculation formula of (c) is as follows:
wherein, F 4 Sub-targeting function F representing non-normalized processing 4 (x) Value of (D), F 4min And F 4max Sub-targeting function F representing all possible non-normalized processes separately 4 (x) Taking a minimum value and a maximum value in values;
F 5 sub-targeting function F representing non-normalized processing 5 (x) Value of (A), F 5min And F 5max Sub-targeting function F representing all possible non-normalized processes separately 5 (x) Taking a minimum value and a maximum value in values;
F 6 sub-targeting function F representing non-normalized processing 6 (x) Value of (A), F 6min And F 6max Sub-targeting function F representing all possible non-normalized processes separately 6 (x) Minimum value in valuesAnd a maximum value.
Preferably, the actual flow rate of mineral material for the path ji deviates from the traffic planning by p ji The calculation formula of (a) is as follows:
wherein o is ji Target flow rate of mineral material, s, representing path ji in a traffic plan ji Representing the accumulated ore material amount of a route before dispatching, and t representing the time when the current shift starts; c kv Representing the actual load of truck k with mineral material of type v.
The embodiment of the invention also provides an intelligent real-time scheduling device for the opencast mineral oil and electricity hybrid-braiding truck, which comprises:
the transceiver is used for carrying out information interaction with the truck and the roadside device;
a processor configured to perform the following operations:
Preferably, in operation 3:
wherein i represents an unloading point, j represents a loading point, k represents a kth truck, I, J and Ke represent a total number of unloading points, a total number of loading points, and a total number of empty-scheduled trucks, respectively; t is ijk Indicating the predicted equipment waste time, x, for dispatching truck k from unloading point i to loading point j ijk Represents decision variables of type 0-1;
wherein p is ij Loading the actual flow rate of the mission for path ij with the deviation from the traffic plan is calculated by:
wherein o is ij Indicating a planned middle road for traffic flowEmpty target flow rate on path ij, t represents the time when the current shift has started, s ij Indicating cumulative empty-vehicle dispatch payload, C kv Representing the actual load of truck k with mineral material of type v;
wherein M is ijk Representing the driving cost of the truck k from the unloading point i to the loading point j, comprising the energy consumption cost and the truck loss cost;
wherein the expression of the overall objective function F (x) is as follows:
wherein, w 1 、w 2 And w 3 Is a predetermined weight coefficient, and w 1 +w 2 +w 3 ＝1，
And &>
Denotes the sub-target function, g (T), normalized by the min-max method ijk ) Correcting the coefficient for the total objective function to ensure that the priority of the idle loading point is highest;
wherein the content of the first and second substances,
wherein, F 1 Sub-targeting function F representing non-normalized processing 1 (x) Value of (A), F 1min And F 1max Sub-targeting function F representing all possible non-normalized processes separately 1 (x) Taking a minimum value and a maximum value in the values; f 2 Sub-goal function F representing unnormalized processing 2 (x) Value of (A), F 2min And F 2max Sub-targeting function F representing all possible non-normalized processes separately 2 (x) Taking a minimum value and a maximum value in values; f 3 Sub-targeting function F representing non-normalized processing 3 (x) Value of (A), F 3min And F 3max Sub-targeting function F representing all possible non-normalized processes separately 3 (x) Taking the minimum and maximum values of the values.
Preferably, x ijk The following constraints are satisfied:
there are two scheduling cases for empty truck k: from unloading point i to loading point j or not, it is formulated as:
for an empty dispatch truck k, where only one loading point is going at the same time, it is formulated as:
for the unloading point i, at most one empty vehicle can participate in scheduling at a time, and the formula is as follows:
if the loading and unloading equipment has faults, the corresponding point is directly deleted from the working list, and the formula is expressed as follows:
due to the adoption of the technical scheme, the invention has the following advantages:
the real-time dispatching is not only optimized aiming at the information of the truck at the current moment, but also the influence of subsequent dispatching of the truck is considered, the difference between the fuel truck and the electric truck and the residual oil quantity or electric quantity of the truck are fully considered, the dispatching time is accurately predicted, and the dynamic dispatching requirement of the truck in the strip mine can be met.
Drawings
Fig. 1 is a schematic flow chart of an intelligent real-time scheduling method for a surface oil and electricity hybrid truck according to an embodiment of the present invention.
Fig. 2 is a schematic view of a specific scene to which the intelligent real-time scheduling method for the opencast mineral oil electric hybrid truck according to the embodiment of the present invention is applied.
Fig. 3 is a schematic structural diagram of an intelligent real-time scheduling device for an open-pit mine oil-electric hybrid truck according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a system for intelligently scheduling the opencast mineral oil electric hybrid trucks in real time according to an embodiment of the present invention.
Detailed Description
In the drawings, the same or similar reference numerals are used to denote the same or similar elements or elements having the same or similar functions. Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
In the description of the present invention, the terms "central", "longitudinal", "lateral", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and therefore, should not be construed as limiting the scope of the present invention.
In the present invention, the technical features of the embodiments and implementations may be combined with each other without conflict, and the present invention is not limited to the embodiments or implementations in which the technical features are located.
The present invention will be further described with reference to the accompanying drawings and specific embodiments, it should be noted that the technical solutions and design principles of the present invention are described in detail in the following only by way of an optimized technical solution, but the scope of the present invention is not limited thereto.
The following terms are referred to herein, and their meanings are explained below for ease of understanding. It will be understood by those skilled in the art that the following terms may have other names, but any other names should be considered consistent with the terms set forth herein without departing from their meaning.
The embodiment of the invention provides an intelligent real-time scheduling method for an open-pit mineral oil and electricity hybrid truck, which comprises the following steps of:
And 2, receiving dispatching applications of the truck, including an empty dispatching application and a heavy dispatching application.
Wherein, heavy vehicle dispatching refers to dispatching of a truck loading mineral aggregate, and empty vehicle dispatching refers to dispatching of an empty truck. When a parking lot is launched, leaves a gas station or a charging station (usually arranged in the parking lot), and enters an unloading stage, the truck sends out an empty vehicle dispatching application. And when the truck enters a loading stage, sending a heavy truck dispatching application.
And 3, performing optimal empty vehicle dispatching calculation on the truck set Ke sending the empty vehicle dispatching application, wherein the optimal empty vehicle dispatching calculation comprises the following steps: establishing a total objective function F (x), ofVariables include sub-targeting functions: equipment waste time F 1 (x) Actual flow rate of loading task path and traffic flow planning deviation F 2 (x) And a truck driving cost F 3 (x) And solving the optimal scheduling result of the truck set Ke sending the empty vehicle scheduling application in a preset time period according to the total objective function F (x).
And 4, judging whether the residual energy of each truck can complete the whole circulation of the empty vehicle dispatching and the next link heavy vehicle dispatching aiming at the optimal dispatching result of the truck set Ke, and if so, entering the step 5, and if not, entering the step 6.
In step 1, all trucks are numbered and performance characterized. Wherein, the finishing form can be shown in the following table 1:
TABLE 1
Table 1 showsAn example of numbering and performance characterization is done for all trucks. Including empty and heavy vehicle speeds (V) k /V k '), energy consumption per kilometer for empty and heavy vehicle travel (Q) k1 /Q k3 ) Idle and heavy vehicle idle energy consumption per unit time (Q) k2 /Q k4 ) And cost per unit time of truck wear (C) k1 /C k2 )。
In step 3, time F is wasted by the device 1 (x) Actual flow rate of loading task path and traffic flow planning deviation F 2 (x) And a truck driving cost F 3 (x) Forming an empty vehicle dispatching total objective function F (x), which comprises the following specific steps:
3.1 projected Equipment waste time F 1 (x)
F 1 (x) The calculation formula of (c) is as follows:
wherein, T ijk Indicating the predicted equipment waste time, x, for dispatching truck k from unloading point i to loading point j ijk Representing decision variables of type 0-1. x is the number of ijk The specific meanings of (A) are as follows: when truck k goes from unloading point i to loading point j, x ijk Take 1, then x ijk Take 0.
In one embodiment, the predicted equipment waste time T ijk The calculation formula is as follows:
T ijk ＝t ijk -t j
in the formula, t ijk Represents the estimated arrival time, t, of the truck k j Indicating the moment at which the electric shovel is available. In addition to the electric shovel, other handling devices are also possible, and this is not limited herein.
When T is ijk And when the load is not less than 0, the load of the truck is distributed before the electric shovel is not finished when the dispatching truck reaches the loading point, and the waiting time of the truck is obtained.
When T is ijk When the load is less than or equal to 0, the load of the truck is distributed before the electric shovel is finished when the dispatching truck reaches the loading point, and the idle time of the electric shovel is obtained.
In one embodiment, the estimated time of arrival t of the truck k ijk The calculation formula is as follows:
in the formula, t now Indicates the current time, t ik Represents the expected unloading time, tt, of truck k at unloading point i ijk Represents the estimated travel time, t, of the truck ijkn Indicating the congestion time for truck k at the nth intersection of road ij.
Predicted unloading time t ik The calculation formula is as follows:
in the formula, C kv Representing the actual load of the truck k with mineral material of type V (including waste material), V iv Representing the speed at which the discharge point i discharges the v-type mineral aggregate.
Estimated driving time tt of truck ijk The calculation formula is as follows:
in the formula, D ij Represents the optimal path distance, V, between the unloading point i and the loading point j k Indicating the empty running speed of the truck k.
In one embodiment, the time t when the electric shovel is available j Approximated as the (expected) load completion time t of the previous truck fj(k-1) The calculation formula is as follows:
t j ＝t fj(k-1) ＝t ij(k-1) +max(T ij(k-1) ,0)+t b1 +t j(k-1)
in the formula, t ij(k-1) Representing the (predicted) arrival time, T, of the previous truck ij(k-1) Indicating that the truck is going from unloading point i to loading point j with predicted equipment waste time，t b1 Indicates the loading preparation time, t j(k-1) Representing the (expected) loading time of the previous truck.
(predicted) loading time t of the previous truck j(k-1) The calculation formula is as follows:
in the formula, C (k-1)v Representing the actual load, V, of the truck k-1 loaded with mineral material of type V (containing waste material) jv Representing the rate at which the loading point j is loaded with a v-type mineral material.
3.2 deviation F of actual flow rate of electric shovel mission Path from traffic flow planning 2 (x) The calculation formula is as follows:
in the formula, p ij For the deviation between the actual flow rate of the electric shovel mission and the traffic flow planning of the path ij, the calculation formula is as follows:
in the formula o ij Representing the empty target flow rate for path ij in the flow plan, t representing the time when the current shift has started, s ij Indicating cumulative empty-vehicle dispatch payload, C kv Representing the actual load of truck k with mineral material of type v.
3.3 truck Driving cost F 3 (x) The calculation formula is as follows:
in the formula, M ijk The driving cost of the truck k from the unloading point i to the loading point j is represented, and comprises energy consumption cost (fuel consumption and electric energy consumption) and truck loss cost.
In a kind of fruitIn the embodiment, M ijk The calculation formula is as follows:
M ijk ＝M ke +M km
in the formula, M ke Representing the cost of energy consumption, M km Representing a loss cost.
Energy consumption cost M ke Cost M of energy consumption of travelling crane ke1 And idle energy consumption cost M ke2 The composition is as follows:
M ke1 ＝D ij ×Q k1 ×Y k
in the formula, Q k1 Indicating the energy consumption per unit distance of the truck k in unloaded mode, Y k Indicating the unit price of the energy used by truck k.
In the formula, t ijkn Indicates the congestion time, T, of truck k at the nth intersection of road ij ijk Indicating the predicted time, t, spent by the truck from unloading point i to loading point j b1 Indicating the loading preparation time, t jk Represents the expected loading time, Q k2 Represents the idle energy consumption per unit time.
The truck loss cost is related to the running time, and the calculation formula is as follows:
in the formula, C k1 Representing the cost of the truck k lost per unit time of no-load operation.
According to the steps, the predicted energy consumption Q of the truck can be obtained ijk The calculation formula of (a) is as follows:
3.4 the general objective function F (x) of empty dispatching is calculated as follows:
in the formula, w 1 、w 2 And w 3 Is a predetermined weight coefficient, and w 1 +w 2 +w 3 ＝1，
And &>
Denotes the sub-target function, g (T), normalized by means of dispersion normalization (min-max method) ijk ) The coefficients are modified for the overall objective function to ensure that the priority of the idle load points is highest.
wherein, F 1 Sub-targeting function F representing non-normalized processing 1 (x) Value of (D), F 1min And F 1max Sub-goal functions F representing all possible unnormalized processes separately 1 (x) Taking a minimum value and a maximum value in values; (ii) a F 2 Sub-goal function F representing unnormalized processing 2 (x) Value of (D), F 2min And F 2max Sub-targeting function F representing all possible non-normalized processes separately 2 (x) Taking a minimum value and a maximum value in values; f 3 Sub-targeting function F representing non-normalized processing 3 (x) Value of (A), F 3min And F 3max Sub-targeting function F representing all possible non-normalized processes separately 3 (x) The minimum and maximum values in the values.
3.5 Total objective function F (x) correction factor g (T) ijk ) The calculation formula of (a) is as follows:
3.6 construction of empty vehicle scheduling constraint conditions as follows:
there are two scheduling cases for empty-vehicle-dispatching truck k: from unloading point i to loading point j or not, it is formulated as:
for an empty dispatch truck k, where only one loading point is going at the same time, it is formulated as:
for the unloading point i, at most one empty vehicle can participate in scheduling at a time, and the formula is as follows:
if the loading and unloading equipment has faults, the corresponding point is directly deleted from the working list, and the formula is expressed as follows:
wherein K ' represents a truck fault set, I ' represents an unloader fault set, and J ' represents a loader fault set.
3.6 considering that the subsequent empty vehicle dispatching may influence the current empty vehicle dispatching, the rolling time domain optimization is adopted, the current empty vehicle dispatching application truck set K is considered, and a predicted time period t is considered later K And solving the optimal solution of the time period to dispatch the truck set K, thereby reducing the influence brought by subsequent empty vehicle dispatching. Wherein the time period t K The calculation formula of (a) is as follows:
t K ＝max(tt ijk )
in step 4, for the optimal solution obtained from the empty dispatching application truck set, energy quantity inspection needs to be carried out on the optimal solution, and the predicted residual energy quantity Q 'is judged' rk Whether the energy requirement Q of the subsequent heavy vehicle dispatching and energy supply operation can be met kmin 。
In step 4, distinguishing the fuel trucks from the electric trucks according to the truck numbers, and establishing an empty vehicle dispatching fuel truck set K y Electric truck set K for dispatching empty vehicles d 。
In one implementation, the energy requirement Q kmin And predicted residual energy quantity Q' rk Regarding the truck type, the type of any truck K in the empty dispatching truck set K is judged according to the following formula:
if k is an element (1,n) y ) If the truck K is a fuel truck, the fuel truck K is placed in the fuel truck set K applied for the empty vehicle dispatching y If k ∈ (n) y +1,n), the truck K is an electric truck, and the electric truck K is placed in the electric truck set K applied for the empty vehicle dispatching d 。
4.1 set K for Fuel trucks y Truck k in
Set K for fuel truck y Medium truck k, energy requirement Q kmin1 And predicted residual energy quantity Q' rk1 The calculation formula is as follows:
Q 1 ＝Q ji'k1 +Q i'sk1
Q kmin1 ＝Q 1max
wherein Q is 1 Denotes the sum of predicted fuel consumption, Q ji'k1 Predicted fuel consumption, Q, for a truck k representing a heavy vehicle dispatch going from a loading point j to an unloading point i i'sk1 Representing the predicted fuel consumption of truck k from unloading point i' to gas station s; q 1max Represents all possible Q 1 Maximum value of (d);
Q' rk1 ＝Q rk1 -Q ijk1
wherein Q is rk1 Representing the current fuel hold, Q 'of the truck' rk1 Indicating the predicted fuel consumption of empty dispatching truck k from unloading point i to loading point j;
if the energy amount requires Q in step 4 kmin1 >Predicted residual energy quantity Q' rk1 If yes, the step 5 is judged, otherwise, the step 6 is carried out.
4.2 set K for dispatching electric trucks to empty vehicles d Truck k in
Electric truck set K for dispatching empty vehicles d Medium truck k, energy requirement Q kmin2 And estimated remaining energy quantity Q' rk2 The calculation formula is as follows:
Q 2 ＝Q ji'k2 +Q i'pk2
Q kmin2 ＝Q 2max
wherein Q is 2 Representing the sum of predicted power consumptions, Q ji'k2 Predicted electric energy consumption, Q, for a truck k representing a heavy vehicle dispatch from a loading point j to an unloading point i i'pk2 Representing the predicted amount of electric energy consumed, Q, by the truck k from the unloading point i' to the charging station p 2max Represents all possible Q 2 The maximum value of (a);
Q' rk2 ＝Q rk2 -Q ijk2 +Q kc2
wherein Q is rk2 Indicating the current charge, Q, of the truck ijk2 Representing the predicted amount of electric energy consumption, Q, for an empty dispatching truck k going from unloading point i to loading point j kc2 Representing potential energy recovery electric quantity of the electric truck;
if the energy demand is Q kmin2 >Predicted residual energy quantity Q' rk2 If yes, the step 5 is judged, otherwise, the step 6 is carried out.
In one embodiment, Q kc2 The calculation formula of (2) is as follows: q kc2 ＝η k ×m k ×h ij ×g，η k Represents the energy recovery efficiency, h ij The elevation difference, m, of the working surface representing the unloading point i and the loading point j k Representing the truck mass, g is the gravitational constant.
In step 6, for trucks K not meeting the energy demand 0 And (3) processing: for truck set K 0 Medium truck, set up fuel truck set K' y And electric truck set K' d . Similar to step 4, the fuel truck and the electric truck can be distinguished by numbering.
6.1 Collection K 'for Fuel trucks' y Truck k in
For fuel truck set K' y The truck k in (1) is dispatched to the gas station s with the minimum expected waiting time, and the calculation formula is as follows:
of these, I, S and K' y Representing the total number of unloading points, the total number of filling stations and the total number of trucks in the fuel truck pool, T, respectively isk Indicating the predicted waiting time, x, for the truck k to refuel from the unloading point i to the refuel station s isk Representing decision variables of type 0-1. For example, when truck k goes to fueling station s, x isk The value of (A) is 1, and not (B) is 0.
Wherein the predicted waiting time T for refueling isk The calculation formula of (a) is as follows:
T isk ＝max(t isk -t s ,0)
wherein, t isk Represents the estimated time of arrival, t, of truck k s Represents the time available at fueling station s;
estimated time of arrival t of truck k isk The calculation formula is as follows:
wherein, t now Indicates the current time, t ik Represents the expected unloading time, tt, of truck k at unloading point i isk Indicating the estimated time of travel, t, of the truck from the unloading point i to the filling station s iskn Indicating the congestion time of the truck k at the nth intersection of the road is, and N indicating the total number of intersections on the road is;
estimated driving time tt of truck isk The calculation formula is as follows:
wherein D is is Representing the optimal path distance, V, between the unloading point i and the filling station s k The traveling speed of the truck k on the road is shown.
In one embodiment, the fueling station availability time t is determined s Approximated as the (predicted) refuelling completion time t of the preceding truck fs(k-1) And the (predicted) refueling completion time t of the preceding truck fs(k-1) The calculation formula is as follows:
t s ＝t fs(k-1) ＝t is(k-1) +T is(k-1) +t b3 +t s(k-1)
in the formula, t is(k-1) Represents the estimated time of arrival, T, of truck k-1 is(k-1) Represents the predicted wait time, t, for refueling of truck k-1 b3 Indicating refuel preparation time, t s(k-1) Indicating the time it takes for truck k-1 to refuel.
In one embodiment, the empty dispatch described in step 3 is issued for a full-fuel truck and an empty dispatch task is executed, and the formula is as follows:
wherein the truck dispatching criteria after refueling follows the empty car dispatching criteria in step 3.
6.2 Collection K 'for electric truck' d Truck k in
K 'for electric truck set' d And (5) sending the truck k to a parking lot charging station p for charging.
In one embodiment, the charging time is 12 hours, and the parking lot full-charge electric truck k' is assigned to participate in the next work scheduling task of the work, and the formula is as follows:
in step 7, from F 4 (x)、F 5 (x) And F 6 (x) And forming a heavy vehicle dispatching objective function F' (x) and a heavy vehicle dispatching constraint condition.
7.1 deviation of actual flow rate of path from traffic flow plan F 4 (x) The calculation formula of (a) is as follows:
wherein J, I and Kl represent the total number of load points, the total number of unload points, and the total number of truck dispatch trucks, respectively; p is a radical of formula ji The actual flow rate of mineral material (i.e. the amount of mineral material passing per hour) representing the path ji deviates from the traffic plan, x jik Representing decision variables of type 0-1. When the truck k goes from the loading point j to the unloading point i, x jik The value is 1, otherwise 0.
Deviation p of actual flow rate of mineral aggregate from traffic flow plan for path ji ji The calculation formula is as follows:
in the formula o ji Target flow rate of mineral material, s, representing path ji in a traffic flow plan ji Representing the cumulative mineral charge on the way before the car dispatch and t representing the time when the current shift has started.
7.2 predicted waiting time for truck F 5 (x) The calculation formula is as follows:
wherein, T jik Represents the expected waiting time, x, for the unloading of truck k from loading point j to unloading point i jik Representing decision variables of type 0-1.
In one embodiment, the projected wait time T is unloaded jik The calculation formula is as follows:
T jik ＝max(t jik -t i ,0)
in the formula, t jik Indicating the estimated time of arrival, t, of truck k from loading point j to unloading point i i Indicating the unload point available time.
Estimated time of arrival t of truck k jik The calculation formula is as follows:
in the formula, t jk Represents the estimated loading time, tt, of truck k jik Indicating the estimated travel time, t, of truck k from loading point j to unloading point i jikn Indicating the congestion time of truck k at the nth intersection of road ji.
Predicted load time t jk The calculation formula is as follows:
in the formula, C kv Representing the actual capacity of a truck k to be loaded with mineral material of type V (containing waste material), V jk Representing the rate at which the loading point j unloads the v-type mineral material.
Estimated driving time tt of truck jik The calculation formula is as follows:
in the formula D ji Represents the optimal path distance, V ', between the loading point j and the unloading point i' k Indicating the heavy vehicle travel speed of the truck k.
Available time t of unloading point i Approximated by the (predicted) unloading completion time t of the preceding truck fi(k-1) And the (predicted) unloading completion time t of the preceding truck fi(k-1) The calculation formula is as follows:
t i ＝t fi(k-1) ＝t ji(k-1) +T ji(k-1) +t b2 +t i(k-1)
in the formula, t ji(k-1) Represents the time, T, of the predicted arrival of the truck k-1 at the unloading point i ji(k-1) Represents the expected waiting time, t, for unloading truck k-1 b2 Indicating the unloading preparation time, t i(k-1) Indicating the expected unloading time of the previous truck k-1.
7.3 truck transportation cost F 6 (x) The calculation formula is as follows:
wherein, M jik Representing the cost, x, of transportation of truck k from loading point j to unloading point i jik Representing decision variables of type 0-1.
In one embodiment, the truck transportation cost is mainly composed of two parts, namely energy consumption cost and loss cost, and the calculation formula is as follows:
M jik ＝M' ke +M' km
in formula (II) to' ke Represents energy consumption cost, M' km Representing a loss cost.
Energy cost is from cost of energy consumption to travel M' ke1 And idle energy consumption cost M' ke2 The method comprises the following two parts:
M' ke1 ＝D ji ×Q k3 ×Y k
in the formula, Q k3 Indicating the energy consumption per distance traveled by the truck, Y k Indicating the unit price of the energy used by truck k.
In the formula, Q k4 And represents the energy consumption per unit time of heavy-load idling.
The truck loss cost is related to the running time, and the calculation formula is as follows:
in the formula, C k2 Representing the cost of the truck k lost per unit time of heavy-duty operation.
According to the steps, the predicted energy consumption Q of the truck can be obtained jik The calculation formula of (a) is as follows:
7.4 the calculation formula of the gross target function F' (x) of the heavy vehicle dispatching is as follows:
wherein, w 4 、w 5 And w 6 Is a weight coefficient, and w 4 +w 5 +w 6 ＝1，
And &>
Expressing a sub-target function normalized by a min-max method;
wherein the content of the first and second substances,
and &>
The normalized calculation formula of (c) is as follows:
wherein, F 4 Sub-targeting function F representing non-normalized processing 4 (x) Value of (A), F 4min And F 4max Sub-goal functions F representing all possible unnormalized processes separately 4 (x) Taking a minimum value and a maximum value in the values;
F 5 sub-targeting function F representing non-normalized processing 5 (x) Value of (D), F 5min And F 5max Sub-targeting function F representing all possible non-normalized processes separately 5 (x) Taking a minimum value and a maximum value in values;
F 6 sub-targeting function F representing non-normalized processing 6 (x) Value of (A), F 6min And F 6max Sub-targeting function F representing all possible non-normalized processes separately 6 (x) Taking the minimum and maximum values of the values.
7.5 the construction of the heavy duty scheduling constraints is as follows:
for a heavy truck dispatching truck k, the loading mineral aggregate and the unloading point of the heavy truck dispatching truck k need to be consistent, if the loading mineral aggregate and the unloading point do not accord, the heavy truck dispatching truck k cannot walk a path ji, and the heavy truck dispatching truck k is expressed by a formula:
x jik ∈{0,1}v j ＝v i
x jik ＝0v j ≠v i
for a truck k dispatched by a heavy truck, where only one unloading point arrives at the same time, the formula is as follows:
for the loading point j, at most one heavy vehicle can participate in the scheduling at a time, and the formula is as follows:
if the loading and unloading equipment has faults, the corresponding point is directly deleted from the working list, and the formula is expressed as follows:
wherein K ' is a truck failure set, I ' is an unloader failure set, and J ' is a loader failure set.
7.6 rolling horizon optimization
Considering that the subsequent empty vehicle dispatching may influence the current heavy vehicle dispatching, a rolling time domain optimization is adopted, and the current empty vehicle dispatching application truck set K is considered, and meanwhile, a predicted time period t 'is considered backwards' K And solving the optimal solution of the time period to dispatch the truck set K, so that the influence brought by subsequent empty vehicle dispatching is reduced. Wherein period of time t' K The calculation formula of (a) is as follows:
t' K ＝max(tt jik )
by adopting the intelligent real-time scheduling method for the opencast mine oil and electricity hybrid truck, real-time scheduling is not only optimized for the information of the truck at the current moment, but also the influence of subsequent truck scheduling is considered, the difference between the oil truck and the electric truck and the residual oil quantity or electric quantity of the truck are fully considered, the scheduling time is accurately predicted, and the dynamic scheduling requirement of the opencast mine truck can be met.
Fig. 2 is a schematic view of a specific scene to which the intelligent real-time scheduling method for the opencast mineral oil electric hybrid truck according to the embodiment of the present invention is applied. As shown in fig. 2, which includes a filling station (charging station not shown), a plurality of trucks, a loading point, an unloading point (crushing station and dumping point in the figure), and a crossing schematic. Fig. 2 does not show a device for applying the intelligent real-time scheduling method for the opencast mine oil-electric hybrid truck according to the embodiment of the present invention, and it is easy to understand that the device can implement scheduling control for each truck through communication with each truck. Accordingly, a communication device is mounted on each truck for information transfer with the control device.
The embodiment of the invention also provides an intelligent real-time scheduling device for the opencast mineral oil and electricity hybrid truck, which is used for realizing the intelligent real-time scheduling method for the opencast mineral oil and electricity hybrid truck provided by the embodiment and any implementation mode thereof. As shown in fig. 3, the apparatus includes:
the transceiver 31 is used for information interaction between the truck and the roadside device; the road side device can comprise a road side monitoring device, a crossing decision planning device and the like and is used for providing road information required by scheduling for the intelligent real-time scheduling device of the opencast mineral oil and electricity hybrid truck;
a processor 32 for performing the following operations:
In operation 3:
wherein i represents an unloading point, j represents a loading point, k represents a kth truck, I, J and Ke represent a total number of unloading points, a total number of loading points, and a total number of empty delivery trucks, respectively; t is a unit of ijk Indicating the predicted equipment waste time, x, for dispatching truck k from unloading point i to loading point j ijk Represents decision variables of type 0-1;
wherein p is ij Loading the actual flow rate of the mission for path ij with the deviation from the traffic plan, calculated by:
wherein o is ij Representing the empty target flow rate for path ij in the flow plan, t representing the time when the current shift has started, s ij Indicating cumulative empty-vehicle dispatch payload, C kv Representing the actual load of truck k with mineral material of type v;
wherein M is ijk Representing the driving cost of the truck k from the unloading point i to the loading point j, comprising the energy consumption cost and the truck loss cost;
wherein the expression of the total objective function F (x) is as follows:
wherein, w 1 、w 2 And w 3 Is a predetermined weight coefficient, and w 1 +w 2 +w 3 ＝1，
And &>
Represents the sub-objective function, g (T), normalized by min-max method ijk ) Correcting the coefficient for the total objective function to ensure that the priority of the idle loading point is highest;
wherein, the first and the second end of the pipe are connected with each other,
wherein, F 1 Sub-goal function F representing unnormalized processing 1 (x) Value of (A), F 1min And F 1max Sub-goal functions F representing all possible unnormalized processes separately 1 (x) Taking a minimum value and a maximum value in values; f 2 Sub-targeting function F representing non-normalized processing 2 (x) Value of (A), F 2min And F 2max Sub-targeting function F representing all possible non-normalized processes separately 2 (x) Taking a minimum value and a maximum value in values; f 3 Sub-targeting function F representing non-normalized processing 3 (x) Value of (A), F 3min And F 3max Sub-goal functions F representing all possible unnormalized processes separately 3 (x) Taking the minimum and maximum values of the values.
x ijk The following constraints are satisfied:
there are two scheduling cases for empty truck k: from unloading point i to loading point j or not, it is formulated as:
for an empty dispatch truck k, where only one loading point is going at the same time, it is formulated as:
for the unloading point i, at most one empty vehicle can participate in scheduling at a time, and the formula is as follows:
if the loading and unloading equipment has faults, the corresponding point is directly deleted from the working list, and the formula is expressed as follows:
it should be noted that the operations performed by the processor 32 in the apparatus correspond to steps 1-7 in the method embodiment. Accordingly, processor 32 may perform any corresponding processing provided in the foregoing method embodiment and any implementation manner thereof, and for any details of operations 1 to 7, reference may be made to the limitations of steps 1 to 7 in the foregoing method embodiment and any implementation manner thereof, which are not described herein again.
The embodiment of the invention also provides an intelligent real-time scheduling system for the opencast mineral oil and electricity hybrid truck, as shown in fig. 4, comprising:
and the scheduling module 41 is arranged in a scheduling center, is internally provided with the real-time scheduling algorithm model, the path optimization model and the traffic flow planning model, and is used for solving an optimization result by combining historical data and real-time information in the data acquisition and storage module to generate a scheduling instruction for the truck to execute.
And a perception planning control module 42, which is arranged at the terminal of the truck and is used for executing the instruction generated by the scheduling module 41, perceiving the surrounding environment by using a sensor, deciding and planning a local optimal path, controlling the movement of the truck, and reminding a driver to make operation adjustment if the truck is artificially controlled.
And the vehicle-mounted monitoring module 43 is arranged at the truck terminal and used for collecting the state information of the position, the speed, the loading capacity, the residual energy quantity and the like of the truck, monitoring the driving condition of the truck so as to find and respond to the emergency condition of the truck, and uploading the data to the data acquisition and storage module for subsequent scheduling decision.
And the roadside monitoring module 44 is arranged in roadside facilities and used for monitoring the mine environment and the position state of the vehicle and prompting the change of the mine road, the vehicle and the like of the dispatching center.
And the intersection decision planning module 45 is arranged at the intersection facility, optimizes the passing sequence and speed of the trucks according to the information of the intersection trucks and the trucks to be arrived, and reduces the delay time of the trucks at the intersection.
The communication module 46 establishes an environment for information interaction of each module and each facility of the strip mine dispatching system, provides a channel for data transmission and data acquisition and storage for decision planning control of a vehicle-mounted terminal, a dispatching center and a road side facility, and helps to realize updating of the working state of loading and unloading equipment and issuing and receiving of a truck dispatching instruction.
And the data acquisition and storage module 47 is arranged in the dispatching center and is used for acquiring and storing various historical information and real-time data of strip mine operation.
And the remote control module 48 is arranged in the dispatching center, and is used for taking over the control instruction of the vehicle-mounted control module through remote manual control to ensure the operation safety of the truck in response to the sudden situation of the truck in the driving process.
By adopting the intelligent real-time scheduling device and system for the opencast mine oil and electricity hybrid truck, real-time scheduling is not only optimized aiming at the information of the truck at the current moment, but also the influence of subsequent truck scheduling is considered, the difference between the oil-fired truck and the electric truck and the residual oil quantity or electric quantity of the truck are fully considered, the scheduling time is accurately predicted, and the dynamic scheduling requirement of the opencast mine truck can be met.
Finally, it should be pointed out that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Those of ordinary skill in the art will understand that: modifications can be made to the technical solutions described in the foregoing embodiments, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An intelligent real-time scheduling method for an open-pit mineral oil and electricity hybrid truck is characterized by comprising the following steps:
step 1, numbering and sorting performance characteristics of all trucks, wherein the performance characteristics comprise at least one of the following: truck type, driving speed, actual loading capacity, energy consumption per kilometer of driving, and truck wear cost per unit time;
step 2, receiving dispatching applications of the truck, including empty dispatching applications and heavy dispatching applications;
and 3, performing optimal empty vehicle dispatching calculation on the truck set Ke sending the empty vehicle dispatching application, wherein the optimal empty vehicle dispatching calculation comprises the following steps: establishing a total objective function F (x), wherein variables of the total objective function F (x) comprise sub objective functions: time F wasted by the equipment 1 (x) Actual flow rate of loading task path and traffic flow planning deviation F 2 (x) And a truck driving cost F 3 (x) According to the total objective function F (x), solving the optimal scheduling result of the truck set Ke which sends the empty vehicle scheduling application in a preset time period;
step 4, aiming at the optimal dispatching result of the truck set Ke, judging whether the remaining energy of each truck can complete the whole circulation of the empty dispatching and the next link of heavy dispatching, if so, entering step 5, and if not, entering step 6;
step 5, if the truck set Ke meets the energy requirement, generating a scheduling instruction according to the optimal scheduling result in the step 3 for the truck to execute; if not, combining the trucks meeting the energy requirement into a new truck set K', and repeating the steps 3-5;
step 6, forming a dispatching truck set K for the trucks which do not meet the energy requirement 0 If the vehicle is a fuel truck, instructing the vehicle to go to a gas station s for refueling; if the electric truck is the electric truck, instructing the electric truck to go to a charging station p for charging;
step 7, aiming at the truck set Kl which sends out the heavy truck dispatching application, a total objective function F' (x) is established, and variables of the total objective function include sub-objective functions: deviation F between actual flow rate of path and traffic flow plan 4 (x) Waiting time of truck F 5 (x) And truck transportation cost F 6 (x) And solving the optimal scheduling result of the truck set Kl which sends the heavy truck scheduling application, and generating a scheduling instruction for the heavy truck set to execute.
2. The intelligent real-time scheduling method for the opencut oil and electricity hybrid truck according to claim 1, wherein in step 3:
wherein i represents an unloading point, j represents a loading point, k represents a kth truck, I, J and Ke represent a total number of unloading points, a total number of loading points, and a total number of empty-scheduled trucks, respectively; t is ijk Indicating the predicted equipment waste time, x, for dispatching truck k from unloading point i to loading point j ijk Represents decision variables of type 0-1;
wherein p is ij Loading the actual flow rate of the mission for path ij with the deviation from the traffic plan, calculated by:
wherein o is ij Representing the empty target flow rate for path ij in the flow plan, t representing the time when the current shift has started, s ij Indicating cumulative empty-vehicle dispatch payload, C kv Representing the actual load of truck k with mineral material of type v;
wherein M is ijk Representing the driving cost of the truck k from the unloading point i to the loading point j, comprising the energy consumption cost and the truck loss cost;
wherein the expression of the total objective function F (x) is as follows:
wherein, w 1 、w 2 And w 3 Is a predetermined weight coefficient, and w 1 +w 2 +w 3 ＝1，
And &>
Denotes the sub-target function, g (T), normalized by the min-max method ijk ) Correcting the coefficient for the total objective function to ensure that the priority of the idle loading point is highest;
wherein the content of the first and second substances,
wherein, F 1 Sub-targeting function F representing non-normalized processing 1 (x) Value of (A), F 1min And F 1max Sub-targeting function F representing all possible non-normalized processes separately 1 (x) Taking a minimum value and a maximum value in values; f 2 Sub-goal function F representing unnormalized processing 2 (x) Value of (D), F 2min And F 2max Sub-goal functions F representing all possible unnormalized processes separately 2 (x) Taking a minimum value and a maximum value in values; f 3 Sub-targeting function F representing non-normalized processing 3 (x) Value of (A), F 3min And F 3max Sub-goal functions F representing all possible unnormalized processes separately 3 (x) Taking the minimum and maximum values of the values.
3. The method for intelligently scheduling the opencast mineral oil electric hybrid truck according to claim 2, wherein x is ijk The following constraints are satisfied:
there are two scheduling cases for empty truck k: from unloading point i to loading point j or not, it is formulated as:
for an empty dispatch truck k, where only one loading point is going at the same time, it is formulated as:
for the unloading point i, at most one empty vehicle can participate in scheduling at a time, and the formula is as follows:
if the loading and unloading equipment has faults, the corresponding point is directly deleted from the working list, and the formula is expressed as follows:
wherein K ' represents a truck fault set, I ' represents an unloader fault set, and J ' represents a loader fault set.
4. The intelligent real-time scheduling method for the strip mine oil-electric hybrid trucks according to claim 1, wherein the step 4 comprises:
distinguishing the fuel truck from the electric truck according to the truck number, and establishing an empty vehicle dispatching fuel truck set K y Electric truck set K for dispatching empty vehicles d ；
Set K for fuel truck y Medium truck k, energy requirement Q kmin1 And estimated remaining energy quantity Q' rk1 The calculation formula is as follows:
Q 1 ＝Q ji'k1 +Q i'sk1
Q kmin1 ＝Q 1max
wherein Q is 1 Denotes the sum of predicted fuel consumption, Q ji'k1 Predicted fuel consumption, Q, for a truck k representing a heavy vehicle dispatch going from a loading point j to an unloading point i i'sk1 Representing the predicted fuel consumption of truck k from unloading point i' to gas station s; q 1max Represents all possible Q 1 Maximum value of (d);
Q′ rk1 ＝Q rk1 -Q ijk1
wherein Q is rk1 Representing the current fuel hold, Q 'of the truck' rk1 Indicating the predicted fuel consumption of empty dispatching truck k from unloading point i to loading point j;
if the energy demand is Q kmin1 >Predicted residual energy quantity Q' rk1 If yes, entering step 5, otherwise, entering step 6;
electric truck set K for dispatching empty vehicles d Medium truck k, energy requirement Q kmin2 And predicted residual energy quantity Q' rk2 The calculation formula is as follows:
Q 2 ＝Q ji'k2 +Q i'pk2
Q kmin2 ＝Q 2max
wherein Q 2 Representing the sum of predicted power consumptions, Q ji'k2 Predicted electric energy consumption, Q, for a truck k representing a heavy vehicle dispatch from a loading point j to an unloading point i i'pk2 Representing the predicted amount of electric energy consumed, Q, by the truck k from the unloading point i' to the charging station p 2max Represents all possible Q 2 Maximum value of (d);
Q′ rk2 ＝Q rk2 -Q ijk2 +Q kc2
wherein Q is rk2 Indicating the current charge, Q, of the truck ijk2 Representing the predicted amount of electric energy consumption, Q, for an empty dispatching truck k going from unloading point i to loading point j kc2 Representing potential energy recovery electric quantity of the electric truck;
if the energy demand is Q kmin2 >Predicted residual energy quantity Q' rk2 If yes, the step 5 is judged, otherwise, the step 6 is carried out.
5. The intelligent real-time scheduling method for the opencut oil and electricity hybrid truck according to claim 1, wherein the step 6 comprises:
for truck set K 0 Medium truck, set up fuel truck set K' y And electric truck set K' d ；
For fuel truck set K' y Truck k in (1) assigned to the addition with the smallest expected waiting timeThe oil station s, the calculation formula is as follows:
wherein, T isk Indicating the predicted waiting time, x, for the truck k to refuel from the unloading point i to the refuel station s isk Represents decision variables of type 0-1; when truck k goes to fueling station s, x isk The value of (1) is taken, and if not, 0 is taken; I. s and K' y Respectively representing the total number of unloading points, the total number of refueling stations and the total number of trucks in the fuel truck set;
wherein the predicted waiting time T for refueling isk The calculation formula of (a) is as follows:
T isk ＝max(t isk -t s ,0)
wherein, t isk Represents the estimated time of arrival, t, of truck k s Represents the time available at fueling station s;
estimated time of arrival t of truck k isk The calculation formula is as follows:
wherein, t now Indicates the current time, t ik Represents the predicted unloading time, tt, of truck k at unloading point i isk Indicating the estimated time of travel, t, of the truck from the unloading point i to the filling station s iskn Indicating the congestion time of the truck k at the nth intersection of the road is, and N indicating the total number of intersections on the road is;
predicted running time tt of truck isk The calculation formula is as follows:
wherein D is is Representing the optimal path distance, V, between the unloading point i and the filling station s k Shows the row of truck k on road isDriving speed;
for electric truck set K' d The truck k in (1) is sent to the parking lot for charging.
6. The intelligent real-time scheduling method for the opencut oil and electricity hybrid truck according to claim 1, wherein the step 7 comprises:
deviation F of path actual flow rate from traffic flow planning 4 (x) The calculation formula of (a) is as follows:
wherein J, I and Kl represent the total number of load points, the total number of unload points, and the total number of trucks dispatched by the truck, respectively; p is a radical of ji Deviation of actual flow rate of mineral material from traffic planning, x, representing path ji jik Represents decision variables of type 0-1;
predicted waiting time F of truck 5 (x) The calculation formula is as follows:
wherein, T jik Represents the expected waiting time, x, for the unloading of truck k from loading point j to unloading point i jik Represents decision variables of type 0-1;
truck transportation cost F 6 (x) The calculation formula is as follows:
wherein, M jik Representing the cost, x, of the transport of the truck k from the loading point j to the unloading point i jik Represents decision variables of type 0-1;
the calculation formula of the total target function F' (x) of the heavy vehicle dispatching is as follows:
wherein, w 4 、w 5 And w 6 Is a weight coefficient, and w 4 +w 5 +w 6 ＝1，
And &>
Expressing a sub-target function normalized by a min-max method;
wherein, the first and the second end of the pipe are connected with each other,
and &>
The normalized calculation formula of (a) is as follows:
wherein, F 4 Sub-targeting function F representing non-normalized processing 4 (x) Value of (A), F 4min And F 4max Sub-targeting function F representing all possible non-normalized processes separately 4 (x) Taking a minimum value and a maximum value in values;
F 5 sub-targeting function F representing non-normalized processing 5 (x) Value of (A), F 5min And F 5max Representing all possible non-normalised processes separatelySub-targeting function F 5 (x) Taking a minimum value and a maximum value in values;
F 6 sub-targeting function F representing non-normalized processing 6 (x) Value of (A), F 6min And F 6max Sub-targeting function F representing all possible non-normalized processes separately 6 (x) Taking the minimum and maximum values of the values.
7. The intelligent real-time scheduling method for the opencast mine oil-electric hybrid truck according to claim 6, wherein the actual flow rate of the mineral aggregate of the path ji is deviated from the traffic flow planning by a deviation p ji The calculation formula of (a) is as follows:
wherein o is ji Target flow rate of mineral material, s, representing path ji in a traffic plan ji Representing the accumulated ore material amount of a route before dispatching, and t representing the time when the current shift starts; c kv Representing the actual load of truck k with mineral material of type v.
8. The utility model provides a opencut mineral oil electricity mixes and compiles real-time scheduling device of truck intelligence which characterized in that includes:
the transceiver is used for carrying out information interaction with the truck and the roadside device;
a processor configured to perform the following operations:
operation 1, numbering and performance characteristic collation is performed for all trucks, wherein the performance characteristics include at least one of: truck type, driving speed, actual loading capacity, energy consumption per kilometer of driving, and truck wear cost per unit time;
operation 2, receiving a dispatching application of the truck, including an empty dispatching application and a heavy dispatching application;
operation 3, performing empty vehicle optimal scheduling calculation on the truck set Ke which sends the empty vehicle scheduling application, including: establishing a total objective function F (x), wherein the variables of the total objective function F (x) comprise sub objective functions: equipment waste time F 1 (x) Actual flow rate of loading task path and traffic flow planning deviation F 2 (x) And a truck driving cost F 3 (x) According to the total objective function F (x), solving the optimal scheduling result of the truck set Ke which sends the empty vehicle scheduling application in a preset time period;
operation 4, aiming at the optimal scheduling result of the truck set Ke, judging whether the remaining energy of each truck can complete the whole cycle of the empty vehicle scheduling and the next link heavy vehicle scheduling, if so, executing operation 5, and if not, executing operation 6;
operation 5, if the truck set Ke meets the energy requirement, generating a dispatching instruction according to the optimal dispatching result in the step 3 for the truck to execute; if not, forming the trucks meeting the energy requirement into a new truck set K', and repeating the operation for 3-5;
operation 6, forming dispatching trucks set K for trucks not meeting energy requirements 0 If the vehicle is a fuel truck, instructing to go to a gas station s for refueling; if the electric truck is the electric truck, instructing the electric truck to go to a charging station p for charging;
operation 7, for the set Kl of trucks issuing the heavy truck dispatch application, an overall objective function F' (x) is established, the variables of which include the sub-objective functions: deviation F of path actual flow rate from traffic flow planning 4 (x) Waiting time of truck F 5 (x) And truck transportation cost F 6 (x) And solving the optimal scheduling result of the truck set Kl which sends the heavy truck scheduling application, and generating a scheduling instruction for the heavy truck set to execute.
9. The intelligent real-time scheduling device for the opencut oil and electricity hybrid truck according to claim 8, wherein in operation 3:
wherein i represents an unloading point, j represents a loading point, k represents a kth truck, and I, J and Ke represent a total number of unloading points, a total number of loading points, and a total number of empty-vehicle-dispatched trucks, respectively; t is ijk Indicating projected equipment waste of dispatching truck k from unloading point i to loading point jM, x ijk Represents decision variables of type 0-1;
wherein p is ij Loading the actual flow rate of the mission for path ij with the deviation from the traffic plan is calculated by:
wherein o is ij Representing the empty target flow rate for path ij in the flow plan, t representing the time when the current shift has started, s ij Indicating cumulative empty-vehicle dispatch payload, C kv Representing the actual load of truck k with mineral material of type v;
wherein M is ijk Representing the driving cost of the truck k from the unloading point i to the loading point j, comprising the energy consumption cost and the truck loss cost;
wherein the expression of the total objective function F (x) is as follows:
wherein w 1 、w 2 And w 3 Is a predetermined weight coefficient, and w 1 +w 2 +w 3 ＝1，
And &>
Denotes the sub-target function normalized by min-max method, g: (T ijk ) Correcting the coefficient for the total objective function to ensure that the priority of the idle loading point is highest;
wherein the content of the first and second substances,
wherein, F 1 Sub-targeting function F representing non-normalized processing 1 (x) Value of (A), F 1min And F 1max Sub-targeting function F representing all possible non-normalized processes separately 1 (x) Taking a minimum value and a maximum value in values; f 2 Sub-goal function F representing unnormalized processing 2 (x) Value of (A), F 2min And F 2max Sub-goal functions F representing all possible unnormalized processes separately 2 (x) Taking a minimum value and a maximum value in values; f 3 Sub-goal function F representing unnormalized processing 3 (x) Value of (A), F 3min And F 3max Sub-goal functions F representing all possible unnormalized processes separately 3 (x) Taking the minimum and maximum values of the values.
10. The intelligent real-time scheduling device of strip mine oil-electric hybrid truck according to claim 9, wherein x is ijk The following constraints are satisfied:
there are two scheduling cases for empty truck k: from unloading point i to loading point j or not, it is formulated as:
for an empty dispatch truck k, where only one loading point is going at the same time, it is formulated as:
for the unloading point i, at most one empty vehicle can participate in scheduling at a time, and the formula is as follows:
if the loading and unloading equipment has faults, the corresponding point is directly deleted from the working list, and the formula is expressed as follows:
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