CN113379350B - Method and system for planning parking position of unmanned delivery vehicle - Google Patents
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Abstract
The invention relates to a method and a system for planning a parking position of an unmanned delivery vehicle, wherein the delivery target position presets available parking points in a searching range; acquiring evaluation parameters, including: the number of stops of each available stop point, the number of times associated with the delivery target position, the average value of stop time consumption, the required length from the starting position to each available stop point, and the walking and passing distance between the delivery target position and the stop point; and selecting the best stopping point from the available stopping points as the stopping position based on the evaluation parameters. The method comprehensively considers route factors, stop point self factors and user use habits, selects the optimal stop point, and ensures user satisfaction and delivery efficiency.
Description
Technical Field
The invention relates to the technical field of unmanned delivery, in particular to a method and a system for planning a parking position of an unmanned delivery vehicle.
Background
Unmanned delivery vehicles often rely on their own positioning navigation systems to achieve navigation positioning and stopping. The parking position is usually selected on the basis of distance preference or time minimization. However, in the planning manner of the parking position, the consideration factor is single, and the optimal parking position cannot be obtained.
For example, the parking location, although closest to the straight line of the target location, may not be convenient for the user to reach and may not be a common pick-up point for the occupants of the building. For another example, different docking points are different in friendliness, different in difficulty level of docking and different in time consumption of docking, which are factors to be considered in selecting the docking position.
Disclosure of Invention
In order to comprehensively consider various factors influencing delivery, the invention provides a method and a system for planning the parking position of an unmanned delivery vehicle, which comprehensively consider route factors, parking point self factors and user use habits, select an optimal parking point, and ensure the delivery satisfaction and the delivery efficiency.
In order to achieve the above object, the present invention provides a method for planning a parking position of an unmanned delivery vehicle, comprising:
searching available stop points in a preset search range of the delivery target position;
acquiring evaluation parameters, including: the number of stops of each available stop point, the number of times associated with the delivery target position, the average value of stop time consumption, the required length from the starting position to each available stop point, and the walking and passing distance between the delivery target position and the stop point;
and selecting the available stop points from the available stop points as stop positions based on the evaluation parameters.
Further, a dock management platform is constructed to manage each dock, and feature information of each dock comprises: whether the target position is available, a target position association value, the number of times of stopping and a time-consuming mean value of stopping; after the unmanned delivery vehicle or the delivery vehicle of the delivery person finishes stopping, information is sent to a stop point management platform, the stop point management platform updates the stop point characteristic information to be available, the target position association value is increased by 1, the stop times are increased by 1, and the stop time average value is updated based on the stop time.
Further, whether available includes available, unavailable, and number of times unavailable;
when the unmanned delivery vehicle arrives at a stop point but cannot stop, transmitting unavailable information, and updating the characteristic information of the stop point into unavailable information by the stop point management platform, wherein the unavailable frequency is increased by 1; and re-acquiring the evaluation parameters, and selecting the best stopping point from the available stopping points as a new stopping position based on the evaluation parameters.
Further, the map service unit obtains the time length from the departure position to each available stop point and the walking distance between the delivery target position and the stop point.
Further, in the delivery process, the map service unit monitors the expected arrival time of arrival, if the difference value between the expected arrival time and the estimated arrival time at the departure exceeds a set threshold value, the estimated parameters are acquired again, and the optimal stop point is selected from all available stop points to serve as a new stop position based on the estimated parameters.
Further, different bias coefficients are set for each evaluation parameter, bias accumulated values of each stop point are calculated, and the available stop point with the highest bias accumulated value is selected as the optimal stop point.
Further, a selection module is arranged, and the optimal stop point is selected from the available stop points to serve as the stop position based on the evaluation parameters; the selection module is internally provided with an artificial neural network model; the artificial neural network model is obtained through training.
Another aspect provides a system for planning a parking position of an unmanned delivery vehicle, comprising:
the searching module searches available stop points in a preset searching range of the delivery target position;
the evaluation parameter acquisition module acquires evaluation parameters, including: the number of stops of each available stop point, the number of times associated with the delivery target position, the average value of stop time consumption, the required length from the starting position to each available stop point, and the walking and passing distance between the delivery target position and the stop point;
the selection module selects one of the available dock points from the available dock points as a dock location based on the evaluation parameter.
Further, the system also comprises a stop point management platform for managing each stop point, and the characteristic information of each stop point comprises: whether the target position is available, a target position association value, the number of times of stopping and a time-consuming mean value of stopping; after the unmanned delivery vehicle or the delivery vehicle of the delivery person finishes stopping, information is sent to a stopping point management platform, the stopping point management platform updates the characteristic information of the stopping point to be available, the delivery target position is added to the associated target position, the associated value of the target position is added by 1, the stopping times is added by 1, and the stopping time average value is updated based on the stopping time.
Further, whether available includes available, unavailable, and number of times unavailable; when the unmanned delivery vehicle arrives at a stop point but cannot stop, transmitting unavailable information, and updating the characteristic information of the stop point into unavailable information by the stop point management platform, wherein the unavailable frequency is increased by 1; and re-acquiring the evaluation parameters, and selecting the best stopping point from the available stopping points as a new stopping position based on the evaluation parameters.
Further, the map service unit is used for acquiring the time length from the departure position to each available stop point and the walking passing distance between the delivery target position and the stop point; in the delivery process, the map service unit monitors the arrival expected time, if the difference between the arrival expected time and the estimated arrival time exceeds a set threshold value, the evaluation parameter acquisition module acquires the evaluation parameter again, and the selection module reselects the optimal stop point as a new stop position.
The technical scheme of the invention has the following beneficial technical effects:
(1) The planning method comprehensively considers route factors, stop point self factors and user use habits, selects the optimal stop point, and ensures user satisfaction and delivery efficiency.
(2) The planning mode of the invention fully considers the goods taking habit of the user, associates the user address and the stop point, considers the non-going distance of the user reaching the stop point, is convenient for the user to take goods and improves the satisfaction degree of the user.
(3) According to the planning mode, the large data analysis is utilized to obtain the average value of the number of times of stopping and the time consumption of stopping, the friendly degree of the stopping point is represented, and the risk that stopping cannot be carried out after the stopping point arrives or the time consumption of stopping is too long is reduced; the stop point management platform integrates the delivery information of the manual delivery vehicle and the unmanned delivery vehicle, and references the strategy of the delivery person, so that the intelligence of planning is improved.
(4) The planning mode of the invention monitors the arrival time, re-evaluates if an emergency occurs, re-selects the stop point, updates the route and has the emergency processing capability.
Drawings
FIG. 1 is a schematic illustration of a dock location planning procedure;
fig. 2 is a schematic diagram of the composition of a parking position planning system.
Detailed Description
The objects, technical solutions and advantages of the present invention will become more apparent by the following detailed description of the present invention with reference to the accompanying drawings. It should be understood that the description is only illustrative and is not intended to limit the scope of the invention. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the present invention.
In order to obtain an optimal parking position, planning a delivery route according to the optimal parking position and optimizing delivery, the invention provides a parking position planning method of an unmanned delivery vehicle, which comprises the following steps in combination with fig. 1:
(1) And searching available stop points in a preset search range of the delivery target position.
Stop points are marked in the map, and the stop points can be marked whether to be available or not. And searching available stop points on the map by taking the target position as the center according to a preset searching range. The preset search range may be, for example, a straight distance from the target position being within a specified range, a walking distance from the target position being within a specified range, or the like.
(2) Acquiring evaluation parameters, including: the number of stops of each available stop point, the number of times associated with the delivery target position, the average value of stop time consumption, the required length from the departure position to each available stop point, and the walking and passing distance between the delivery target position and the stop point.
The friendly degree of the stop points is represented through the stop times and the stop time consumption average value of each available stop point, and if the stop times are more, the stop time consumption average value is shorter, so that the stop point is convenient to stop.
The association times with the delivery target location represent the use habit of the user, and the address of the target location can be associated with the stop point. Further, since the target location of delivery is likely not the room itself, a class of target locations with the same pick-up habits may be associated with the stop points without having to be accurate to the specific room. For example, people in the same unit building or the same building may have similar habits, and the same cell may have only one pick-up point, so the probability of selecting the same pick-up point is high, and thus the unit building or the building corresponding to the target address may be extracted as a delivery target position, and the number of times of association between the unit building or the building and the stop point is constructed. The method can construct a target address family in a big data fusion mode, form the association of the target address family and the stop points, and count the association times of the target address family and the stop points.
The walking distance between the delivery destination and the stop point is also an important factor in determining whether the user can pick up goods easily, and the user will usually select the nearest station for walking to pick up goods, but because some uncontrollable factors may exist that the pick-up point is close but the user does not like to use, the number of times of association with the delivery destination is combined to take into consideration. In the case where the initial delivery target position and the stop point have not been associated with a sufficient number of times, it is necessary to select a walking distance as a judgment index.
The length of time required from the departure position to each available stop point is also a factor to be considered, and if special conditions such as construction near the position, etc. which cause the stop points to be difficult to access, the stop points need to be considered to be replaced.
Constructing a dock management platform, and managing each dock, wherein the characteristic information of each dock comprises: whether the target position, the stop times and the stop time consumption average value are available or not. The collected information is from unmanned delivery vehicles and delivery vehicles for delivery personnel, including processing uses of historical data.
After the unmanned delivery vehicle or the delivery vehicle of the delivery person finishes stopping, information is sent to a stop point management platform, the stop point management platform updates the stop point characteristic information to be available, the target position association value is increased by 1, the stop times are increased by 1, and the stop time average value is updated based on the stop time.
When the unmanned delivery vehicle arrives at a stop point but cannot stop, transmitting unavailable information, and updating the characteristic information of the stop point into unavailable information by the stop point management platform, wherein the unavailable frequency is increased by 1; and re-acquiring the evaluation parameters, and selecting the best stopping point from the available stopping points as a new stopping position based on the evaluation parameters. The stop point management platform collects information of each delivery vehicle, is not limited to unmanned delivery vehicles, and can contain information of manual delivery vehicles and early manual delivery historical data. And the information of artificially selecting the stop stations is added and utilized through big data management, so that the intelligence of stop position selection is improved. After the manual delivery vehicle arrives at the stop point, if the manual delivery vehicle cannot stop, the manual delivery vehicle manually sends unavailable information to the stop point management platform, the stop point management platform updates the characteristic information of the stop point to be unavailable, and the unavailable frequency is increased by 1.
After each stop, the stop time is recorded, namely the time spent reaching the vicinity of the stop point to the stop completion. The time spent in stopping can be the time from the start of the delivery vehicle to the completion of stopping, or the time spent in stopping can be the time from the delivery vehicle reaching the position of 2m nearby the stopping point, and the calculation mode of the time spent in stopping can be set according to the requirements.
The method for updating the average value of the parking time consumption comprises the following steps:
wherein the method comprises the steps ofN is the number of times of stopping, t j Time consuming for the stop and worry about>And (5) updating the time-consuming mean value of the stop.
Further, each dock is associated with a label in the map, and whether the label is available.
And acquiring the time length required from the departure position to each available stop point and the walking distance between the delivery target position and the stop point by the map service unit.
Further, the map service unit can monitor the arrival expected arrival time t in the delivery process, if the difference t-t0 between the arrival expected arrival time t and the estimated arrival time t0 at the departure exceeds a set threshold, the evaluation parameter is re-acquired, and the optimal stop point is selected from the available stop points to serve as a new stop position based on the evaluation parameter. And (4) re-planning once abnormal congestion occurs in the delivery process, if the planned record is still the stop point, calculating the delivery route again, and if a route with shorter time is found, updating the route.
(3) And selecting the best stopping point from the available stopping points as the stopping position based on the evaluation parameters.
In one embodiment, selecting the optimal stop point may be calculated by: setting different bias coefficients for each evaluation parameter, and calculating each stop point bias accumulated valueWherein delta i Represents the i-th evaluation parameter, k i Representing the bias factor of the i-th evaluation parameter. The available stop point with the highest bias accumulation value S is selected as the optimal stop point.
In yet another embodiment, selecting the optimal docking point may be performed using a selection module with an artificial neural network model built into the selection module; the artificial neural network model is obtained through training. A BP neural network architecture may be employed.
The artificial neural network model takes the characteristics of each station as characteristic vectors, and each characteristic vector comprises the stop times, the association times with the delivery target position, the stop time consumption average value, the time required for the departure position to each available stop point, and the walking and passing distance between the delivery target position and the stop point. The required characteristic parameters are acquired by the stop point management platform and the map service unit.
Training of the artificial neural network model includes: and taking manually selected historical distribution data as a basis, extracting evaluation parameters to form a large number of samples, and forming a training sample library and a test sample library. After training the artificial neural network model for 50 rounds by selecting samples from the training sample library, selecting test samples from the test sample library for testing, if the precision requirement is met, completing the training, packaging the artificial neural network model, otherwise, continuing the training until the precision requirement is met.
In another aspect, the present invention provides a parking position planning system for an unmanned delivery vehicle, in conjunction with fig. 2, including a search module, an evaluation parameter acquisition module, a selection module, a parking point management platform, and a map service unit.
The searching module presets available searching stop points in a searching range at the delivery target position.
The evaluation parameter acquisition module acquires evaluation parameters, including: the number of stops of each available stop point, the number of times associated with the delivery target position, the average value of stop time consumption, the required length from the departure position to each available stop point, and the walking and passing distance between the delivery target position and the stop point.
The selection module is used for selecting the optimal stop point from the available stop points to serve as the stop position based on the evaluation parameters.
The dock management platform manages each dock, and the characteristic information of each dock comprises: whether the target position, the stop times and the stop time consumption average value are available or not is associated; after the unmanned delivery vehicle or the delivery vehicle of the delivery person finishes stopping, information is sent to a stop point management platform, the stop point management platform updates the stop point characteristic information to be available, the delivery target position is added to the associated target position, the stopping times are increased by 1, and the stopping time average value is updated based on the stopping time.
Further, whether available includes available, unavailable, and number of times unavailable; when the unmanned delivery vehicle arrives at a stop point but cannot stop, transmitting unavailable information, and updating the characteristic information of the stop point into unavailable information by the stop point management platform, wherein the unavailable frequency is increased by 1; and re-acquiring the evaluation parameters, and selecting the best stopping point from the available stopping points as a new stopping position based on the evaluation parameters.
The map service unit is used for acquiring the time length from the departure position to each available stop point and the walking passing distance between the delivery target position and the stop point; in the delivery process, the map service unit monitors the arrival expected time, if the difference between the arrival expected time and the estimated arrival time exceeds a set threshold value, the evaluation parameter acquisition module acquires the evaluation parameter again, and the selection module reselects the optimal stop point as a new stop position.
In summary, the present invention relates to a method and a system for planning a parking position of an unmanned delivery vehicle, wherein a delivery target position presets a search available parking point within a search range; acquiring evaluation parameters, including: the number of stops of each available stop point, the number of times associated with the delivery target position, the average value of stop time consumption, the required length from the starting position to each available stop point, and the walking and passing distance between the delivery target position and the stop point; and selecting the best stopping point from the available stopping points as the stopping position based on the evaluation parameters. The method comprehensively considers route factors, stop point self factors and user use habits, selects the optimal stop point, and ensures user satisfaction and delivery efficiency.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explanation of the principles of the present invention and are in no way limiting of the invention. Accordingly, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present invention should be included in the scope of the present invention. Furthermore, the appended claims are intended to cover all such changes and modifications that fall within the scope and boundary of the appended claims, or equivalents of such scope and boundary.
Claims (7)
1. A method of planning a parking position of an unmanned delivery vehicle, comprising:
searching available stop points in a preset search range of the delivery target position;
acquiring evaluation parameters, including: the number of stops of each available stop point, the number of times associated with the delivery target position, the average value of stop time consumption, the required length from the starting position to each available stop point, and the walking and passing distance between the delivery target position and the stop point;
selecting one of the available dock points from the available dock points as a dock location based on the evaluation parameter;
further comprises: constructing a dock management platform, and managing each dock, wherein the characteristic information of each dock comprises: whether the target position is available, a target position association value, the number of times of stopping and a time-consuming mean value of stopping; after the unmanned delivery vehicle or the delivery vehicle of the delivery person finishes stopping, sending information to a stop point management platform, wherein the stop point management platform can update the characteristic information of the stop point, the target position association value is increased by 1, the stop times are increased by 1, and the stop time average value is updated based on the stop time; the method for updating the average value of the parking time consumption comprises the following steps:
wherein,for the stored average value of the time consumption for parking, n is the number of times of parking,/->Time consuming for the stop and worry about>The updated time-consuming mean value of the stop;
wherein, whether available includes available, unavailable, and number of times of unavailable; when the unmanned delivery vehicle arrives at a stop point but cannot stop, transmitting unavailable information, and updating the characteristic information of the stop point into unavailable information by the stop point management platform, wherein the unavailable frequency is increased by 1; and re-acquiring the evaluation parameters, and selecting the best stopping point from the available stopping points as a new stopping position based on the evaluation parameters.
2. The method for planning a parking position of an unmanned delivery vehicle according to claim 1, wherein a length of time required from a departure position to each available parking point and a walking distance between a delivery target position and the parking point are acquired by a map service unit.
3. The method for planning a parking position of an unmanned delivery vehicle according to claim 2, wherein the expected arrival time of arrival is monitored by a map service unit during delivery, and if the estimated expected arrival time difference from the departure exceeds a set threshold, an evaluation parameter is retrieved, and the optimal parking point is selected from available parking points as a new parking position based on the evaluation parameter.
4. The method of planning a parking position of an unmanned delivery vehicle of claim 1, wherein different bias coefficients are set for each evaluation parameter, each parking point bias accumulation value is calculated, and the available parking point with the highest bias accumulation value is selected as the best parking point.
5. The method for planning a parking position of an unmanned delivery vehicle according to claim 1, wherein a selection module is provided for selecting an optimal parking point from the available parking points as a parking position based on the evaluation parameters; the selection module is internally provided with an artificial neural network model; the artificial neural network model is obtained through training.
6. A system for planning a parking position of an unmanned delivery vehicle, comprising:
the searching module searches available stop points in a preset searching range of the delivery target position;
the evaluation parameter acquisition module acquires evaluation parameters, including: the number of stops of each available stop point, the number of times associated with the delivery target position, the average value of stop time consumption, the required length from the starting position to each available stop point, and the walking and passing distance between the delivery target position and the stop point;
the selection module is used for selecting one of available stop points from the available stop points to serve as a stop position based on the evaluation parameters;
the system also comprises a stop point management platform for managing each stop point, and the characteristic information of each stop point comprises: whether the target position is available, a target position association value, the number of times of stopping and a time-consuming mean value of stopping; after the unmanned delivery vehicle or the delivery vehicle of the delivery person finishes stopping, sending information to a stop point management platform, wherein the stop point management platform can update the characteristic information of the stop point, the target position association value is increased by 1, the stop times are increased by 1, and the stop time average value is updated based on the stop time; the method for updating the average value of the parking time consumption comprises the following steps:
wherein,for the stored average value of the time consumption for parking, n is the number of times of parking,/->Time consuming for the stop and worry about>The updated time-consuming mean value of the stop;
wherein, whether available includes available, unavailable, and number of times of unavailable; when the unmanned delivery vehicle arrives at a stop point but cannot stop, transmitting unavailable information, and updating the characteristic information of the stop point into unavailable information by the stop point management platform, wherein the unavailable frequency is increased by 1; and re-acquiring the evaluation parameters, and selecting the best stopping point from the available stopping points as a new stopping position based on the evaluation parameters.
7. The unmanned delivery vehicle's stop location planning system of claim 6, further comprising a map service unit that obtains a length of time required from the departure location to each available stop point and a walking distance between the delivery target location and the stop point; in the delivery process, the map service unit monitors the arrival expected time, if the difference between the arrival expected time and the estimated arrival time exceeds a set threshold value, the evaluation parameter acquisition module acquires the evaluation parameter again, and the selection module reselects the optimal stop point as a new stop position.
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