Unmanned vehicle scheduling method and system based on Internet of Things

Abstract

The embodiment of the invention provides an unmanned vehicle scheduling method and system based on the Internet of Things, and relates to the technical field of logistics transportation, and the method comprises the steps: obtaining target information based on a target Internet of Things; determining a target unmanned vehicle and a target driving path according to the target information; and controlling the target unmanned vehicle to move towards the target cargo according to the target unmanned vehicle and the target driving path. In this way, the target information can be accurately obtained based on the target Internet of Things; according to the target information, a target unmanned vehicle and a target driving path are accurately and efficiently determined; according to the target unmanned vehicle and the target driving path, the target unmanned vehicle is automatically and accurately controlled to move towards the target cargo, so that the automatic matching precision and matching efficiency of the target unmanned vehicle and the target cargo transportation task are improved, and the transportation efficiency of the target cargo is improved.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of logistics transportation, and particularly to an unmanned vehicle scheduling method and system based on the Internet of Things. Background Art

[0002] An autonomous vehicle (AV), also known as a self-driving car or driverless vehicle, is mainly applicable to autonomous driving operations without manual intervention. Currently, in logistics transportation operations, there are problems such as low intelligence and poor accuracy in the scheduling of autonomous vehicles.

[0003] Therefore, there is an urgent need for a new technical solution to solve the above technical problems. Summary of the Invention

[0004] According to an embodiment of the present application, there is provided an unmanned vehicle scheduling method and system based on the Internet of Things, which can accurately obtain target information based on a target Internet of Things; determine a target unmanned vehicle and a target driving path accurately and efficiently according to the target information; and automatically and accurately control the target unmanned vehicle to move towards the target goods according to the target unmanned vehicle and the target driving path, which is beneficial to improving the automatic matching accuracy and matching efficiency of the target unmanned vehicle and the target goods transportation task, thereby improving the transportation efficiency of the target goods.

[0005] In the first aspect of the present application, there is provided an unmanned vehicle scheduling method based on the Internet of Things, including:

[0006] Obtaining target information based on a target Internet of Things;

[0007] Determining a target unmanned vehicle and a target driving path according to the target information;

[0008] Controlling the target unmanned vehicle to move towards the target goods according to the target unmanned vehicle and the target driving path.

[0009] In some feasible embodiments, before performing the step of obtaining target information based on a target Internet of Things, the method further includes:

[0010] Obtaining first relevant information and second relevant information;

[0011] Constructing a target Internet of Things according to the first relevant information and the second relevant information;

[0012] wherein the first relevant information corresponds to the relevant information of target goods within a first preset range;

[0013] the second relevant information corresponds to the relevant information of unmanned vehicles within a second preset range;

[0014] The first preset range is less than or equal to the second preset range.

[0015] In some possible implementation manners, the above-mentioned target information includes:

[0016] The first point position information, the second point position information, the deadline information for reaching the second point position, and / or the specification information of the target goods;

[0017] The load information, the vehicle condition information, the third point position information corresponding to the corresponding transportation task, and / or the fourth point position information of multiple driverless vehicles within a preset distance from the first point position;

[0018] Among them, the first point position corresponds to the current point position of the target goods;

[0019] The second point position corresponds to the target point position of the target goods;

[0020] The third point position corresponds to the current point position of the transportation task;

[0021] The fourth point position corresponds to the target point position of the transportation task.

[0022] In some possible implementation manners, the above-mentioned determining the target driverless vehicle and the target driving path according to the target information includes:

[0023] When it is determined that there is an intersection between the first driving path and the second driving path, according to the load information, the vehicle condition information, the power consumption information of multiple driverless vehicles performing the transportation task of the target goods, the time window information for multiple driverless vehicles to reach the first point position, and / or the second point position, determine the target driverless vehicle;

[0024] Among them, the first driving path corresponds to the driving path between the first point position and the second point position;

[0025] The second driving path corresponds to the driving path between the third point position and the fourth point position.

[0026] In some possible implementation manners, the above-mentioned determining the target driverless vehicle and the target driving path according to the target information further includes:

[0027] According to the target information, determine multiple driving paths for the target driverless vehicle to perform the transportation task of the target goods;

[0028] Based on the target Internet of Things, determine the traffic signal change characteristics, the congestion degree characteristics, and / or the road condition characteristics of the multiple driving paths;

[0029] According to the traffic signal change characteristics, the congestion degree characteristics, and / or the road condition characteristics, determine the power consumption corresponding to the multiple driving paths, and / or the time of reaching the second point position;

[0030] Determine the target driving path according to the power consumption and / or the time of arrival at the second point.

[0031] In some feasible embodiments, the traffic signal change characteristics, congestion degree characteristics, and / or road condition characteristics for determining multiple driving paths based on the target Internet of Things include:

[0032] Based on the target Internet of Things, obtain the driving characteristics of the driverless vehicles corresponding to multiple driving paths within a preset time;

[0033] Determine the traffic signal change characteristics, congestion degree characteristics, and / or road condition characteristics according to the driving characteristics.

[0034] In some feasible embodiments, the above power consumption is determined according to the following formula:

[0035] P total =(a×v avg 3 +b×v avg 2 +c×v avg +d)×F congestion +E traffic

[0036] Wherein, P total is the power consumption corresponding to the driving path, a is the air resistance coefficient, b is the rolling resistance coefficient, c is the friction loss coefficient, d is the fixed energy consumption when the driverless vehicle is stationary, v avg is the average speed, F congestion is the congestion factor, and E traffic is the energy loss caused by traffic signals.

[0037] In some feasible embodiments, the above time of arrival at the second point is determined according to the following formula:

[0038]

[0039] Wherein, T target is the time of arrival at the second point, D is the corresponding distance of the driving path, v free-flow is the maximum average speed that the driverless vehicle can reach without traffic congestion, F congestion is the congestion factor, F road is the road condition factor, and T wait is the waiting time caused by traffic signals.

[0040] In some feasible embodiments, the above control of the target driverless vehicle to move towards the target goods according to the target driverless vehicle and the target driving path includes:

[0041] Modify the target driving path and / or adjust the speed of the target driverless vehicle according to the duration information, weather information, and / or road condition information corresponding to the remaining road segments, so that the target goods reach the second point before the deadline.

[0042] In the second aspect of the present application, a driverless vehicle scheduling system based on the Internet of Things is provided, including:

[0043] An acquisition unit for acquiring target information based on the target Internet of Things;

[0044] A determination unit for determining a target driverless vehicle and a target driving path according to the target information;

[0045] A control unit for controlling the target driverless vehicle to move towards the target goods according to the target driverless vehicle and the target driving path.

[0046] A driverless vehicle scheduling method and system based on the Internet of Things provided by an embodiment of the present application, wherein the method includes: acquiring target information based on the target Internet of Things; determining a target driverless vehicle and a target driving path according to the target information; controlling the target driverless vehicle to move towards the target goods according to the target driverless vehicle and the target driving path. The present application can accurately acquire target information based on the target Internet of Things; accurately and efficiently determine a target driverless vehicle and a target driving path according to the target information; automatically and accurately control the target driverless vehicle to move towards the target goods according to the target driverless vehicle and the target driving path, which is beneficial to improving the automation matching accuracy and matching efficiency of the target driverless vehicle and the target goods transportation task, thereby improving the transportation efficiency of the target goods.

[0047] It should be understood that the content described in the summary of the invention section is not intended to limit the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Combined with the drawings and referring to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present application will become more obvious. In the drawings, the same or similar reference numerals represent the same or similar elements, where:

[0049] Figure 1 It is a flowchart of a driverless vehicle scheduling method based on the Internet of Things provided by an embodiment of the present application;

[0050] Figure 2 It is a flowchart of another driverless vehicle scheduling method based on the Internet of Things provided by an embodiment of the present application;

[0051] Figure 3A flowchart of another method for scheduling driverless vehicles based on the Internet of Things provided by an embodiment of this application;

[0052] Figure 4 A structural diagram of a driverless vehicle scheduling system based on the Internet of Things provided by an embodiment of this application;

[0053] Figure 5 A structural diagram of an electronic device provided by an embodiment of this application. Detailed implementation manners

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0055] In addition, the term "and / or" in this article is only a relationship describing associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0056] In the first aspect of the embodiments of this application, a method for scheduling driverless vehicles based on the Internet of Things is proposed.

[0057] In some feasible implementation manners, the above method includes: obtaining first relevant information and second relevant information; constructing a target Internet of Things according to the first relevant information and the second relevant information; where the first relevant information corresponds to relevant information of target goods within a first preset range; the second relevant information corresponds to relevant information of driverless vehicles within a second preset range; and the first preset range is less than or equal to the second preset range.

[0058] Exemplarily, the first relevant information may include: geographical coordinate information corresponding to the current locations of all target goods within the first preset range, geographical coordinate information corresponding to the target locations to which all target goods need to be delivered, specification information of all target goods, category information of all target goods, and / or value information corresponding to all target goods, etc.

[0059] Specifically, the above-mentioned geographic coordinate information may include: longitude and latitude coordinates. The above-mentioned specification information may include: size information, mass information, and / or volume information, etc. The above-mentioned category information may include: fragile goods, chemicals, food, etc., to determine the specific transportation requirements of the above-mentioned target goods, and / or specific storage requirements to match the above-mentioned specific transportation requirements, and / or the specific storage requirements can be used for the target driverless vehicle that executes the above-mentioned target goods transportation task.

[0060] Specifically, the above-mentioned value information may include: the market value of the target goods corresponding to the current administrative region.

[0061] Exemplarily, the above-mentioned second relevant information may include: the load information, vehicle condition information, full load rate information, category configuration information, safety information, and / or corresponding transportation task information, etc. of all driverless vehicles within the second preset range.

[0062] Specifically, the above-mentioned load information may include: the total mass information, volume information, distribution information of the goods currently loaded on the driverless vehicle, and / or the category information of the loaded goods. Among them, the category information may include: refrigerated goods information, and / or dangerous goods information, etc.

[0063] Specifically, the above-mentioned vehicle condition information may include: mechanical state information, communication state information, and / or maintenance record information, etc. Among them, the above-mentioned mechanical state information may include: tire wear degree information, battery power information, and / or engine state information. The above-mentioned communication state information may include: signal strength information, connection type information, bandwidth information, packet loss rate information, signal-to-noise ratio information, and / or delay information, etc.

[0064] Specifically, the above-mentioned full load rate information may include: the ratio information of the current load of the driverless vehicle to the maximum load, remaining accommodation space information, and / or remaining load capacity information, etc.

[0065] Specifically, the above-mentioned category configuration information may include: vehicle type, and / or the types of goods that can be supported for transportation, etc. Among them, the above-mentioned vehicle type may include: van, flatbed truck, and / or refrigerated truck, etc. The above-mentioned types of goods that can be supported for transportation may include: fragile goods, chemicals, and / or fresh food, etc.

[0066] Specifically, the above safety information can be determined based on the response time of the emergency braking system of the driverless vehicle, the success rate of obstacle avoidance, and / or historical accident records. Among them, the safety of the driverless vehicle is negatively correlated with the response time of the emergency braking system of the driverless vehicle, that is, the shorter the response time of the emergency braking system of the driverless vehicle, the higher the safety of the driverless vehicle; the safety of the above driverless vehicle is positively correlated with the success rate of obstacle avoidance, that is, the higher the success rate of obstacle avoidance, the higher the safety of the driverless vehicle; the safety of the driverless vehicle is negatively correlated with the number of historical accidents, that is, the more the number of historical accidents, the higher the safety of the driverless vehicle.

[0067] Specifically, the above corresponding transportation task information may include: the starting geographical coordinate information of the currently executed transportation task, the geographical coordinate information corresponding to the current location, the geographical coordinate information corresponding to the target location, the departure time of the currently executed transportation task, and / or the estimated arrival time, etc.

[0068] Among them, the above first preset range can be determined according to the area selected by the target user on the satellite map. Alternatively, the above first preset range can also be determined according to the administrative region input by the target user and / or the set geographical coordinate range. The above second preset range can be determined by itself according to the above first preset range, so that the second preset range is greater than or equal to the above first preset range.

[0069] Thus, the above method can realize the accurate and dynamic construction of the target Internet of Things according to the relevant information of the target goods within the first preset range and the relevant information of the driverless vehicle within the second preset range, so as to improve the acquisition accuracy and acquisition efficiency of the target information, and provide an Internet of Things architecture basis for accurately determining the target driverless vehicle and the target driving path according to the target information.

[0070] It should be noted that the update frequency of the above target Internet of Things can be determined according to the accuracy requirement of the target user for the target information and / or the performance of the device. Among them, the update frequency of the above target Internet of Things is positively correlated with the accuracy requirement of the target user for the target information, that is, the higher the accuracy requirement of the target user for the target information, the faster the update frequency of the target Internet of Things. The update frequency of the above target Internet of Things is positively correlated with the computing upper limit and / or storage space upper limit of the device hardware. That is, the higher the computing upper limit and / or storage space upper limit of the device hardware, the faster the update frequency of the target Internet of Things.

[0071] Exemplarily, Figures 1 - 3 is a schematic flowchart of a driverless vehicle scheduling method 100 based on the Internet of Things provided by an embodiment of the present application. As Figures 1 - 3 shown, the method 100 includes:

[0072] Step S110; Obtain target information based on the target Internet of Things.

[0073] Exemplarily, based on the target Internet of Things, according to a dynamic time window, the above-mentioned target information can be obtained. Among them, the above-mentioned dynamic time window automatically adjusts the window duration to obtain the above-mentioned target information. It should be noted that the above-mentioned dynamic time window duration is negatively correlated with the congestion degree and / or complexity of the road section. That is, the higher the congestion degree and / or complexity of the road section, the shorter the dynamic time window duration.

[0074] Therefore, obtaining the above-mentioned target information based on the dynamic time window is beneficial to automatically control the dynamic window to become shorter in the case of large traffic flow, frequent accidents, and / or complex road conditions on the road section, so as to realize the real-time update and acquisition of target information. In the case of small traffic flow and low road condition complexity on the road section, automatically controlling the dynamic window to extend is beneficial to avoid transmitting and processing target information with high similarity, thereby saving computing power.

[0075] It should be noted that according to the actual needs of the target user, based on the target Internet of Things, the above-mentioned target information can also be obtained according to a fixed time window. Among them, the length of the above-mentioned fixed time window can be defined by the target user according to actual needs.

[0076] In some feasible implementation manners, the above-mentioned target information includes: the first point information, the second point information, the deadline information for reaching the second point, and / or the specification information of the target goods; the load information, vehicle condition information, the third point information corresponding to the corresponding transportation task, and / or the fourth point information of multiple driverless vehicles within a preset distance from the first point; among them, the first point corresponds to the current point of the target goods; the second point corresponds to the target point of the target goods; the third point corresponds to the current point of the transportation task; the fourth point corresponds to the target point of the transportation task.

[0077] Exemplarily, the above-mentioned first point information may include: the geographical coordinate information of the current point of the target goods; the above-mentioned second point information may include: the geographical coordinate information of the target point of the target goods.

[0078] Exemplarily, the above-mentioned deadline information for reaching the second point can be set by the sender and / or the receiver of the target goods. Alternatively, the above-mentioned deadline information for reaching the second point can also be automatically deduced according to the category attribute information of the target goods. For example: when the category attribute of the above-mentioned target goods is fresh fruits, the storage duration of the target goods can be determined according to the above-mentioned category attribute, and the above-mentioned deadline information can be automatically deduced according to the above-mentioned storage duration.

[0079] Exemplarily, the above specification information may include: dimension information, quality information, volume information, and / or category information, etc. The above category information may include: fragile items, chemicals, food, etc., for determining the specific transportation requirements of the above target goods, and / or specific storage requirements to match the above specific transportation requirements, and / or the specific storage requirements can be used for the target driverless vehicle to perform the transportation task of the above target goods.

[0080] Exemplarily, the above load information may include: the total mass information, volume information, distribution information of the currently loaded goods of the driverless vehicle, and / or category information of the loaded goods. Among them, the category information may include: refrigerated goods information, and / or dangerous goods information, etc.

[0081] Exemplarily, the above vehicle condition information may include: mechanical state information, communication state information, and / or maintenance record information, etc. Among them, the above mechanical state information may include: tire wear degree information, battery power information, and / or engine state information. The above communication state information may include: signal strength information, connection type information, bandwidth information, packet loss rate information, signal-to-noise ratio information, and / or latency information, etc.

[0082] Exemplarily, the above third point location information may include: the geographical coordinate information of the current point location of the transportation task executed by the driverless vehicle. The above fourth point location information may include: the geographical coordinate information of the target point location corresponding to the transportation task executed by the driverless vehicle.

[0083] It should be noted that, based on the target Internet of Things, according to the dynamic time window, and / or fixed time window, the first point location information, second point location information, deadline information for arriving at the second point location, and / or specification information of the above target goods, the load information, vehicle condition information, third point location information of the corresponding transportation task, and / or fourth point location information of multiple driverless vehicles within a preset distance from the first point location can be obtained. Among them, the above preset distance can be determined according to the transportation urgency requirement of the target goods, and the above preset distance is negatively correlated with the transportation urgency requirement of the target goods, that is, the higher the transportation urgency requirement of the target goods, the smaller the preset distance.

[0084] Thus, the above method can accurately determine the target driverless vehicle and the target driving path according to the first point position information, the second point position information, the deadline information for reaching the second point position, and / or the specification information of the target goods; the load information, the vehicle condition information, the third point position information corresponding to the transportation task, and / or the fourth point position information of multiple driverless vehicles within a preset distance from the first point position, providing accurate data support for accurately controlling the target driverless vehicle to move towards the target goods according to the target driverless vehicle and the target driving path, thereby improving the automation matching accuracy and matching efficiency of the target driverless vehicle and the target goods transportation task, and further improving the transportation efficiency of the target goods.

[0085] Step S120; determine the target driverless vehicle and the target driving path according to the target information.

[0086] Exemplarily, the target driverless vehicle and the target driving path can be determined according to the current point position information of the target goods, the target point position information of the target goods, the deadline information for the target goods to reach the target point position, the specification information, the load information of the driverless vehicle within a preset distance from the first point position, the vehicle condition information, the current point position information corresponding to the transportation task of the driverless vehicle, and / or the target point position information corresponding to the transportation task of the driverless vehicle.

[0087] It should be noted that when multiple target driverless vehicles are determined according to the above target information, the best target driverless vehicle can be determined according to the vehicle condition information as the final target driverless vehicle to complete the transportation task of the above target goods.

[0088] Exemplarily, the mechanical state scores of multiple target driverless vehicles can be determined according to the tire wear degree information and corresponding weights, the battery power information and corresponding weights, and / or the engine state information and corresponding weights of the multiple target driverless vehicles. The communication state scores of multiple target driverless vehicles can be determined according to the signal strength information and corresponding weights, the connection type information and corresponding weights, the bandwidth information and corresponding weights, the packet loss rate information and corresponding weights, the signal-to-noise ratio information and corresponding weights, and / or the delay information and corresponding weights of the multiple target driverless vehicles. The final scores corresponding to the multiple target driverless vehicles are determined according to the mechanical state scores and corresponding weights, and the communication state scores and corresponding weights. The target driverless vehicle with the highest final score is selected as the final target driverless vehicle to complete the transportation task of the above target goods.

[0089] Specifically, the above mechanical state score can be determined according to the following formula:

[0090] Mechanical state score = tire wear degree score × ω 1 + battery power score × ω2 + Engine status score × ω 3 (1)

[0091] Where ω 1 is the weight corresponding to the tire wear degree score, ω 2 is the weight corresponding to the battery power score, ω 3 is the weight corresponding to the engine status score. The sum of the weights ω 1 corresponding to the tire wear degree score, the weights ω 2 corresponding to the battery power score, and the weights ω 3 corresponding to the engine status score is 1. The tire wear degree score is negatively correlated with the tire wear degree, that is, the higher the tire wear degree, the smaller the tire wear degree score; the battery power score is positively correlated with the remaining battery power, that is, the higher the remaining battery power, the higher the battery power score; the engine status score is negatively correlated with the cumulative operation duration of the engine, that is, the shorter the cumulative operation duration of the engine, the higher the engine status score.

[0092] It should be noted that the magnitude relationship among the weights ω 1 corresponding to the tire wear degree score, the weights ω 2 corresponding to the battery power score, and the weights ω 3 corresponding to the engine status score can be determined according to the actual situation.

[0093] Exemplarily, when the transportation distance of the target goods is greater than the preset distance threshold, the weight ω 1 corresponding to the tire wear degree score is less than or equal to the weight ω 3 corresponding to the engine status score, and the weight ω 3 corresponding to the engine status score is less than or equal to the weight ω 2 corresponding to the battery power score. Wherein, the preset distance threshold is used to represent the distance corresponding to the long-distance transportation task of the target goods.

[0094] Exemplarily, when the transportation distance of the target goods is less than or equal to the preset distance threshold, the weight ω 1 corresponding to the tire wear degree score is less than or equal to the weight ω 2 corresponding to the battery power score, and the weight ω 2 corresponding to the battery power score is less than or equal to the weight ω 3 corresponding to the engine status score, so as to improve the safety of the transportation task of the target goods.

[0095] Specifically, the communication status score can be determined according to the following formula:

[0096] Communication status score = Signal strength score × ω4 + Connection type score × ω 5 + Bandwidth score × ω 6 + Packet loss rate score × ω 7 + Signal-to-noise ratio score × ω 8 + Latency score × ω 9 (2)

[0097] Where ω 4 is the weight corresponding to the signal strength score, ω 5 is the weight corresponding to the connection type score, ω 6 is the weight corresponding to the bandwidth score, ω 7 is the weight corresponding to the packet loss rate score, ω 8 is the weight corresponding to the signal-to-noise ratio score, ω 9 is the weight corresponding to the latency score. The sum of the above weights ω 4 corresponding to the signal strength score, ω 5 corresponding to the connection type score, ω 6 corresponding to the bandwidth score, ω 7 corresponding to the packet loss rate score, ω 8 corresponding to the signal-to-noise ratio score, ω 9 corresponding to the latency score is 1. The above signal strength score is positively correlated with the signal strength, that is, the stronger the signal strength, the higher the above signal strength score; the above connection type score is positively correlated with the coverage range corresponding to the connection type, that is, the higher the coverage range corresponding to the connection type, the higher the above connection type score; the above bandwidth score is positively correlated with the bandwidth, that is, the larger the bandwidth, the higher the bandwidth score; the above packet loss rate score is negatively correlated with the packet loss rate, that is, the higher the packet loss rate, the lower the packet loss rate score; the above signal-to-noise ratio score is positively correlated with the signal-to-noise ratio, that is, the higher the signal-to-noise ratio, the higher the signal-to-noise ratio score; the above latency score is negatively correlated with the latency duration, that is, the longer the latency duration, the lower the above latency score.

[0098] It should be noted that the magnitude relationship among the above weights ω 4 corresponding to the signal strength score, ω 5 corresponding to the connection type score, ω 6 corresponding to the bandwidth score, ω 7 corresponding to the packet loss rate score, ω 8 corresponding to the signal-to-noise ratio score, ω 9 corresponding to the latency score can be determined according to the actual situation.

[0099] Exemplarily, in a conventional scenario, the weight ω 4 corresponding to the signal strength score can be set to 0.15, and the weight ω 5 corresponding to the connection type score can be set to 0.15, and the weight ω 6It can be set to 0.20, and the weight ω corresponds to the packet loss rate score 7 It can be set to 0.15, and the weight ω corresponds to the signal-to-noise ratio score 8 It can be set to 0.20, and the weight ω corresponds to the latency score 9 It can be set to 0.15.

[0100] Exemplarily, in the case where the transportation task of the target goods is urgent, and / or, the requirements for the real-time performance and reliability of data transmission are high, then the above-mentioned weight ω corresponding to the signal strength score 4 It can be set to 0.10, and the weight ω corresponds to the connection type score 5 It can be set to 0.15, and the weight ω corresponds to the bandwidth score 6 It can be set to 0.15, and the weight ω corresponds to the packet loss rate score 7 It can be set to 0.20, and the weight ω corresponds to the signal-to-noise ratio score 8 It can be set to 0.20, and the weight ω corresponds to the latency score 9 It can be set to 0.20.

[0101] Exemplarily, in the case where the data transmission volume is large, then the above-mentioned weight ω corresponding to the signal strength score 4 It can be set to 0.10, and the weight ω corresponds to the connection type score 5 It can be set to 0.10, and the weight ω corresponds to the bandwidth score 6 It can be set to 0.30, and the weight ω corresponds to the packet loss rate score 7 It can be set to 0.15, and the weight ω corresponds to the signal-to-noise ratio score 8 It can be set to 0.15, and the weight ω corresponds to the latency score 9 It can be set to 0.20.

[0102] Exemplarily, in the case where the wide-area coverage range of data transmission is large, the above-mentioned second preset range is large, and / or, the transportation distance of the above-mentioned target goods is far, then the above-mentioned weight ω corresponding to the signal strength score 4 It can be set to 0.10, and the weight ω corresponds to the connection type score 5 It can be set to 0.25, and the weight ω corresponds to the bandwidth score 6 It can be set to 0.15, and the weight ω corresponds to the packet loss rate score 7 It can be set to 0.20, and the weight ω corresponds to the signal-to-noise ratio score 8 It can be set to 0.15, and the weight ω corresponds to the latency score 9 It can be set to 0.15.

[0103] Specifically, the above-mentioned final score can be determined according to the following formula:

[0104] Final score = Mechanical state score × ω10 + Communication status score × ω 11 (3)

[0105] Wherein, ω 10 is the weight corresponding to the mechanical status score, and ω 11 is the weight corresponding to the communication status score. The sum of the weight ω 10 corresponding to the above mechanical status score and the weight ω 11 corresponding to the above communication status score is 1. It should be noted that the weight ω 10 corresponding to the above mechanical status score can be set to be greater than or equal to the weight ω 11 .

[0106] It should be noted that in the case where multiple target driving paths corresponding to the target unmanned vehicles are determined according to the above target information, the final target driving path is determined according to the road condition information of the target driving path and / or the weather information corresponding to the target driving path. Wherein, the road condition information of the above target driving path and / or the weather information can be determined by retrieving the target data collected by multiple target unmanned vehicles passing through the corresponding section of the above target driving path within a preset time period. Among them, the above target data can include: target video data and / or target image data. The preset time period is negatively correlated with the accuracy requirements of the road condition information and / or the weather information, that is, the higher the accuracy requirements of the road condition information and / or the weather information, the shorter the preset time period. The road condition information of the above target driving path and / or the weather information can also be determined by retrieving satellite data. Among them, the above road condition information can include: road condition information, such as: road surface wetness condition information, pothole condition information, water accumulation or icing condition information, etc.; traffic flow information, and / or obstacle information, etc. The above weather information can include: sunny, rain and snow, cloudy, visibility, temperature gradient, and / or humidity gradient, etc.

[0107] Exemplarily, the score of the target driving path can be determined according to the road condition information and the corresponding weight, the weather information and the corresponding weight, and the target driving path with the highest score is selected as the final target driving path to control the above target unmanned vehicle to perform the transportation task of the target goods according to the above target driving path.

[0108] Wherein, the score of the above target driving path can be determined according to the following formula:

[0109] Target driving path score = Road condition score × ω 12 + Weather score × ω 13 (4)

[0110] Wherein, ω 12 is the weight corresponding to the road condition score, and ω 13is the weight corresponding to the weather score. The weight ω corresponding to the above road condition score 12 and the weight ω corresponding to the above weather score 13 sum to 1. It should be noted that among them, the weight ω corresponding to the road condition score 12 can be set to be greater than or equal to the weight ω corresponding to the above weather score 13 .

[0111] It should be noted that the above road condition score is negatively correlated with the road surface slipperiness, pothole degree, water accumulation degree, icing degree, traffic flow, and / or the number of obstacles. That is, the higher the road surface slipperiness, pothole degree, water accumulation degree, icing degree, traffic flow, and / or the more the number of obstacles, the lower the road condition score. The above weather score is negatively correlated with the severity of the weather. That is, the higher the severity of the weather, the lower the weather score.

[0112] In some feasible embodiments, the above step S120; determining the target driverless vehicle and the target driving path according to the target information includes:

[0113] Step S121; when it is determined that there is an intersection between the first driving path and the second driving path, determine the target driverless vehicle according to the load information, vehicle condition information, power consumption information of multiple driverless vehicles performing the transportation task of the target goods, the time window information of multiple driverless vehicles arriving at the first point, and / or the second point; wherein, the first driving path corresponds to the driving path between the first point and the second point; the second driving path corresponds to the driving path between the third point and the fourth point.

[0114] Exemplarily, when it is determined that there is an intersection between the transportation path of the target goods and the transportation paths corresponding to the transportation tasks performed by multiple driverless vehicles, that is, when there is a section of the road that the transportation path of the target goods and the transportation paths corresponding to the transportation tasks performed by multiple driverless vehicles pass through in common, determine the target driverless vehicle according to the load information, vehicle condition information, power consumption information of multiple driverless vehicles performing the transportation task of the target goods, the time window information of multiple driverless vehicles arriving at the first point, and / or the second point.

[0115] Among them, the above load information includes: the total mass information, volume information, distribution information of the currently loaded goods of the driverless vehicle, and / or the category information of the loaded goods. Among them, the category information may include: refrigerated goods information, and / or dangerous goods information, etc.

[0116] Specifically, based on the above load information, the matching degree between the remaining loading space of multiple driverless vehicles and the volume of the above target goods, and the matching degree between the remaining loadable goods mass of the above multiple driverless vehicles and the mass of the above target goods can be determined.

[0117] Specifically, based on the time window information of the above multiple driverless vehicles arriving at the first point and the second point, the total duration for the above multiple driverless vehicles to execute the transportation task of the above target goods can be determined.

[0118] Specifically, based on the matching degree between the remaining loading space of the above multiple driverless vehicles and the volume of the above target goods, the matching degree between the remaining loadable goods mass of the above multiple driverless vehicles and the mass of the above target goods, the final score of the above multiple driverless vehicles determined based on formula (3), the maximum power consumption for the multiple driverless vehicles to execute the transportation task of the target goods, and / or the total duration for the multiple driverless vehicles to execute the transportation task of the above target goods, the above target driverless vehicle can be determined.

[0119] Specifically, based on the matching degree and corresponding weight between the remaining loading space of the above multiple driverless vehicles and the volume of the above target goods, the matching degree and corresponding weight between the remaining loadable goods mass of the above multiple driverless vehicles and the mass of the above target goods; the above final score and corresponding weight; the above maximum power consumption and corresponding weight, and / or the above total duration and corresponding weight; the comprehensive score of the target driverless vehicle can be determined; the driverless vehicle with the highest comprehensive score is selected as the above target driverless vehicle.

[0120] It should be noted that among them, the sum of the corresponding weights of the matching degree between the remaining loading space of the above multiple driverless vehicles and the volume of the above target goods, the corresponding weights of the matching degree between the remaining loadable goods mass of the above multiple driverless vehicles and the mass of the above target goods, the corresponding weights of the above final score, the corresponding weights of the above maximum power consumption, and the corresponding weights of the above total duration is 1, and the weights can be determined according to the value of the target goods and the urgency of the delivery requirement of the target goods in the actual situation. For example: in the case where the target goods are high-value and urgently needed electronic products, the corresponding weight of the matching degree between the remaining loading space of the above multiple driverless vehicles and the volume of the above target goods can be set to 0.25, the corresponding weight of the matching degree between the remaining loadable goods mass of the above multiple driverless vehicles and the mass of the above target goods can be set to 0.25, the corresponding weight of the above final score can be set to 0.2, the corresponding weight of the above maximum power consumption can be set to 0.15, and the corresponding weight of the above total duration can be set to 0.15.

[0121] It should be noted that the comprehensive score of multiple driverless vehicles can be determined based on the matching degree between the remaining loading space of the vehicle and the volume of the above-mentioned target goods, the matching degree between the remaining loadable goods mass of the vehicle and the mass of the above-mentioned target goods, the above-mentioned final score, the above-mentioned maximum power consumption, and / or the above-mentioned total duration. The driverless vehicle with the highest comprehensive score is selected as the above-mentioned target driverless vehicle, that is, the driverless vehicle with a higher matching degree between the remaining loading space and the volume of the above-mentioned target goods, a higher matching degree between the remaining loadable goods mass of the vehicle and the mass of the above-mentioned target goods, a higher final score, a lower maximum power consumption, and a shorter total duration is selected as the above-mentioned target driverless vehicle.

[0122] Thus, the above method can be implemented to select a driverless vehicle with a higher matching degree between the remaining loading space and the volume of the above-mentioned target goods, a higher matching degree between the remaining loadable goods mass of the vehicle and the mass of the above-mentioned target goods, better mechanical and communication states, lower power consumption for performing the transportation task of the above-mentioned target goods, and shorter time consumption for performing the transportation task of the above-mentioned target goods as the target driverless vehicle when it is determined that there is an intersection between the transportation path corresponding to the transportation task performed by multiple driverless vehicles and the transportation path of the above-mentioned target goods. This can avoid the waste of load space during the transportation task of the target driverless vehicle for the target goods, avoid the waste of the total load space during the process of multiple driverless vehicles networking to perform all transportation tasks of the target goods in the target Internet of Things, improve the safety of the selected target driverless vehicle, thereby improving the safety of the target goods transportation operation, being beneficial to reducing the power consumption during the process of the target driverless vehicle performing the transportation task of the above-mentioned target goods, and further avoiding the waste of energy during the transportation operation of the above-mentioned target goods, being beneficial to shortening the transportation duration of the above-mentioned target goods, and thereby enhancing the satisfaction and experience of the recipient and / or sender of the above-mentioned target goods.

[0123] In some feasible implementation manners, the above step S120; determining the target driverless vehicle and the target driving path according to the target information further includes: step S122; determining multiple driving paths for the target driverless vehicle to perform the transportation task of the target goods according to the target information.

[0124] Exemplarily, after determining the target driverless vehicle based on step S121, multiple driving paths with scores greater than a preset threshold can be selected according to the above formula (4) to implement determining multiple driving paths for the target driverless vehicle to perform the transportation task of the target goods according to the target information. Among them, the above-mentioned preset threshold can be set by itself according to the actual demand scenario.

[0125] Step S123; based on the target Internet of Things, determining the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of the multiple driving paths.

[0126] In some feasible embodiments, the above step S123; based on the target Internet of Things, determining the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of multiple driving paths includes:

[0127] Step S123-a; based on the target Internet of Things, obtaining the driving characteristics of driverless vehicles corresponding to multiple driving paths within a preset time.

[0128] Exemplarily, the above driving characteristics may include: the driving speed characteristics of the vehicle, the vehicle driving acceleration characteristics, the vehicle lane change frequency characteristics, the vehicle spacing characteristics, the vehicle driving bumpiness characteristics, and / or the waiting duration characteristics of the vehicle at the corresponding position of the traffic signal, etc. Among them, the above preset time can be set according to the accuracy requirement of the target user for the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of the target path. Among them, the above preset time is negatively correlated with the accuracy requirement of the target user for the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of the target path. That is, the higher the accuracy requirement of the target user for the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of the target path, the shorter the preset time.

[0129] It should be noted that the above driving characteristics can be uploaded to the above target Internet of Things after the relevant data in the driving recorder is preprocessed locally by the driverless vehicle. Among them, the above preprocessing operations include: data cleaning, data compression, feature extraction, and / or data standardization, etc., to reduce the load of the cloud server, improve the data transmission speed and transmission efficiency, and reduce the data delay.

[0130] Step S123-b; determining the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics according to the driving characteristics.

[0131] Exemplarily, the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics can be determined according to the above driving speed characteristics of the vehicle, the vehicle driving acceleration characteristics, the vehicle lane change frequency characteristics, the vehicle spacing characteristics, the vehicle driving bumpiness characteristics, and / or the waiting duration characteristics of the vehicle at the corresponding position of the traffic signal, etc.

[0132] Specifically, the above traffic signal change characteristics can be determined according to the waiting duration characteristics of vehicles greater than or equal to a preset number at the corresponding position of the traffic signal within a preset time. Among them, the above traffic signal change characteristics may include: the change frequency characteristics corresponding to red lights, green lights, and yellow lights, and / or the duration characteristics, etc. The above preset number is positively correlated with the accuracy requirement of the target user for the determination of traffic signal change characteristics. That is, the higher the accuracy requirement of the target user for the determination of traffic signal change characteristics, the larger the preset number.

[0133] Specifically, the above congestion degree feature can be determined according to the driving speed feature, vehicle driving acceleration feature, vehicle lane change frequency feature, and / or vehicle spacing feature of the above vehicles that are greater than or equal to a preset quantity within a preset time.

[0134] Specifically, the above driving speed feature may include: the average speed of the vehicle, the standard deviation of the vehicle speed, and / or the coefficient of variation of the vehicle speed. Among them, the congestion degree is negatively correlated with the average speed of the vehicle, that is, the higher the average speed of the vehicle, the lower the congestion degree. The congestion degree is negatively correlated with the standard deviation of the vehicle speed, and / or the coefficient of variation, that is, the larger the standard deviation of the vehicle speed, and / or the coefficient of variation, the higher the congestion degree.

[0135] Specifically, the above driving acceleration feature may include: the standard deviation of the vehicle acceleration, and / or the coefficient of variation of the vehicle acceleration. Among them, the congestion degree is negatively correlated with the standard deviation of the vehicle acceleration, and / or the coefficient of variation, that is, the larger the standard deviation of the vehicle acceleration, and / or the coefficient of variation, the higher the congestion degree.

[0136] Specifically, the above congestion degree is positively correlated with the vehicle lane change frequency, that is, the higher the vehicle lane change frequency, the higher the congestion degree. Among them, the above vehicle lane change frequency can be determined according to the lane change frequency of the vehicles that are greater than or equal to a preset quantity within a preset time. It should be noted that the above preset quantity is positively correlated with the target user's determination accuracy requirement for the lane change frequency feature, that is, the higher the target user's determination accuracy requirement for the lane change frequency feature, the larger the above preset quantity.

[0137] Specifically, the above congestion degree is negatively correlated with the vehicle spacing, that is, the larger the vehicle spacing, the lower the congestion degree. Among them, the above vehicle spacing can be determined according to the spacing between the vehicles that are greater than or equal to a preset quantity within a preset time. The above preset quantity is positively correlated with the target user's determination accuracy requirement for the vehicle spacing feature, that is, the higher the target user's determination accuracy requirement for the vehicle spacing feature, the larger the above preset quantity.

[0138] Specifically, the above vehicle driving bumpiness characteristics may include: vehicle vibration acceleration characteristics, vehicle vibration frequency characteristics, vehicle vibration duration characteristics, and / or vehicle vibration intensity characteristics. The above road condition characteristics may include: good road conditions, general road conditions, poor road conditions, and / or extremely poor road conditions. Among them, good road conditions correspond to relatively stable vehicle driving, light vibration intensity, low frequency, and relatively smooth road surface. General road conditions correspond to small undulations or cracks on the road surface, mild vehicle bumpiness, and moderate vibration frequency and intensity. Poor road conditions correspond to obvious potholes, cracks or other large defects on the road surface, frequent medium or severe vehicle bumpiness, high vibration frequency, and large intensity. Extremely poor road conditions correspond to serious road surface damage, the vehicle cannot drive, extremely strong vibration, and extremely long vibration duration.

[0139] Thus, the above method can accurately obtain the driving characteristics of the driverless vehicle corresponding to multiple driving paths within a preset time based on the target Internet of Things; according to the above driving characteristics, accurately determine the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics, providing a data basis for selecting the target driving path corresponding to the target driverless vehicle, thereby improving the determination accuracy and efficiency of the target driving path.

[0140] Step S124; determine the power consumption corresponding to multiple driving paths and / or the time to reach the second point according to the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics.

[0141] Exemplarily, in some feasible embodiments, the above power consumption is determined according to the following formula:

[0142] P total =(a×v avg 3 +b×v avg 2 +c×v avg +d)×F congestion +E traffic (5)

[0143] Wherein, P total is the power consumption corresponding to the driving path, a is the air resistance coefficient, b is the rolling resistance coefficient, c is the friction loss coefficient, d is the fixed energy consumption when the driverless vehicle is stationary, v avg is the average speed, F congestion is the congestion factor, and E traffic is the energy loss caused by traffic signals.

[0144] It should be noted that the above air resistance coefficient a, rolling resistance coefficient b, friction loss coefficient c, fixed energy consumption d when the driverless vehicle is stationary, average speed v avg , congestion factor F congestionand the energy loss E caused by traffic signals traffic can be determined according to the vehicle's driving speed characteristics, vehicle driving acceleration characteristics, vehicle lane change frequency characteristics, vehicle spacing characteristics, vehicle driving bumpiness characteristics, traffic signal change characteristics, congestion characteristics, and / or road condition characteristics.

[0145] Thus, the above method can be implemented based on the above formula (5), according to the air resistance coefficient a, rolling resistance coefficient b, friction loss coefficient c, fixed energy consumption d when the driverless vehicle is stationary, average speed v avg , congestion factor F congestion and the energy loss E caused by traffic signals traffic , accurately determine the power consumption P corresponding to the above multiple driving paths total , so as to accurately select the driving path with smaller power consumption as the target driving path according to the above power consumption, so as to reduce the energy consumption generated by the target driverless vehicle when performing the transportation task of the target goods, thereby reducing the energy consumption loss and saving the cost of the target driverless vehicle when performing the transportation task of the target goods.

[0146] In some feasible implementation manners, the above-mentioned time to reach the second point is determined according to the following formula:

[0147]

[0148] wherein, T target is the time to reach the second point, D is the corresponding distance of the driving path, v free-flow is the maximum average speed that the driverless vehicle can reach without traffic congestion, F congestion is the congestion factor, F road is the road condition factor, T wait is the waiting time due to traffic signals.

[0149] It should be noted that the corresponding distance D of the driving path can be determined according to the above-mentioned second point and the first point, wherein the corresponding distance D of the driving path is the actual distance of the driverless vehicle corresponding to the driving path when performing the transportation task of the target goods. The maximum average speed v that the driverless vehicle can reach without traffic congestion free-flow can be determined according to the above-mentioned congestion characteristics, vehicle driving speed characteristics, and / or vehicle driving acceleration characteristics. The above-mentioned congestion factor F congestion can be determined according to the congestion characteristics, the above-mentioned road condition factor F road can be determined according to the road condition characteristics, and the above-mentioned waiting time T due to traffic signals wait can be determined according to the traffic signal change characteristics.

[0150] Thus, based on the above formula (6), the above method can achieve the determination of the time when the target driverless vehicle arrives at the second point based on the corresponding distance D of the driving route, the maximum average speed v that the driverless vehicle can reach without traffic congestion free-flow , the above congestion factor F congestion , the above road condition factor F road , and the above waiting time T caused by traffic signals wait , accurately determine the time when the target driverless vehicle arrives at the second point based on the above multiple driving routes, and thus based on the above arrival time T at the second point target accurately select the driving route with a shorter time consumption as the target driving route to complete the transportation task of the target goods as soon as possible, reduce the time consumption of the transportation task, so that the recipient of the target goods can receive the target goods as soon as possible, and further improve the user experience of the sender of the target goods and / or the recipient of the target goods.

[0151] Step S125: Determine the target driving route according to the power consumption and / or the time of arrival at the second point.

[0152] It should be noted that, according to the actual situation requirements, the weight corresponding to the above power consumption and / or the weight corresponding to the above arrival time at the second point can be determined. The power consumption score is determined according to the weight corresponding to the above power consumption and the above formula (5), the arrival time score at the second point is determined according to the weight corresponding to the above arrival time at the second point and the above formula (6), and the final scores of multiple driving routes are determined according to the above power consumption score and / or the arrival time score at the second point, so as to select the driving route with the highest final score as the target route.

[0153] Thus, the above method can achieve the accurate determination of multiple driving routes for the target driverless vehicle to perform the transportation task of the target goods according to the target information; accurately determine the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of multiple driving routes based on the target Internet of Things; accurately determine the power consumption and / or the time of arrival at the second point corresponding to multiple driving routes according to the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics; accurately select the route with a smaller power consumption and / or an earlier arrival time at the second point as the target driving route according to the power consumption and / or the time of arrival at the second point, which is beneficial to reducing the energy consumption generated by the target driverless vehicle when performing the transportation task of the target goods, reducing the energy consumption loss, saving the cost of the target driverless vehicle when performing the transportation task of the target goods, and / or completing the transportation task of the target goods as soon as possible, reducing the time consumption of the transportation task, so that the recipient of the target goods can receive the target goods as soon as possible, and further improving the user experience of the sender of the target goods and / or the recipient of the target goods.

[0154] Step S130: Control the target driverless vehicle to move towards the target goods according to the target driverless vehicle and the target driving path.

[0155] Exemplarily, after determining the target driverless vehicle and the target driving path corresponding to the target driverless vehicle based on Steps S121 - S125, the target driverless vehicle can be controlled to move towards the current position of the target goods, i.e., the first position above, according to the above target driving path, obtain the target goods, and then control the target driverless vehicle to continue moving towards the target position of the target goods, i.e., the second position above, according to the above target driving path, so as to complete the transportation task of the above target goods.

[0156] In some feasible embodiments, the above Step S130: Control the target driverless vehicle to move towards the target goods according to the target driverless vehicle and the target driving path includes:

[0157] Step S131: Modify the target driving path and / or adjust the speed of the target driverless vehicle according to the duration information, weather information, and / or road condition information corresponding to the remaining section, so that the target goods reach the second position before the deadline.

[0158] Exemplarily, the duration information corresponding to the remaining section can be determined according to the difference between the current clock information and the deadline information.

[0159] Exemplarily, when it is determined according to the above remaining travel time information that the duration corresponding to the remaining section is less than the preset duration, it is determined that there is a risk of delay in the target goods transportation task. Modify the target driving path to increase the proportion of highway sections and / or main trunk sections in the target driving path, reduce the proportion of sections with high congestion, and / or the proportion of sections with poor road conditions, so that the target goods reach the second position before the deadline. And / or, when it is determined according to the above remaining travel time information that the time corresponding to the remaining section is less than the preset time, automatically control the speed of the target driverless vehicle to increase, so that the target goods reach the second position before the deadline. Among them, the above preset duration corresponds to the maximum duration required to travel from the current node to the second position based on the average driving speed of a preset number of vehicles traveling on the remaining travel section. Among them, the above average driving speed can be determined based on the target Internet of Things, and the above preset number is positively correlated with the target user's determination accuracy requirement for the preset duration, that is, the higher the determination accuracy requirement for the preset duration, the larger the preset number.

[0160] Exemplarily, based on the target Internet of Things, the target vehicle currently traveling on the remaining section can perform target acquisition and preprocessing operations on the weather information and / or road condition information corresponding to the remaining section, and transmit back the weather information and / or road condition information after the preprocessing operation. In the case where it is determined according to the above weather information that the severity of the weather corresponding to the remaining section is greater than the preset severity, and / or the congestion degree of the road condition is greater than the preset degree, the target driving route is corrected to select a section where the severity of the weather is less than or equal to the preset severity, and / or the congestion degree is less than or equal to the preset congestion degree as the target driving route, so that the target goods can reach the second point before the deadline. And / or, in the case where it is determined according to the above weather information that the severity of the weather corresponding to the remaining section is greater than the preset severity, and / or the congestion degree of the road condition is greater than the preset degree, the speed of the target driverless vehicle is automatically controlled to decrease, so as to improve the safety of the target driverless vehicle traveling on the remaining section. Among them, the above severity can be determined based on the maximum wind speed, the maximum precipitation per hour, the minimum visibility, and / or the maximum road surface slipperiness index collected by the target vehicle currently traveling on the remaining section based on the target Internet of Things. The above preset severity can be determined based on the weather severity corresponding to the maximum average driving speed of the target driverless vehicle traveling on the remaining section corresponding to the duration of the remaining section. The above congestion degree can be determined based on the maximum number of passing vehicles per lane per minute and / or the minimum average vehicle speed collected by the target vehicle currently traveling on the remaining section based on the target Internet of Things. The above preset congestion degree can correspond to the duration of the remaining section and determine the congestion degree corresponding to the maximum average driving speed of the target driverless vehicle traveling on the remaining section.

[0161] Thus, the above method can accurately perform a correction operation on the target driving route and / or accurately perform a regulation operation on the driving speed of the target driverless vehicle according to the duration information corresponding to the remaining section, the weather information corresponding to the remaining section, and / or the road condition information, so that the target goods can reach the second point before the deadline, which is beneficial to further improving the execution accuracy of the target goods transportation task and enabling the target goods to arrive at the target point on time, thereby further enhancing the user experience of the sender of the target goods and / or the recipient of the target goods.

[0162] Based on this, the method for scheduling driverless vehicles based on the Internet of Things provided by this application includes: obtaining target information based on the target Internet of Things; determining the target driverless vehicle and the target driving path according to the target information; and controlling the target driverless vehicle to move towards the target goods according to the target driverless vehicle and the target driving path. In this way, this application can accurately obtain target information based on the target Internet of Things; accurately and efficiently determine the target driverless vehicle and the target driving path according to the target information; and automatically and accurately control the target driverless vehicle to move towards the target goods according to the target driverless vehicle and the target driving path, which is beneficial to improving the automatic matching accuracy and matching efficiency of the target driverless vehicle and the target goods transportation task, thereby improving the transportation efficiency of the target goods.

[0163] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0164] The above is the introduction of the method embodiments. The following further illustrates the solution of this application through device embodiments.

[0165] In the second aspect of the embodiments of this application, a driverless vehicle scheduling system based on the Internet of Things is proposed. Figure 4 It is a structural schematic diagram of a driverless vehicle scheduling system 200 provided by the embodiments of this application. As Figure 4 shown, the system 200 includes: an acquisition unit 210, a determination unit 220, and a control unit 230.

[0166] The acquisition unit 210 is used to obtain target information based on the target Internet of Things.

[0167] The determination unit 220 is used to determine the target driverless vehicle and the target driving path according to the target information.

[0168] The control unit 230 is used to control the target driverless vehicle to move towards the target goods according to the target driverless vehicle and the target driving path.

[0169] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the described method can refer to the corresponding process in the foregoing system embodiments, and will not be elaborated here.

[0170] Figure 5The following is a schematic structural diagram of an electronic device 300 provided by an embodiment of the present application. As Figure 5 shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the terminal device or server are also stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0171] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0172] Specifically, according to an embodiment of the present application, the above method flow steps can be implemented as a computer software program. For example: an embodiment of the present application includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the above functions defined in the system of the present application are executed.

[0173] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. And in this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination of the above.

[0174] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0175] The units or modules involved in the embodiments described in this application can be implemented in software or in hardware. The described units or modules can also be provided in a processor. Among them, the names of these units or modules do not, in some cases, constitute a limitation on the units or modules themselves.

[0176] The above description is only a preferred embodiment of this application and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the application involved in this application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions applied in this application.

Claims

1. A method for dispatching unmanned vehicles based on the Internet of Things, characterized in that: include: Based on the target Internet of Things, obtain target information; Determining a target unmanned vehicle and a target driving path according to the target information; According to the target unmanned vehicle and the target driving path, the target unmanned vehicle is controlled to move toward the target cargo.

2. The method for dispatching unmanned vehicles based on the Internet of Things according to claim 1, characterized in that: Before executing the step of acquiring target information based on the target Internet of Things, the method further includes: Acquire first relevant information and second relevant information; Constructing the target Internet of Things according to the first relevant information and the second relevant information; Wherein, the first relevant information corresponds to the relevant information of the target goods within the first preset range; The second relevant information corresponds to relevant information of an unmanned vehicle within a second preset range; The first preset range is smaller than or equal to the second preset range.

3. The method for dispatching unmanned vehicles based on the Internet of Things according to claim 2, characterized in that: The target information includes: The first location information, the second location information, the deadline information for reaching the second location, and / or the specification information of the target goods; Load information, vehicle condition information, third point information corresponding to the transport task, and / or fourth point information of a plurality of the unmanned vehicles within a preset distance from the first point; Wherein, the first point corresponds to the current point of the target cargo; The second point corresponds to the target point of the target cargo; The third point corresponds to the current point of the transport task; The fourth point corresponds to the target point of the transportation task.

4. The method for dispatching unmanned vehicles based on the Internet of Things according to claim 3, characterized in that: Determining the target unmanned vehicle and the target driving path according to the target information includes: In the case where it is determined that the first driving path and the second driving path have an intersection, the target unmanned vehicle is determined according to the load information, the vehicle condition information, the power consumption information of the multiple unmanned vehicles performing the transportation task of the target cargo, and the time window information of the multiple unmanned vehicles arriving at the first point and / or the second point; Wherein, the first driving path corresponds to the driving path between the first point and the second point; The second driving path corresponds to a driving path between the third point and the fourth point.

5. The method for dispatching unmanned vehicles based on the Internet of Things according to claim 4, characterized in that: Determining the target unmanned vehicle and the target driving path according to the target information further includes: Determining, based on the target information, multiple driving paths for the target unmanned vehicle to perform the transportation task of the target cargo; Based on the target Internet of Things, determining traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of the multiple driving paths; Determine the power consumption corresponding to the plurality of driving paths and / or the time of arriving at the second point according to the traffic signal change characteristics, the congestion characteristics, and / or the road condition characteristics; The target driving path is determined according to the power consumption and / or the time of reaching the second point.

6. The method for dispatching unmanned vehicles based on the Internet of Things according to claim 5, characterized in that: The determining of traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of the plurality of driving paths based on the target Internet of Things includes: Based on the target Internet of Things, obtaining driving characteristics of the unmanned vehicle corresponding to the multiple driving paths within a preset time; The traffic signal change characteristics, the congestion characteristics, and / or the road condition characteristics are determined based on the driving characteristics.

7. The method for dispatching unmanned vehicles based on the Internet of Things according to claim 5, characterized in that: The power consumption is determined according to the following formula: P total =(a×v avg 3 +b×v avg 2 +c×v avg +d)×F congestion +E traffic Among them, P total is the power consumption corresponding to the driving path, a is the air resistance coefficient, b is the rolling resistance coefficient, c is the friction loss coefficient, d is the fixed energy consumption of the unmanned vehicle at rest, and v avg is the average speed, F congestion is the crowding factor, F traffic Energy loss due to traffic signals.

8. The method for dispatching unmanned vehicles based on the Internet of Things according to claim 5, characterized in that: The time of reaching the second point is determined according to the following formula: Among them, T target is the time when the second point is reached, D is the corresponding distance of the driving path, and v free-flo is the maximum average speed that the driverless vehicle can reach without traffic congestion, F congestion is the crowding factor, F road is the road condition factor, T wait is the waiting time due to traffic signals.

9. The unmanned vehicle dispatching method based on the Internet of Things according to any one of claims 5 to 8, characterized in that: The controlling the target unmanned vehicle to move toward the target cargo according to the target unmanned vehicle and the target driving path includes: According to the duration information, weather information, and / or road condition information corresponding to the remaining road sections, the target driving path is corrected and / or the speed of the target unmanned vehicle is adjusted so that the target cargo reaches the second location before the deadline.

10. An unmanned vehicle dispatching system based on the Internet of Things, characterized in that: include: An acquisition unit, used for acquiring target information based on a target Internet of Things; A determination unit, configured to determine a target unmanned vehicle and a target driving path according to the target information; A control unit is used to control the target unmanned vehicle to move toward the target cargo according to the target unmanned vehicle and the target driving path.

Images