Unmanned vehicle dispatching method and system based on Internet of Things

Abstract

The embodiments of the present application provide an unmanned vehicle scheduling method and system based on the Internet of Things, which relates to the field of logistics and transportation technology, wherein the method includes: obtaining target information based on the target Internet of Things; determining the target unmanned vehicle and the target driving path based on the target information; and controlling the target unmanned vehicle to move toward the target goods based on the target unmanned vehicle and the target driving path. In this way, the present application can accurately obtain target information based on the target Internet of Things; accurately and efficiently determine the target unmanned vehicle and the target driving path based on the target information; and automatically and accurately control the target unmanned vehicle to move toward the target goods based on the target unmanned vehicle and the target driving path, which is conducive to improving the automated 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.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of logistics and transportation technology, and in particular to an unmanned vehicle scheduling method and system based on the Internet of Things. Background Art

[0002] Autonomous vehicles (AVs), also known as self-driving cars or driverless cars, are primarily designed to operate autonomously without human intervention. Currently, the scheduling of AVs in logistics and transportation operations suffers from low intelligence and poor accuracy.

[0003] Therefore, a new technical solution is urgently needed to solve the above technical problems. Summary of the Invention

[0004] According to the embodiments of the present application, an Internet of Things-based unmanned vehicle scheduling method and system are provided, which can realize accurate acquisition of target information based on the target Internet of Things; accurately and efficiently determine the target unmanned vehicle and the target driving path based on the target information; and automatically and accurately control the target unmanned vehicle to move toward the target cargo based on the target unmanned vehicle and the target driving path, which is conducive to improving the automated matching accuracy and matching efficiency of the target unmanned vehicle and the target cargo transportation task, thereby improving the transportation efficiency of the target cargo.

[0005] In a first aspect of the present application, a method for dispatching unmanned vehicles based on the Internet of Things is provided, comprising:

[0006] Based on the target Internet of Things, obtain target information;

[0007] Determine the target unmanned vehicle and target driving path based on the target information;

[0008] According to the target unmanned vehicle and the target driving path, the target unmanned vehicle is controlled to move toward the target cargo.

[0009] In some feasible implementations, before executing the target Internet of Things-based acquisition of target information, the above method further includes:

[0010] Acquire 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] The first relevant information corresponds to the relevant information of the target goods within the first preset range;

[0013] The second relevant information corresponds to relevant information of the unmanned vehicle within the second preset range;

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

[0015] In some feasible implementations, the target information includes:

[0016] First location information, second location information, deadline for arrival at the second location, and / or specification information of the target goods;

[0017] Load information, vehicle condition information, third point information corresponding to the transport task, and / or fourth point information of multiple unmanned vehicles within a preset distance from the first point;

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

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

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

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

[0022] In some feasible implementations, determining the target unmanned vehicle and the target driving path based on the target information includes:

[0023] When it is determined that the first driving path and the second driving path intersect, determining a target unmanned vehicle based on the load information, the vehicle condition information, the power consumption information of the multiple unmanned vehicles performing the task of transporting the target cargo, and the time window information of the multiple unmanned vehicles arriving at the first point and / or the second point;

[0024] The first driving path corresponds to a driving path between the first point and the second point;

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

[0026] In some feasible implementations, determining the target unmanned vehicle and the target driving path based on the target information further includes:

[0027] Determine multiple driving paths for the target unmanned vehicle to perform the transportation task of the target cargo based on the target information;

[0028] Determine traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of multiple driving routes based on the target Internet of Things;

[0029] Determining the power consumption corresponding to the plurality of driving paths and / or the time of arrival at the second point based on traffic signal change characteristics, congestion characteristics, and / or road condition characteristics;

[0030] The target driving path is determined based on the power consumption and / or the time of reaching the second point.

[0031] In some feasible implementations, the above-mentioned determination of traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of multiple driving routes based on the target Internet of Things includes:

[0032] Based on the target IoT, the driving characteristics of the unmanned vehicle corresponding to multiple driving paths within a preset time are obtained;

[0033] Based on the driving characteristics, traffic signal change characteristics, congestion characteristics, and / or road condition characteristics are determined.

[0034] In some feasible implementations, the 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] 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, v avg is the average speed, F congestion is the congestion factor, E traffic Energy loss due to traffic signals.

[0037] In some feasible implementations, the time of reaching the second point is determined according to the following formula:

[0038]

[0039] Among them, 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, T wait is the waiting time due to traffic signals.

[0040] In some feasible implementations, controlling the target unmanned vehicle to move toward the target cargo based on the target unmanned vehicle and the target driving path includes:

[0041] Based on the duration information, weather information, and / or road condition information corresponding to the remaining road section, the target driving path is modified and / or the speed of the target unmanned vehicle is adjusted so that the target cargo reaches the second location before the deadline.

[0042] In a second aspect of the present application, an unmanned vehicle dispatching system based on the Internet of Things is provided, comprising:

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

[0044] a determination unit, configured to determine a target unmanned vehicle and a target driving path based on the target information;

[0045] The 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.

[0046] The embodiment of the present application provides an Internet of Things-based unmanned vehicle scheduling method and system, wherein the method includes: obtaining target information based on the target Internet of Things; determining the target unmanned vehicle and the target driving path based on the target information; and controlling the target unmanned vehicle to move toward the target cargo based on the target unmanned vehicle and the target driving path. The present application can achieve accurate acquisition of target information based on the target Internet of Things; accurate and efficient determination of the target unmanned vehicle and the target driving path based on the target information; and automatic and accurate control of the target unmanned vehicle to move toward the target cargo based on the target unmanned vehicle and the target driving path, which is conducive to improving the automated matching accuracy and matching efficiency of the target unmanned vehicle and the target cargo transportation task, thereby improving the transportation efficiency of the target cargo.

[0047] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0049] Figure 1 A schematic diagram of a process for dispatching unmanned vehicles based on the Internet of Things provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of a process for another method for dispatching unmanned vehicles based on the Internet of Things provided in an embodiment of the present application;

[0051] Figure 3A schematic diagram of a process for another method for dispatching unmanned vehicles based on the Internet of Things provided in an embodiment of the present application;

[0052] Figure 4 A schematic structural diagram of an IoT-based unmanned vehicle dispatching system provided in an embodiment of the present application;

[0053] Figure 5 A structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0055] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0056] In a first aspect of the embodiments of the present application, a method for dispatching unmanned vehicles based on the Internet of Things is proposed.

[0057] In some feasible embodiments, the above method includes: obtaining first relevant information and second relevant information; constructing a target Internet of Things based on the first relevant information and the second relevant information; wherein the first relevant information corresponds to the relevant information of the target goods within a first preset range; the second relevant information corresponds to the relevant information of the unmanned vehicle within a second preset range; the first preset range is less than or equal to the second preset range.

[0058] Exemplarily, the first relevant information may include: geographic coordinate information corresponding to the current location of all target goods within the first preset range, geographic 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 geographic coordinate information may include longitude and latitude coordinates. The specification information may include size information, mass information, and / or volume information. The category information may include fragile goods, chemicals, food, etc., which is used to determine the specific transportation requirements of the target cargo and / or specific storage requirements to match the target unmanned vehicle that meets the specific transportation requirements and / or specific storage requirements to perform the target cargo transportation mission.

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

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

[0062] Specifically, the load information may include: total mass information, volume information, distribution information of the cargo currently loaded on the unmanned vehicle, and / or category information of the cargo loaded. The category information may include: refrigerated goods information and / or dangerous goods information.

[0063] Specifically, the vehicle condition information may include mechanical status information, communication status information, and / or maintenance record information. The mechanical status information may include tire wear information, battery charge information, and / or engine status information. The communication status information may include signal strength information, connection type information, bandwidth information, packet loss rate information, signal-to-noise ratio information, and / or latency information.

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

[0065] Specifically, the category configuration information may include vehicle types and / or supported cargo types. Vehicle types may include vans, flatbed trucks, and / or refrigerated trucks. Supported cargo types may include fragile items, chemicals, and / or fresh food.

[0066] Specifically, the safety information can be determined based on the response time of the unmanned vehicle's emergency braking system, its obstacle avoidance success rate, and / or historical accident records. The safety of the unmanned vehicle is negatively correlated with the response time of the unmanned vehicle's emergency braking system, meaning that the shorter the response time of the unmanned vehicle's emergency braking system, the higher the safety of the unmanned vehicle. The safety of the unmanned vehicle is positively correlated with the obstacle avoidance success rate, meaning that the higher the obstacle avoidance success rate, the higher the safety of the unmanned vehicle. The safety of the unmanned vehicle is negatively correlated with the number of historical accidents, meaning that the greater the number of historical accidents, the higher the safety of the unmanned vehicle.

[0067] Specifically, the above-mentioned corresponding transportation task information may include: the geographical coordinate information of the starting point 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 expected arrival time, etc.

[0068] The first preset range can be determined based on the area selected by the target user on the satellite map. Alternatively, the first preset range can also be determined based on the administrative region entered by the target user and / or a set geographic coordinate range. The second preset range can be determined based on the first preset range, such that the second preset range is greater than or equal to the first preset range.

[0069] Therefore, the above method can realize the accurate and dynamic construction of the target Internet of Things based on the relevant information of the target goods within the first preset range and the relevant information of the unmanned vehicle within the second preset range, so as to improve the accuracy and efficiency of obtaining the target information, and provide an Internet of Things architecture foundation for accurately determining the target unmanned vehicle and the target driving path based on the target information.

[0070] It should be noted that the update frequency of the target IoT can be determined based on the target user's accuracy requirements for the target information and / or the performance of the device. The update frequency of the target IoT is positively correlated with the target user's accuracy requirements for the target information. That is, the higher the target user's accuracy requirements for the target information, the faster the update frequency of the target IoT. The update frequency of the target IoT is also positively correlated with the computing upper limit of the device hardware and / or the storage space upper limit. That is, the higher the computing upper limit of the device hardware and / or the storage space upper limit, the faster the update frequency of the target IoT.

[0071] For example, Figure 1-Figure 3 A process diagram of an unmanned vehicle scheduling method 100 based on the Internet of Things provided in an embodiment of the present application is shown as follows: Figure 1-Figure 3 As shown, the method 100 includes:

[0072] Step S110: Based on the target Internet of Things, obtain target information.

[0073] For example, the target information can be obtained based on a dynamic time window, based on the target IoT. The dynamic time window automatically adjusts the window duration to obtain the target information. It should be noted that the duration of the dynamic time window is negatively correlated with the congestion and / or complexity of the road section. That is, the higher the congestion 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 for automatically controlling the dynamic window to be shortened when the traffic flow on the road section is large, accidents occur frequently, and / or the road conditions are complex, so as to achieve real-time update and acquisition of the target information. When the traffic flow on the road section is small and the road conditions are less complex, automatically controlling the dynamic window to be extended is beneficial for avoiding the transmission and processing of target information with high similarity, thereby saving computing power.

[0075] It should be noted that the target information can also be obtained based on a fixed time window according to the actual needs of the target user and the target Internet of Things. The length of the fixed time window can be defined according to the actual needs of the target user.

[0076] In some feasible embodiments, the above-mentioned target information includes: the first point information, the second point information, the deadline information for arriving at the second point, and / or specification information of the target cargo; the load information, vehicle condition information, the third point information corresponding to the transportation task, and / or the fourth point information of multiple 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 transportation task; and the fourth point corresponds to the target point of the transportation task.

[0077] For example, the first location information may include: geographic coordinate information of the current location of the target cargo; the second location information may include: geographic coordinate information of the target location of the target cargo.

[0078] Exemplarily, the cut-off time for arrival at the second location may be set by the shipper and / or recipient of the target goods. Alternatively, the cut-off time for arrival at the second location may be automatically estimated based on the category attribute information of the target goods. For example, if the category attribute of the target goods is fresh fruit, the storage time of the target goods may be determined based on the category attribute, and the cut-off time may be automatically estimated based on the storage time.

[0079] For example, the specification information may include size information, mass information, volume information, and / or category information. The category information may include fragile goods, chemicals, food, etc., which is used to determine specific transportation requirements for the target cargo and / or specific storage requirements to match a target unmanned vehicle that meets the specific transportation requirements and / or specific storage requirements for performing the target cargo transportation mission.

[0080] For example, the load information may include: total mass information, volume information, distribution information of the cargo currently loaded on the unmanned vehicle, and / or category information of the cargo loaded. The category information may include: refrigerated goods information and / or dangerous goods information.

[0081] Exemplarily, the vehicle condition information may include mechanical status information, communication status information, and / or maintenance record information. The mechanical status information may include tire wear information, battery charge information, and / or engine status information. The communication status information may include signal strength information, connection type information, bandwidth information, packet loss rate information, signal-to-noise ratio information, and / or latency information.

[0082] For example, the third location information may include the geographic coordinate information of the current location of the transport task performed by the unmanned vehicle. The fourth location information may include the geographic coordinate information of the target location corresponding to the transport task performed by the unmanned vehicle.

[0083] It should be noted that, based on the target IoT, first location information, second location information, deadline information for reaching the second location, and / or specification information of the target cargo, as well as load information, vehicle condition information, third location information corresponding to the transportation task, and / or fourth location information of multiple unmanned vehicles within a preset distance from the first location can be obtained according to a dynamic time window and / or a fixed time window. The preset distance can be determined based on the urgency of the transportation of the target cargo, and the preset distance and the urgency of the transportation of the target cargo are negatively correlated, i.e., the higher the urgency of the transportation of the target cargo, the smaller the preset distance.

[0084] Therefore, the above method can accurately determine the target unmanned vehicle and the target driving path based on the first point information, second point information, deadline information for arriving at the second point, and / or specification information of the target cargo; load information, vehicle condition information, third point information corresponding to the transportation task, and / or fourth point information of multiple unmanned vehicles within a preset distance from the first point, and provide accurate data support for accurately controlling the target unmanned vehicle to move toward the target cargo based on the target unmanned vehicle and the target driving path, thereby improving the automated matching accuracy and matching efficiency of the target unmanned vehicle and the target cargo transportation task, and further improving the transportation efficiency of the target cargo.

[0085] Step S120: Determine the target unmanned vehicle and the target driving path based on the target information.

[0086] Exemplarily, the target unmanned vehicle and the target driving path can be determined based on the current location information of the target cargo, the target location information of the target cargo, the deadline information for the target cargo to arrive at the target location, the specification information, the load information of the unmanned vehicle within a preset distance from the first location, the vehicle condition information, the current location information of the unmanned vehicle corresponding to the transportation task, and / or the target location information of the unmanned vehicle corresponding to the transportation task.

[0087] It should be noted that, when it is determined that there are multiple target unmanned vehicles based on the above target information, the best target unmanned vehicle can be determined as the final target unmanned vehicle based on the vehicle condition information to complete the transportation task of the above target goods.

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

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

[0090] Mechanical condition score = tire wear score × ω1 + battery charge score × ω2 + engine condition score × ω3 (1)

[0091] Where ω1 is the weight corresponding to the tire wear score, ω2 is the weight corresponding to the battery charge score, and ω3 is the weight corresponding to the engine status score. The sum of the weights ω1, ω2, and ω3 for the tire wear score, ω1 for the battery charge score, and ω3 for the engine status score is 1. The tire wear score is negatively correlated with the degree of tire wear; that is, the higher the tire wear, the lower the tire wear score. The battery charge score is positively correlated with the remaining battery charge; that is, the higher the remaining battery charge, the higher the battery charge score. The engine status score is negatively correlated with the cumulative engine runtime; that is, the shorter the cumulative engine runtime, the higher the engine status score.

[0092] It should be noted that the relationship between the weight ω1 corresponding to the tire wear degree score, the weight ω2 corresponding to the battery power score, and the weight ω3 corresponding to the engine status score can be determined according to the actual situation.

[0093] For example, if the target cargo's transport distance is greater than a preset distance threshold, the weight ω1 associated with the tire wear score is less than or equal to the weight ω3 associated with the engine condition score, and the weight ω3 associated with the engine condition score is less than or equal to the weight ω2 associated with the battery charge score. The preset distance threshold indicates that the target cargo's transport mission corresponds to a long-distance transport mission.

[0094] Exemplarily, when the transportation distance of the target cargo is less than or equal to a preset distance threshold, the weight ω1 corresponding to the above-mentioned tire wear score is less than or equal to the weight ω2 corresponding to the above-mentioned battery power score, and the weight ω2 corresponding to the above-mentioned small battery power score is less than or equal to the weight ω3 corresponding to the engine status score, so as to improve the safety of the transportation mission of the target cargo.

[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 × ω5 + bandwidth score × ω6 + packet loss rate score × ω7 + signal-to-noise ratio score × ω8 + delay score × ω9 (2)

[0097] Where ω4 is the weight corresponding to the signal strength score, ω5 is the weight corresponding to the connection type score, ω6 is the weight corresponding to the bandwidth score, ω7 is the weight corresponding to the packet loss rate score, ω8 is the weight corresponding to the signal-to-noise ratio score, and ω9 is the weight corresponding to the delay score. The sum of the weight ω4 for the signal strength score, ω5 for the connection type score, ω6 for the bandwidth score, ω7 for the packet loss rate score, ω8 for the signal-to-noise ratio score, and ω9 for the delay score is 1. The above-mentioned signal strength score is positively correlated with the signal strength, that is, the stronger the signal strength, the higher the above-mentioned signal strength score; the above-mentioned 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-mentioned connection type score; the above-mentioned bandwidth score is positively correlated with the bandwidth, that is, the larger the bandwidth, the higher the bandwidth score; the above-mentioned 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-mentioned 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-mentioned delay score is negatively correlated with the delay duration, that is, the longer the delay duration, the lower the above-mentioned delay score.

[0098] It should be noted that the relationship between the signal strength score corresponding to weight ω4, the connection type score corresponding to weight ω5, the bandwidth score corresponding to weight ω6, the packet loss rate score corresponding to weight ω7, the signal-to-noise ratio score corresponding to weight ω8, and the delay score corresponding to weight ω9 can be determined according to the actual situation.

[0099] For example, in a normal situation, the weight ω4 corresponding to the above-mentioned signal strength score can be set to 0.15, the weight ω5 corresponding to the connection type score can be set to 0.15, the weight ω6 corresponding to the bandwidth score can be set to 0.20, the weight ω7 corresponding to the packet loss rate score can be set to 0.15, the weight ω8 corresponding to the signal-to-noise ratio score can be set to 0.20, and the weight ω9 corresponding to the delay score can be set to 0.15.

[0100] For example, when the transportation task of the target cargo is more urgent, and / or the real-time and reliability requirements of data transmission are high, the weight ω4 corresponding to the above-mentioned signal strength score can be set to 0.10, the weight ω5 corresponding to the connection type score can be set to 0.15, the weight ω6 corresponding to the bandwidth score can be set to 0.15, the weight ω7 corresponding to the packet loss rate score can be set to 0.20, the weight ω8 corresponding to the signal-to-noise ratio score can be set to 0.20, and the weight ω9 corresponding to the delay score can be set to 0.20.

[0101] For example, when the data transmission volume is large, the weight ω4 corresponding to the above-mentioned signal strength score can be set to 0.10, the weight ω5 corresponding to the connection type score can be set to 0.10, the weight ω6 corresponding to the bandwidth score can be set to 0.30, the weight ω7 corresponding to the packet loss rate score can be set to 0.15, the weight ω8 corresponding to the signal-to-noise ratio score can be set to 0.15, and the weight ω9 corresponding to the delay score can be set to 0.20.

[0102] Exemplarily, when the wide-area coverage 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 long, the weight ω4 corresponding to the above-mentioned signal strength score can be set to 0.10, the weight ω5 corresponding to the connection type score can be set to 0.25, the weight ω6 corresponding to the bandwidth score can be set to 0.15, the weight ω7 corresponding to the packet loss rate score can be set to 0.20, the weight ω8 corresponding to the signal-to-noise ratio score can be set to 0.15, and the weight ω9 corresponding to the delay score can be set to 0.15.

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

[0104] Final score = mechanical condition score × ω 10 +Communication Status Score×ω 11 (3)

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

[0106] It should be noted that if the target information determines that there are multiple target driving routes corresponding to the target unmanned vehicles, the final target driving route is determined based on the road condition information and / or weather information corresponding to the target driving routes. The road condition information and / or weather information for the target driving route can be determined by retrieving target data collected by multiple target unmanned vehicles that passed through the corresponding road section of the target driving route within a preset time period. The number of targets can include target video data and / or target image data. The preset time period is negatively correlated with the required accuracy of the road condition information and / or weather information. Specifically, the higher the required accuracy of the road condition information and / or weather information, the shorter the preset time period. The road condition information and / or weather information for the target driving route can also be determined by retrieving satellite data. The road condition information can include road condition information, such as slippery road conditions, pothole conditions, waterlogging, or icy conditions, traffic flow information, and / or obstacle information. The weather information may include: sunny, rainy, snowy, cloudy, visibility, temperature gradient, and / or humidity gradient, etc.

[0107] For example, the score of the target driving path can be determined based on 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-mentioned target unmanned vehicle to perform the transportation task of the target goods according to the above-mentioned target driving path.

[0108] The score of the 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] Among them, ω 12 is the weight corresponding to the road condition score, ω 13 The weight corresponding to the weather score. The weight corresponding to the above road condition score ω 12 And the weight ω corresponding to the above weather score 13 The sum of is 1. It should be noted that the road condition score corresponds to the weight ω 12 Can be set to be greater than or equal to the weight corresponding to the above weather score ω 13 .

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

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

[0113] Step S121: When it is determined that the first driving path and the second driving path intersect, the target unmanned vehicle is determined based on the load information, the vehicle condition information, the power consumption information of multiple unmanned vehicles performing the transportation task of the target cargo, 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 the driving path between the third point and the fourth point.

[0114] Exemplarily, when it is determined that the transportation path of the target cargo and the transportation paths corresponding to the transportation tasks performed by multiple unmanned vehicles intersect, that is, when the transportation path of the target cargo and the transportation paths corresponding to the transportation tasks performed by multiple unmanned vehicles have a common road section, the target unmanned vehicle is determined based on the load information, vehicle condition information, power consumption information of the multiple unmanned vehicles in performing the transportation tasks of the target cargo, time window information of the multiple unmanned vehicles arriving at the first point, and / or the second point.

[0115] The load information includes the total mass, volume, distribution, and / or category of the cargo currently loaded on the unmanned vehicle. The category information may include refrigerated goods and / or hazardous materials.

[0116] Specifically, the matching degree between the remaining loading spaces corresponding to the plurality of unmanned vehicles and the volume of the target cargo, and the matching degree between the mass of the remaining loadable cargo and the mass of the target cargo can be determined based on the load information.

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

[0118] Specifically, the target unmanned vehicle can be determined based on the matching degree between the remaining loading space of the multiple unmanned vehicles and the volume of the target cargo, the matching degree between the mass of the remaining cargo that can be loaded by the multiple unmanned vehicles and the mass of the target cargo, the final scores of the multiple unmanned vehicles determined based on formula (3), the maximum power consumption of the multiple unmanned vehicles in performing the transportation task of the target cargo, and / or the total time that the multiple unmanned vehicles perform the transportation task of the target cargo.

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

[0120] It should be noted that the sum of the corresponding weights of the degree of matching between the remaining loading space of the above-mentioned multiple unmanned vehicles and the volume of the above-mentioned target cargo, the corresponding weights of the degree of matching between the mass of the above-mentioned cargo that can be loaded by the above-mentioned multiple unmanned vehicles and the mass of the above-mentioned target cargo, the corresponding weight of the above-mentioned final score, the corresponding weight of the above-mentioned maximum power consumption, and the corresponding weight of the above-mentioned total duration is 1. The above-mentioned weights can be determined according to the value of the target cargo and the urgency of the delivery demand of the target cargo in the actual situation. For example, in the case where the target cargo is an electronic product of high value and urgently needs to be delivered, the corresponding weight of the degree of matching between the remaining loading space of the above-mentioned multiple unmanned vehicles and the volume of the above-mentioned target cargo can be set to 0.25, the corresponding weight of the degree of matching between the mass of the above-mentioned cargo that can be loaded by the above-mentioned multiple unmanned vehicles and the mass of the above-mentioned target cargo can be set to 0.25, the corresponding weight of the above-mentioned final score can be set to 0.2, the corresponding weight of the above-mentioned maximum power consumption can be set to 0.15, and the corresponding weight of the above-mentioned total duration can be set to 0.15.

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

[0122] Therefore, the above method can achieve the situation where it is determined that the transportation paths corresponding to the transportation tasks performed by multiple unmanned vehicles and the transportation paths of the target goods intersect, and select an unmanned vehicle as the target unmanned vehicle with a high degree of match between the remaining loading space and the volume of the above-mentioned target goods, a high degree of match between the mass of the remaining cargo that can be loaded on the vehicle and the mass of the above-mentioned target goods, a good mechanical state and communication state, a low power consumption when performing the transportation task of the target goods, and a short time when performing the transportation task of the target goods, thereby avoiding the waste of load space in the process of the target unmanned vehicle performing the target goods transportation task, avoiding the waste of total load space in the process of the unmanned vehicle networking to perform all target goods transportation tasks in the target Internet of Things, improving the safety of the selected target unmanned vehicle, thereby improving the safety of the target goods transportation operation, and helping to reduce the power consumption of the target unmanned vehicle in the process of performing the target goods transportation task, thereby avoiding energy waste in the transportation operation of the target goods, and helping to shorten the transportation time of the target goods, thereby improving the satisfaction and experience of the recipient and / or sender of the target goods.

[0123] In some feasible implementations, the above-mentioned step S120: determining the target unmanned vehicle and the target driving path based on the target information also includes: step S122: determining multiple driving paths for the target unmanned vehicle to perform the transportation task of the target cargo based on the target information.

[0124] For example, after determining the target unmanned vehicle based on step S121, multiple driving paths with scores greater than a preset threshold can be selected according to the above formula (4), so as to determine multiple driving paths for the target unmanned vehicle to perform the transportation task of the target cargo based on the target information. The above preset threshold can be set according to the actual demand scenario.

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

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

[0127] Step S123-a: Based on the target Internet of Things, obtain the driving characteristics of the unmanned vehicle corresponding to multiple driving paths within a preset time.

[0128] Exemplarily, the aforementioned driving characteristics may include: vehicle speed characteristics, vehicle acceleration characteristics, vehicle lane change frequency characteristics, vehicle spacing characteristics, vehicle driving bumpiness characteristics, and / or vehicle waiting time characteristics at corresponding traffic light positions. The aforementioned preset time may be set based on the target user's requirements for the accuracy of determining traffic signal change characteristics, congestion characteristics, and / or road condition characteristics for the target path. The preset time is negatively correlated with the target user's requirements for the accuracy of determining traffic signal change characteristics, congestion characteristics, and / or road condition characteristics for the target path. That is, the higher the target user's requirements for the accuracy of determining traffic signal change characteristics, congestion characteristics, and / or road condition characteristics for the target path, the shorter the preset time.

[0129] It should be noted that the aforementioned driving characteristics can be generated by pre-processing the relevant data from the driving recorder locally in the unmanned vehicle before uploading it to the target IoT. Pre-processing operations may include data cleaning, data compression, feature extraction, and / or data standardization to reduce the load on the cloud server, improve data transmission speed and efficiency, and reduce data latency.

[0130] Step S123-b: Determine traffic signal change characteristics, congestion characteristics, and / or road condition characteristics based on driving characteristics.

[0131] Exemplarily, traffic signal change characteristics, congestion characteristics, and / or road condition characteristics can be determined based on the above-mentioned vehicle driving speed characteristics, vehicle driving acceleration characteristics, vehicle lane change frequency characteristics, vehicle spacing characteristics, vehicle driving bumpiness characteristics, and / or vehicle waiting time characteristics at the corresponding position of the traffic light.

[0132] Specifically, the traffic signal change characteristics can be determined based on the waiting time characteristics of a preset number of vehicles at the corresponding traffic signal location within a preset time period. The traffic signal change characteristics may include: the frequency characteristics of changes corresponding to red, green, and yellow lights, and / or the duration characteristics. The preset number is positively correlated with the target user's desired accuracy in determining the traffic signal change characteristics. Specifically, the higher the target user's desired accuracy in determining the traffic signal change characteristics, the larger the preset number.

[0133] Specifically, the above-mentioned congestion characteristics can be determined based on the driving speed characteristics, vehicle driving acceleration characteristics, vehicle lane change frequency characteristics, and / or vehicle spacing characteristics of the above-mentioned vehicles that are greater than or equal to a preset number within a preset time.

[0134] Specifically, the aforementioned driving speed characteristics may include: the average speed of vehicles, the standard deviation of vehicle speeds, and / or the coefficient of variation of vehicle speeds. The congestion level is negatively correlated with the average speed of vehicles; that is, the higher the average speed of vehicles, the lower the congestion level. The congestion level is negatively correlated with the standard deviation of vehicle speeds and / or the coefficient of variation; that is, the larger the standard deviation of vehicle speeds and / or the coefficient of variation, the higher the congestion level.

[0135] Specifically, the driving acceleration characteristics may include: the standard deviation of vehicle acceleration and / or the coefficient of variation of vehicle acceleration. The congestion level is negatively correlated with the standard deviation and / or coefficient of variation of vehicle acceleration. Specifically, a larger standard deviation and / or coefficient of variation of vehicle acceleration indicates a higher congestion level.

[0136] Specifically, the congestion level is positively correlated with the frequency of lane changes; that is, the higher the lane change frequency, the higher the congestion level. The lane change frequency can be determined based on the lane change frequency of a preset number of vehicles or more within a preset time period. It should be noted that the preset number is positively correlated with the target user's required accuracy for determining lane change frequency characteristics; that is, the higher the target user's required accuracy for determining lane change frequency, the larger the preset number.

[0137] Specifically, the congestion level is negatively correlated with vehicle spacing; that is, the greater the distance between vehicles, the lower the congestion level. The vehicle spacing can be determined based on the distance between vehicles that is greater than or equal to a preset number within a preset time period. The preset number is positively correlated with the target user's requirement for the accuracy of determining vehicle spacing characteristics; that is, the higher the target user's requirement for the accuracy of determining vehicle spacing characteristics, the larger the preset number.

[0138] Specifically, the above-mentioned 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-mentioned 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, lighter vibration intensity, lower frequency, and relatively smooth road surface. General road conditions correspond to the presence of small undulations or cracks on the road surface, mild vehicle bumps, and moderate vibration frequency and vibration intensity. Poor road conditions correspond to the presence of obvious potholes, cracks or other large defects on the road surface, frequent medium or severe vehicle bumps, high vibration frequency, and large intensity. Extremely poor road conditions correspond to serious damage to the road surface, the inability of the vehicle to drive, extremely strong vibration, and extremely long vibration duration.

[0139] Therefore, the above method can realize the accurate acquisition of the driving characteristics of unmanned vehicles corresponding to multiple driving paths within a preset time based on the target Internet of Things; based on the above driving characteristics, the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics are accurately determined to provide a data basis for selecting the target driving path corresponding to the target unmanned vehicle, thereby improving the determination accuracy and efficiency of the target driving path.

[0140] Step S124: 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, congestion characteristics, and / or road condition characteristics.

[0141] For example, in some feasible implementations, the 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] 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, v avg is the average speed, F congestion is the congestion factor, E traffic Energy loss due to traffic signals.

[0144] It should be noted that the above-mentioned air resistance coefficient a, rolling resistance coefficient b, friction loss coefficient c, fixed energy consumption d of the unmanned vehicle at rest, average speed v avg , congestion factor F congestion and the energy loss E caused by traffic signals traffic It can be determined based on vehicle speed characteristics, vehicle acceleration characteristics, vehicle lane change frequency characteristics, vehicle spacing characteristics, vehicle bumpiness characteristics, traffic signal change characteristics, congestion characteristics, and / or road condition characteristics.

[0145] Therefore, the above method can be realized 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 of the unmanned vehicle at rest, 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, thereby accurately selecting a driving path with lower power consumption as the target driving path based on the above power consumption, so as to reduce the energy consumption generated by the target unmanned vehicle in performing the target cargo transportation task, thereby reducing energy consumption losses and saving the cost of the target unmanned vehicle in performing the target cargo transportation task.

[0146] In some feasible implementations, the time of reaching the second point is determined according to the following formula:

[0147]

[0148] Among them, 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, 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 based on the second point and the first point, wherein the corresponding distance D of the driving path is the actual distance traveled by the unmanned vehicle corresponding to the driving path when performing the transportation task of the target goods. The maximum average speed v that the unmanned vehicle can reach in the absence of traffic congestion is free-flow The congestion factor F can be determined based on the above congestion characteristics, the vehicle's travel speed characteristics, and / or the vehicle's travel acceleration characteristics. congestion The above traffic factor F can be determined based on the congestion characteristics. road The waiting time T caused by traffic signals can be determined based on the road condition characteristics. wait It can be determined based on the changing characteristics of traffic signals.

[0150] Therefore, the above method can be implemented based on the above formula (6), according to the corresponding distance D of the driving path, the maximum average speed v that the unmanned vehicle can reach in the absence of traffic congestion free-flow , the above congestion factor F congestion 、The above road condition factor F road And the waiting time T caused by traffic signals wait , accurately determine the time when the target unmanned vehicle arrives at the second point based on the above multiple driving paths, and then according to the time T when the target unmanned vehicle arrives at the second point targetAccurately select a shorter driving route as the target driving route to complete the target cargo transportation task as soon as possible, reduce the time consumption of the transportation task, so that the recipient of the target cargo can receive the target cargo as soon as possible, thereby improving the user experience of the sender of the target cargo and / or the recipient of the target cargo.

[0151] Step S125: Determine the target driving path based on the power consumption and / or the time of reaching the second point.

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

[0153] Therefore, the above method can accurately determine multiple driving paths for the target unmanned vehicle to perform the transportation task of the target goods based on the target information; based on the target Internet of Things, accurately determine the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of the multiple driving paths; accurately determine the power consumption corresponding to the multiple driving paths and / or the time of arrival at the second point based on the traffic signal change characteristics, congestion characteristics, and / or road condition characteristics; based on the power consumption and / or the time of arrival at the second point, accurately select a path with lower power consumption and / or an earlier time of arrival at the second point as the target driving path, which is conducive to reducing the energy consumption generated by the target unmanned vehicle in performing the transportation task of the target goods, reducing energy consumption losses, saving the cost of the target unmanned vehicle in 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 spent on the transportation task, so that the recipient of the target goods receives the target goods as soon as possible, thereby improving the user experience of the sender of the target goods and / or the recipient of the target goods.

[0154] Step S130: According to the target unmanned vehicle and the target driving path, control the target unmanned vehicle to move toward the target cargo.

[0155] For example, after determining the target unmanned vehicle and the target driving path corresponding to the target unmanned vehicle based on steps S121 to S125, the target unmanned vehicle can be controlled to move to the current location of the target cargo, that is, the first point, according to the above-mentioned target driving path to obtain the target cargo, and then the target unmanned vehicle can be controlled to continue to move to the target location of the target cargo, that is, the second point, according to the above-mentioned target driving path to complete the transportation task of the above-mentioned target cargo.

[0156] In some feasible implementations, the above step S130: controlling the target unmanned vehicle to move toward the target cargo according to the target unmanned vehicle and the target driving path includes:

[0157] Step S131: Based on the duration information, weather information, and / or road condition information corresponding to the remaining road sections, the target driving path is modified, and / or the speed of the target unmanned vehicle is adjusted so that the target cargo reaches the second point before the deadline.

[0158] Exemplarily, the duration information corresponding to the remaining road segments may be determined based on the difference between the current clock information and the deadline information.

[0159] For example, if the remaining travel time information determines that the duration corresponding to the remaining road segment is less than a preset duration, the target cargo transport mission is determined to be at risk of delay, and the target route is modified to increase the proportion of expressways and / or trunk roads within the target route, and to reduce the proportion of highly congested roads and / or roads with poor road conditions, so that the target cargo arrives at the second destination before the deadline. Alternatively, if the remaining travel time information determines that the duration corresponding to the remaining road segment is less than a preset time, the target unmanned vehicle is automatically controlled to increase its speed so that the target cargo arrives at the second destination before the deadline. The preset duration corresponds to the maximum duration required to travel from the current node to the second destination based on the average speed of a preset number of vehicles traveling along the remaining travel segment. The average speed can be determined based on the target Internet of Things. The preset number is positively correlated with the target user's accuracy requirement for determining the preset duration; that is, the higher the accuracy requirement for determining the preset duration, the larger the preset number.

[0160] For example, based on the target IoT, the target vehicle currently traveling on the remaining road section can be controlled to perform target collection and preprocessing operations on the weather information and / or road condition information corresponding to the remaining road section, and then transmit back the preprocessed weather information and / or road condition information. If, based on the aforementioned weather information, it is determined that the weather severity corresponding to the remaining road section is greater than a preset severity, and / or the road congestion is greater than a preset degree, the target driving path can be modified to select a road section with weather severity less than or equal to the preset severity, and / or a congestion less than or equal to the preset congestion as the target driving path, so that the target cargo arrives at the second location before the deadline. And / or, if, based on the aforementioned weather information, it is determined that the weather severity corresponding to the remaining road section is greater than a preset severity, and / or the road congestion is greater than a preset congestion, the target unmanned vehicle can be automatically controlled to reduce its speed to improve the safety of the target unmanned vehicle traveling on the remaining road section. Among them, the above-mentioned severity can be determined based on the maximum wind speed, maximum hourly precipitation, minimum visibility, and / or maximum road slipperiness index collected by the target Internet of Things controlling the target vehicle currently traveling on the remaining road section. The above-mentioned preset severity can be determined based on the duration corresponding to the remaining road section, and the weather severity corresponding to the maximum average driving speed of the target unmanned vehicle traveling on the remaining road section. The above-mentioned congestion can be determined based on the maximum number of vehicles passing through each lane per minute, and / or the minimum average speed, collected by the target Internet of Things controlling the target vehicle currently traveling on the remaining road section. The above-mentioned preset congestion can correspond to the duration corresponding to the remaining road section, and determine the congestion corresponding to the maximum average driving speed of the target unmanned vehicle traveling on the remaining road section.

[0161] Therefore, the above method can accurately perform correction operations on the target driving path and / or accurately perform control operations on the driving speed of the target unmanned vehicle based on the duration information corresponding to the remaining road sections, the weather information corresponding to the remaining road sections, and / or the road condition information, so that the target goods can reach the second point before the deadline, which is conducive to further improving the execution accuracy of the target goods transportation task, so that the target goods arrive at the target point on time, thereby further improving the user experience of the sender of the target goods and / or the recipient of the target goods.

[0162] Based on this, the IoT-based unmanned vehicle scheduling method provided by this application includes: obtaining target information based on the target IoT; determining the target unmanned vehicle and the target driving path based on the target information; and controlling the target unmanned vehicle to move toward the target cargo based on the target unmanned vehicle and the target driving path. In this way, this application can achieve accurate acquisition of target information based on the target IoT; accurate and efficient determination of the target unmanned vehicle and the target driving path based on the target information; and automatic and accurate control of the target unmanned vehicle to move toward the target cargo based on the target unmanned vehicle and the target driving path, which is conducive to improving the automated matching accuracy and matching efficiency of the target unmanned vehicle and target cargo transportation tasks, thereby improving the transportation efficiency of the target cargo.

[0163] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0164] The above is an introduction to the method embodiment. The following is a further explanation of the solution described in this application through an apparatus embodiment.

[0165] In a second aspect of the embodiments of the present application, an unmanned vehicle dispatching system based on the Internet of Things is proposed. Figure 4 This is a structural diagram of an unmanned vehicle dispatching system 200 based on the Internet of Things provided in an embodiment of the present application. Figure 4 The system 200 shown includes: an acquisition unit 210 , a determination unit 220 and a control unit 230 .

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

[0167] The determination unit 220 is configured to determine a target unmanned vehicle and a target driving path based on the target information.

[0168] The control unit 230 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.

[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 aforementioned system embodiment and will not be repeated here.

[0170] Figure 5This is a structural diagram of an electronic device 300 provided in an embodiment of the present application. Figure 5 As shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 into the 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, ROM 302 and 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, and the like; an output section 307 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 308 including a hard disk; and a communication section 309 including a network interface card such as a LAN card or a modem. 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. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read therefrom can be installed into the storage section 308 as needed.

[0172] In particular, 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 comprising a computer program carried on a machine-readable medium, the computer program containing 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 a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the above-mentioned functions defined in the system of the present application are performed.

[0173] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may 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 may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0174] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the aforementioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example: two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0175] The units or modules involved in the embodiments described in this application may be implemented in software or hardware. The units or modules described may also be provided in a processor. The names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves.

[0176] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application involved in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned application concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied for 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 based on the target information; Controlling the target unmanned vehicle to move toward target cargo based on the target unmanned vehicle and the target driving path; The acquiring of target information based on the target Internet of Things includes: acquiring the target information according to a dynamic time window; wherein the duration of the dynamic time window is negatively correlated with the congestion and / or complexity of the road section; Determining the target unmanned vehicle and the target driving path according to the target information includes: Determining, based on the target information, multiple driving paths for the target unmanned vehicle to perform the task of transporting the target cargo; Determining traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of the plurality of driving routes based on the target Internet of Things; Determining the power consumption corresponding to the plurality of driving paths and / or the time of arrival at the second point based on the traffic signal change characteristics, the congestion characteristics, and / or the road condition characteristics; The second point corresponds to the target point of the target cargo; determining the target driving path according to the power consumption and / or the time of reaching the second point; Determining 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; determining the traffic signal change characteristics, the congestion characteristics, and / or the road condition characteristics based on the driving characteristics; The power consumption is determined according to the following formula: ; in, is the power consumption corresponding to the driving path, is the air resistance coefficient, is the rolling resistance coefficient, is the friction loss coefficient, is the fixed energy consumption of the unmanned vehicle at rest, is the average speed, is the congestion factor, is the energy loss caused by the traffic signal; the time of reaching the second point is determined according to the following formula: ; in, is the time when the second point is reached, is the corresponding distance of the driving path, is the maximum average speed that an autonomous vehicle can achieve without traffic congestion, is the congestion factor, is the road condition factor, is the waiting time due to traffic signals.

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 for arrival at 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; The first point corresponds to the current point of the target cargo; The third point corresponds to the current point of the transportation 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 further includes: When it is determined that the first driving path and the second driving path intersect, determining the target unmanned vehicle based on the load information, the vehicle condition information, the power consumption information of the plurality of unmanned vehicles performing the task of transporting the target cargo, and the time window information of the plurality of 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 any one of claims 1 to 4, 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: Based on the duration information, weather information, and / or road condition information corresponding to the remaining road segments, the target driving path is modified and / or the speed of the target unmanned vehicle is regulated so that the target cargo arrives at the second location before the deadline.

6. 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 based on the target information; a control unit, configured to control the target unmanned vehicle to move toward target cargo based on the target unmanned vehicle and the target driving path; The acquiring of target information based on the target Internet of Things includes: acquiring the target information according to a dynamic time window; wherein the duration of the dynamic time window is negatively correlated with the congestion and / or complexity of the road section; Determining the target unmanned vehicle and the target driving path according to the target information includes: Determining, based on the target information, multiple driving paths for the target unmanned vehicle to perform the task of transporting the target cargo; Determining traffic signal change characteristics, congestion characteristics, and / or road condition characteristics of the plurality of driving routes based on the target Internet of Things; Determining the power consumption corresponding to the plurality of driving paths and / or the time of arrival at the second point based on the traffic signal change characteristics, the congestion characteristics, and / or the road condition characteristics; The second point corresponds to the target point of the target cargo; determining the target driving path according to the power consumption and / or the time of reaching the second point; Determining 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; determining the traffic signal change characteristics, the congestion characteristics, and / or the road condition characteristics based on the driving characteristics; The power consumption is determined according to the following formula: ; in, is the power consumption corresponding to the driving path, is the air resistance coefficient, is the rolling resistance coefficient, is the friction loss coefficient, is the fixed energy consumption of the unmanned vehicle at rest, is the average speed, is the congestion factor, is the energy loss caused by the traffic signal; the time of reaching the second point is determined according to the following formula: ; in, is the time when the second point is reached, is the corresponding distance of the driving path, is the maximum average speed that an autonomous vehicle can achieve without traffic congestion, is the congestion factor, is the road condition factor, is the waiting time due to traffic signals.

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