An Internet of Things-based smart city traffic management method and system, and a medium

Abstract

The embodiment of the present specification provides a smart city traffic management method and system based on Internet of Things, and a medium. The method is applied to a traffic management platform, and includes: obtaining power supply detection information of a traffic indication device through a sensor network platform; when the power supply detection information is a power-off state, predicting a congestion degree of a position where the traffic indication device is located within a target time period; and based on the congestion degree, determining whether temporary power supply is needed for the traffic indication device.

Description

[0001] Priority Statement

[0002] This specification claims priority to U.S. Patent Application No. 17663434, filed on May 16, 2022. The contents of which are incorporated herein by reference in their entirety. TECHNICAL FIELD

[0003] The present specification relates to the field of smart city, and in particular, to a smart city traffic management method and system based on Internet of Things and a medium. BACKGROUND

[0004] With the rapid development of social economy and the gradual increase of urban population, the urban transportation network is becoming increasingly developed. The urban traffic information system has become an indispensable part of improving the quality of life of residents. The urban traffic information system can provide traffic information for residents through traffic indication devices, which are generally powered by city power supply. When city power supply problems occur (such as partial area circuit maintenance), it may cause traffic indication devices to be unable to work and thus affect urban traffic.

[0005] Therefore, it is necessary to propose a smart city traffic management method and system based on Internet of Things to reduce the impact of traffic indication devices on urban traffic when they are powered off. SUMMARY

[0006] One of the embodiments of the present specification provides a smart city traffic management method based on Internet of Things, applied to a traffic management platform, the traffic management method comprising: obtaining power supply detection information of a traffic indication device through a sensor network platform; when the power supply detection information is in a power-off state, predicting the congestion degree of the location of the traffic indication device within a target time period; and determining whether temporary power supply is needed for the traffic indication device based on the congestion degree.

[0007] One of the embodiments of the present specification provides a smart city traffic management system based on Internet of Things, the system comprising a traffic management platform, a sensor network platform and a monitoring object platform, the traffic management platform being configured to perform the following operations: obtaining power supply detection information of a traffic indication device through a sensor network platform; when the power supply detection information is in a power-off state, predicting the congestion degree of the location of the traffic indication device within a target time period; and determining whether temporary power supply is needed for the traffic indication device based on the congestion degree.

[0008] One of the embodiments of the present specification provides a computer readable storage medium, the storage medium storing computer instructions, when the computer reads the computer instructions in the storage medium, the computer executes the smart city traffic management method based on Internet of Things of any one of the preceding embodiments.

[0009] When the traffic sign is powered off, the embodiments of this specification can predict the congestion level at the location of the traffic sign during a target time period, generate detour prompts in a timely manner, and send them to the target terminal. This accelerates the dissemination of information related to the power outage of the traffic sign, reduces the traffic burden at the relevant intersections, and lowers the degree of traffic congestion and the probability of vehicles being stuck in congestion. Attached Figure Description

[0010] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0011] Figure 1 These are schematic diagrams illustrating application scenarios of a traffic management platform based on some embodiments of this specification;

[0012] Figure 2 This is a schematic diagram of a smart city traffic management system according to some embodiments of this specification;

[0013] Figure 3 This is an exemplary flowchart of a traffic management method according to some embodiments of this specification;

[0014] Figure 4 This is a schematic diagram of a traffic network according to some embodiments of this specification;

[0015] Figure 5 This is a schematic diagram illustrating a method for determining congestion levels according to some embodiments of this specification;

[0016] Figure 6 This is an exemplary flowchart of a method for determining intersection throughput according to some embodiments of this specification;

[0017] Figure 7 This is an exemplary flowchart illustrating a temporary power supply method according to some embodiments of this specification. Detailed Implementation

[0018] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0019] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0020] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0021] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0022] Figure 1 These are schematic diagrams illustrating application scenarios of a traffic management platform based on some embodiments of this specification.

[0023] like Figure 1 As shown, the application scenarios of the traffic management platform 100 may include traffic instruction equipment 110, network 120, memory 130, processor 140 and terminal 150.

[0024] The traffic management platform 100 can determine the degree of congestion that may be caused by a power outage at each intersection by implementing the methods and / or processes disclosed in this specification based on the power supply detection information of the traffic indicator equipment at each intersection, and further determine whether to provide temporary power to the traffic indicator equipment.

[0025] Traffic guidance device 110 can refer to a device that can issue action instructions to various vehicles and pedestrians traveling on the road, such as traffic lights installed at intersections. In some embodiments, the traffic guidance device may be equipped with a power supply detection device (not shown) for acquiring power supply detection information of the traffic guidance device. In some embodiments, the traffic guidance device 110 may also include a camera device (not shown), such as a webcam, for acquiring road video to determine the congestion situation at the intersection. In some embodiments, the traffic guidance device 110 can communicate and exchange data with a memory 130, a processor 140, and a terminal 150 via a network 120.

[0026] Network 120 may include any suitable network that facilitates information and / or data exchange between traffic management platform 100. One or more components of traffic management platform 100 (e.g., traffic sign 110, memory 130, processor 140, terminal 150) may exchange information and / or data via network 120. For example, network 120 may send power supply monitoring information of traffic sign devices obtained from a monitoring platform to the traffic management platform. In some embodiments, network 120 may be any one or more of wired or wireless networks. In some embodiments, network 120 may include one or more network access points. For example, network 120 may include wired or wireless network access points. In some embodiments, the network may be a point-to-point, shared, centralized, or other topologies, or a combination of multiple topologies.

[0027] The memory 130 can be used to store data, instructions, and / or any other information. In some embodiments, the memory 130 can store data and / or information obtained from, for example, traffic indication device 110, processor 140, etc. For example, the memory 130 can store road video, power supply detection information, etc. In some embodiments, the memory 130 can be located within the processor 140. In some embodiments, the memory 130 can include mass storage, removable storage, etc., or any combination thereof.

[0028] Processor 140 can process data and / or information obtained from other devices or various components of traffic management platform 100. In some embodiments, processor 140 can be directly connected to or connected via network 120 to traffic indication device 110, memory 130, and terminal 150 to access information and / or data. For example, processor 140 can obtain power detection information from traffic indication device 110 and / or memory 130. In some embodiments, processor 140 can process data and / or information obtained from traffic indication device 110. For example, processor 140 can determine the traffic volume at an intersection based on road video obtained from traffic indication device 110. In some embodiments, processor 140 can be a single server or a group of servers. Processor 140 can be local or remote. Processor 140 can be implemented on a cloud platform.

[0029] Terminal 150 may refer to one or more terminal devices or software used by a user. In some embodiments, terminal 150 may be a mobile device, tablet computer, laptop computer, or any combination thereof. In some embodiments, terminal 150 may interact with other components in traffic management platform 100 via network 120. For example, terminal 150 may receive road congestion levels and detour information sent by traffic management platform 100. In some embodiments, terminal 150 may be a terminal device or software used by rescue personnel.

[0030] An Internet of Things (IoT) system is an information processing system that includes some or all of the following platforms: a management platform, a sensor network platform, and an object platform. The management platform coordinates and manages the connections and collaboration between various functional platforms (such as the sensor network platform and the object platform), aggregating information from the IoT operating system and providing sensing, management, and control functions. The sensor network platform connects the management platform and the object platform, performing sensing and communication functions for both sensing and control information. The object platform is the functional platform for generating sensing information and executing control information.

[0031] Information processing in an IoT system can be divided into two processes: processing of sensing information and processing of control information. Control information can be generated based on sensing information. Sensing information processing involves the object platform acquiring the sensing information and transmitting it to the management platform via the sensor network platform. Control information, on the other hand, is distributed from the management platform to the object platform via the sensor network platform, thereby enabling control of the corresponding objects.

[0032] In some embodiments, when an IoT system is applied to urban management, it can be referred to as a smart city IoT system.

[0033] Figure 2 This is a schematic diagram of a smart city traffic management system according to some embodiments of this specification. For example... Figure 2 As shown, the smart city traffic management system 200 can be implemented based on an Internet of Things (IoT) system. The smart city traffic management system 200 includes a traffic management platform 210, a sensor network platform 220, and a monitoring object platform 230. In some embodiments, the smart city traffic management system 200 can be part of or implemented by the processor 140.

[0034] In some embodiments, the smart city traffic management system 200 can be applied to various traffic management scenarios. In some embodiments, the smart city traffic management system 200 can acquire power supply detection information of traffic indication devices in various scenarios to obtain traffic management strategies for each scenario. These various traffic management scenarios may include scenarios such as passage, parking, and detours. It should be noted that the above scenarios are merely examples and do not limit the specific application scenarios of the smart city traffic management system 200. Those skilled in the art can apply the smart city traffic management system 200 to any other suitable scenario based on the content disclosed in this embodiment.

[0035] In some embodiments, the smart city traffic management system 200 can be applied to road operation management. When applied to road operation management, the monitoring object platform 230 can be used to collect power supply detection information of traffic signage equipment, such as whether the traffic signage equipment is in a power outage state; the monitoring object platform 230 can upload the collected power supply detection information of the traffic signage equipment to the sensor network platform 220. The sensor network platform 220 can summarize and process the collected data, such as classifying the collected data by region, by equipment model, or by service life. The sensor network platform 220 then uploads the further summarized and processed data to the traffic management platform 210. Based on the processing of the collected data, the traffic management platform 210 makes policies or instructions related to traffic management in the area where the traffic signage equipment in a power outage state is located, such as power supply instructions for the traffic signage equipment and issuing detour information.

[0036] The following will use the application of the Smart City Traffic Management System 200 in the power supply prediction management scenario as an example to give a detailed explanation of the Smart City Traffic Management System 200.

[0037] Traffic management platform 210 can refer to a platform for managing traffic in a city. In some embodiments, traffic management platform 210 can be a management platform. Traffic management platform 210 can be configured to acquire power supply detection information of traffic signage devices through sensor network platform 220; when the power supply detection information indicates a power outage, predict the congestion level at the location of the traffic signage devices within a target time period; and determine, based on the congestion level, whether temporary power supply to the traffic signage devices is needed.

[0038] In some embodiments, the traffic management platform 210 can also acquire the location information of traffic signage devices; process the location information based on a prediction model to determine the congestion level at the location of the traffic signage devices within a target time period. In some embodiments, the location information includes at least one of the road type at the location of the traffic signage devices, the surrounding road environment, and road network information within a preset range.

[0039] The sensor network platform 220 can refer to a platform that uniformly manages information communication. In some embodiments, the sensor network platform can connect to a traffic management platform and a monitoring object platform to realize the functions of power supply detection information communication and control information sensing communication.

[0040] The monitoring object platform 230 can refer to a platform that acquires power supply detection information of traffic signage equipment obtained by the power supply detection device.

[0041] Figure 3This is an exemplary flowchart illustrating a traffic management method according to some embodiments of this specification. In some embodiments, process 300 may be executed by a traffic management platform 210. Figure 3 As shown, process 300 includes the following steps:

[0042] Step 310: Obtain power supply detection information of traffic signage equipment through the sensor network platform.

[0043] Traffic guidance devices can be used to generate traffic signals, which can be used to inform vehicles and pedestrians of the traffic rules for the current road segment. For example, traffic guidance devices can be installed in all directions at a road intersection (also called a junction), allowing pedestrians to determine the current traffic rules at the intersection based on the traffic lights and other markings on the traffic guidance devices. In some embodiments, traffic guidance devices may also include other guidance devices for providing traffic assistance information. For example, traffic guidance devices may also include LED display boards for showing road conditions.

[0044] In some embodiments, the traffic management platform can communicate with other IoT platforms and related devices of the smart city traffic management system through a sensor network platform (such as sensor network platform 220). For example, the traffic management platform can communicate with traffic indication devices through a sensor network platform (such as sensor network platform 220). The traffic management platform can obtain the power supply status of the traffic indication devices through the sensor network platform and control the traffic indication signals of each traffic indication device to control the city's traffic situation.

[0045] Power supply detection information can include the power supply status of traffic indicator equipment, which may include a power outage state. When traffic indicator equipment is in a power outage state, it cannot generate traffic signals. For example, when the power supply system of the traffic indicator equipment fails, the traffic indicator equipment is in a power outage state. Another example is when the traffic indicator equipment is powered by an external battery, and the external battery runs out of power, the traffic indicator equipment is in a power outage state.

[0046] In some embodiments, the power supply status of traffic indicator devices may further include a fully powered state and a temporary power supply state. When the traffic indicator device is in a fully powered state, it is powered by its internal power supply circuit, and the device operates normally. The internal power supply circuit can be connected to smart city power supply systems such as the city's power grid and the power grids of surrounding residential areas. When the traffic indicator device is in a temporary power supply state, it is powered by its external power supply circuit, and the device operates normally. The external power supply circuit can be connected to an external power source, which may include devices such as lithium batteries, solar cells, or storage batteries.

[0047] In some embodiments, power supply detection information can be determined by assessing the power supply status of the traffic indicator device. In other embodiments, the power supply status of the traffic indicator device can be determined based on the power supply status of the power supply network to which the traffic indicator device is located. For example, the traffic indicator device may be connected to and powered by the city's power supply network. When a portion of the city's power supply network experiences a power outage, the corresponding outage area can be determined first based on that portion of the network. Then, the traffic indicator devices in a power outage state can be identified based on the outage area to generate power supply detection information. This power supply detection information may include the traffic indicator devices in a power outage state.

[0048] In some embodiments, the power supply status of traffic signage can be detected by a power supply detection device. The power supply detection device can send the detected power supply status of the traffic signage to a monitoring platform, which can then determine the power supply detection information. In other words, the monitoring platform can obtain the power supply detection information of the traffic signage detected by the power supply detection device.

[0049] A monitoring platform (e.g., monitoring platform 230) can communicate with a power supply detection device and can be used to monitor the power supply status of at least one traffic sign device. In some embodiments, the monitoring platform can be set up in various road areas of a city to monitor the power supply status of each traffic sign device in that road area. When the power supply detection devices of each traffic sign device send their power supply status to the monitoring platform, the monitoring platform can generate power supply detection information based on the power supply status of each traffic sign device in that road area. In some embodiments, the monitoring platform can send the acquired power supply detection information to a traffic management platform (e.g., traffic management platform 210) through a sensor network platform (e.g., sensor network platform 220). That is, the traffic management platform can access the monitoring platform through the sensor network platform and obtain the power supply detection information of the traffic sign devices detected by the power supply detection device on the monitoring platform.

[0050] A power supply detection device can be used to detect the power supply status of traffic signage. Specifically, the device can generate a signal based on the power supply status of the traffic signage and send it to the monitoring platform. In some embodiments, the power supply detection device may have an internal power supply, and its operation is unaffected by the power supply status of the traffic signage.

[0051] In some embodiments, a power supply detection device can be installed on the traffic indicator equipment and connected to the power supply circuit of the traffic indicator equipment. The power supply detection device can generate a signal based on the voltage changes in the power supply circuit. The form of the signal can be determined according to actual needs. For example, when the traffic indicator equipment is in a power-off state, the voltage of the power supply circuit becomes 0, and the power supply detection device can generate a pulse signal in response to the voltage change of the power supply circuit and send it to the monitoring platform.

[0052] In some embodiments, after receiving a signal, the monitoring platform can identify the traffic indicator device based on its connection to the power supply detection device (e.g., the port where the signal was received), and determine the power supply status of the traffic indicator device based on the type of signal. For example, when the monitoring platform receives a pulse signal, it can determine that the traffic indicator device at the source of the signal is in a power-off state.

[0053] Step 320: When the power supply detection information indicates a power outage, predict the congestion level at the location of the traffic indicator device within the target time period.

[0054] A power outage status detected by the power supply detection system indicates that the traffic signage equipment is currently powered off. For example, when the traffic signage equipment suddenly loses power, the monitoring platform can receive a signal from the power supply detection device, thereby generating power supply detection information indicating that the traffic signage equipment is powered off.

[0055] In some embodiments, the power supply detection information may also include the future power supply status of the traffic indicator equipment. For example, future power outage areas can be determined based on power outage plans (such as circuit maintenance plans) from relevant power supply departments, and then the power supply status of each traffic indicator equipment in the future time period can be determined based on the power outage areas and the location of the traffic indicator equipment. As another example, when the traffic indicator equipment is powered by an external power source, the power depletion time can be determined based on the amount of power supplied by the external power source, thereby determining the time during which the traffic indicator equipment will be in a power outage state.

[0056] The location of a traffic sign can refer to the intersection where the traffic sign is located. In some embodiments, the location of the traffic sign can include the various directions of the intersection. For example, when the traffic sign is located at a crossroads, the location can include all four directions of the crossroads.

[0057] The target time period can be a period of time after the traffic signage begins to be powered off. For example, the target time period could be one hour after the traffic signage is powered off. Another example is the estimated power outage time for the traffic signage; for instance, the estimated power outage time can be determined based on a power outage notice from the relevant power supply department. Yet another example is a specific time period after the traffic signage is powered off. For instance, the target time period could be peak traffic hours after the power outage (such as morning rush hour, evening rush hour, etc.).

[0058] Congestion level can be used to describe the degree of traffic congestion caused by a power outage at an intersection. Traffic congestion refers to the impact of traffic events (such as power outages to traffic signage) on vehicle traffic demand. Vehicles are forced to reduce speed or stop, resulting in a traffic backlog exceeding a certain level. In some embodiments, congestion level can be described by congestion grades. For example, congestion grades can include Level I congestion, Level II congestion, Level III congestion, and Level IV congestion. Level I congestion can represent severe congestion, Level II congestion can represent moderate congestion, Level III congestion can represent mild congestion, and Level IV congestion can represent unobstructed traffic.

[0059] In some embodiments, the degree of congestion can be determined by traffic congestion indicators. For example, when the traffic congestion indicators meet preset conditions, the congestion level is determined to be at the corresponding congestion level. For instance, traffic congestion indicators may include average vehicle delay at intersections. When the average vehicle delay at an intersection is less than 35 seconds, the congestion level of the intersection is Level IV; when the average vehicle delay at an intersection is greater than 35 seconds but less than 50 seconds, the congestion level is Level III; when the average vehicle delay at an intersection is greater than 50 seconds but less than 70 seconds, the congestion level is Level II; and when the average vehicle delay at an intersection is greater than 70 seconds, the congestion level is Level I.

[0060] In some embodiments, traffic congestion indicators may include at least one of the following: average vehicle delay at intersection, queue length, intersection queuing time, queue overflow, and queue time index. Queue length may refer to the length of a vehicle queue from the intersection stop line or the start of the queue to the end of the queue. Intersection queuing time may refer to the time taken for vehicles in a queue at an intersection to pass the stop line of the approach lane from the moment they first stop. The queue time index may refer to the ratio of vehicle queuing time at a signalized intersection to the signal control cycle. Queue overflow may refer to the traffic phenomenon where vehicle queues at downstream intersections extend to upstream intersections.

[0061] In some embodiments, the congestion level of an intersection can be calculated by collecting data from traffic indicator equipment located in a power-off state. For example, the average vehicle delay time of each approach lane at the intersection can be collected over a certain time interval to determine the maximum average vehicle delay at the intersection. In some embodiments, the data collection method, collection time, and congestion level calculation method can be determined according to relevant national standards. For example, relevant provisions in the "Road Traffic Congestion Evaluation Method" (GA / T 115-2020) and the "Urban Traffic Operation Status Evaluation Specification" (GB / T 33171-2016) can be referenced.

[0062] In some embodiments, the congestion level at the location of the traffic indicator device in a power-off state (such as an intersection) can be estimated based on historical traffic data and / or real-time traffic data from relevant intersections. For example, traffic data at the location of the traffic indicator device in a power-off state can be estimated based on real-time traffic data (such as throughput) from nearby intersections that are not experiencing power outages, and then the congestion level at that location can be determined based on the estimated traffic data.

[0063] In some embodiments, machine learning methods can be used to predict the congestion level at the location of traffic indication equipment within a target time period; that is, congestion levels can be predicted based on a predictive model. For details regarding the predictive model, please refer to [link / reference needed]. Figure 5 Related descriptions.

[0064] Step 330: Based on the level of congestion, determine whether temporary power supply is needed for the traffic signage equipment.

[0065] Temporary power supply refers to supplying power to traffic signage equipment via an external power source. For example, an external power source can be connected to the power supply circuit of the traffic signage equipment, thereby supplying power to the equipment via a temporary power supply device. The temporary power supply device may include an external power source. Exemplarily, the external power source may include a generator, a battery, etc. In some embodiments, the temporary power supply device may also include alternative traffic signage equipment installed at intersections experiencing power outages, such as solar-powered traffic signage equipment or traffic signage equipment with built-in power supplies.

[0066] In some embodiments, when the level of congestion or related parameters meet preset conditions, it can be determined that temporary power supply is needed for the traffic indication equipment; otherwise, temporary power supply is not needed. In some embodiments, the preset conditions may be preset thresholds. When the level of congestion or the level of congestion exceeds the preset threshold, it can be determined that the level of congestion or related parameters meet the preset conditions. For example, the preset conditions may include that the congestion level of the intersection is Level I congestion.

[0067] In some embodiments, the power supply status of each road network node in the traffic network can be determined based on the power supply status of the traffic indication equipment. For example, the power supply status of each traffic indication equipment can be determined based on the power supply detection information obtained in step 310, thereby determining the power supply status of each road network node. Figure 4 As shown, a transportation network can be composed of road network nodes (intersections). Figure 4 The road network nodes can include nodes A to N (or intersections A to N) and the connections between them. A node can represent an intersection in the traffic network, and the connections between nodes can indicate the existence of a connecting road between intersections. A connecting road can be characterized as two intersections being on the same road or separated by a distance less than a threshold. For example, the road from intersection D to intersection E can be denoted as road DE. When the traffic indicator equipment at nodes E, F, G, J, and K is in a power-off state, it can be determined that nodes E, F, G, J, and K are intersections in a power-off state (intersections where the associated traffic indicator equipment is located in a power-off state), and in... Figure 4 It is depicted using dashed lines.

[0068] In some embodiments, whether to supply power to the traffic signage devices can be determined based on the correlation between the intersections where the traffic signage devices are located. For example, a temporary power supply device can supply power to intersections connected to multiple traffic signage devices that are currently without power. For example, such as... Figure 4 In the traffic network shown, intersections E, F, G, J, and K are in a state of power outage. Intersection F is connected to the three intersections in a state of power outage, so the traffic signage equipment at intersection F can be temporarily powered.

[0069] In some embodiments, when implementing the traffic management methods provided in some embodiments of this specification, neighboring intersections of the intersection in a power outage state are also considered. For example, the congestion index of the intersection in a power outage state can be determined based on neighboring intersections. In some embodiments, neighboring intersections can be intersections whose traffic conditions are affected by traffic signs that are in a power outage state. For example, neighboring intersections can be intersections where traffic signs are located around the traffic signs that are in a power outage state; that is, intersections on the same road or at a distance of less than a threshold can be determined as neighboring intersections based on the intersections where the traffic signs are located. For example, nodes adjacent to the intersection can be designated as neighboring intersections based on the positions of nodes E, F, G, J, and K; that is, nodes A, B, C, D, H, I, L, M, and N represent neighboring intersections of the intersection in a power outage state.

[0070] In some embodiments, temporary power supply may include powering the traffic indication device via an external power source (such as a battery) carried by the drone. For details regarding power supply via an external power source carried by the drone, please refer to this specification. Figure 7And related content.

[0071] According to some embodiments of the traffic management method provided in this specification, the power outage status of traffic indicator equipment can be obtained in a timely manner, and the equipment can be powered on according to the congestion situation. This enables timely maintenance of traffic conditions, ensuring that the power outage of traffic indicator equipment does not affect the normal passage of the traffic network, and thus preventing the power outage of traffic indicator equipment from further impacting traffic (such as increasing congestion levels, intersection queuing time, etc.).

[0072] In some embodiments, the processor 140 can also adjust the signal strategy of the relevant traffic indicator device in response to the traffic indicator device being in a power-off state and its congestion situation. The relevant traffic indicator device can be a traffic indicator device adjacent to the traffic indicator device in a power-off state. For example, the relevant traffic indicator device can be a traffic indicator device within 5 km of the traffic indicator device in a power-off state. The signal strategy can include the duration of each traffic control information signal of the traffic indicator device. Exemplarily, in... Figure 4 In the traffic network shown, based on node G being in a power-off state, the signal strategies of traffic indication devices at nodes C and L can be controlled. For example, the time interval between the green light in the CG direction at node C and the green light at node L can be determined based on the congestion situation at node G. Exemplarily, the time interval between the green light in the CG direction and the green light at node L can be determined based on the length of road CL, the maximum average vehicle delay at node G, and the average vehicle speed. Then, the minimum time interval between the green light in the CG direction and the green light at node L can be determined based on the signal cycle, and the signal strategies at nodes C and L can be controlled based on this time interval.

[0073] like Figure 3 As shown, some embodiments of the traffic management method provided in this specification may also include steps related to generating detour prompts to remind users to detour around the power outage area.

[0074] Step 340: Generate detour suggestion information based on the level of congestion. In some embodiments, step 340 can be performed by the traffic management platform 210.

[0075] The target terminal can refer to the device on which the driver receives detour instructions. For example, the target terminal can be a mobile terminal; exemplarily, it can include the driver's smart terminal, vehicle navigation device, etc. In some embodiments, the target terminal may also include related indicator devices for providing traffic assistance information; for example, the target terminal may include LED display devices at intersections.

[0076] In some embodiments, target terminals within a preset range can be determined based on their locations. For example, a traffic management platform can obtain the locations of each terminal connected to the platform, determine the terminals within a preset range as target terminals based on their locations, and send detour prompts to the target terminals.

[0077] In some embodiments, once the traffic management platform identifies the target terminal, it can send detour prompts to the target terminal via a sensor network platform. For example, the traffic management platform can determine the device ID of the target terminal and then send the detour prompts to the port corresponding to the device ID via the sensor network platform.

[0078] According to some embodiments of this specification, the traffic management method can generate detour prompts in a timely manner and send them to the target terminal, thereby accelerating the dissemination of information about traffic indication equipment being in a power-off state and reducing the traffic burden at relevant intersections.

[0079] It should be noted that the above description of process 300 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 300 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, the duration of the power outage after the external power supply is depleted can be estimated based on the duration of the external power supply, and power supply detection information can be generated to repeatedly execute the traffic management method provided in this specification.

[0080] Figure 5 This is a schematic diagram illustrating a method for determining congestion levels according to some embodiments of this specification.

[0081] Step 510: Obtain the location information of the traffic indication device. In some embodiments, step 510 may be performed by the monitoring object platform 230.

[0082] Location information may be information relating to the location of the traffic indication device. In some embodiments, the traffic indication device may be a traffic indication device that is in a power-off state.

[0083] In some embodiments, when the monitoring object platform 230 determines that a traffic indicator device is in a power-off state, the monitoring object platform 230 automatically detects the location information of the traffic indicator device and writes the location information of the traffic indicator device into the power supply detection information of the traffic indicator device, and sends it to the traffic management platform 210 through the sensor network platform 220.

[0084] In some embodiments, location information may include at least one of the following: road type, surrounding environment, and road network information within a preset range. For example, road type may include highways, Class I highways, Class II highways, Class III highways, Class IV highways, expressways, arterial roads, secondary arterial roads, local roads, lanes, factory / mining roads, forest roads, rural roads, etc., or any combination thereof. The surrounding environment may include whether the road where the traffic indicator is located (even if it is powered off) includes certain special road sections. In some embodiments, special road sections may include school zones, accident-prone zones, park zones, tourist zones, etc. In some embodiments, location information may also include surveillance information for that location, such as surveillance video of the location of the traffic indicator.

[0085] The road network information within the preset range can be a road network data structure of different functions, levels, and locations within the preset range. The road network information within the preset range can include road network information covering the area formed by connecting multiple intersections where traffic signage equipment is currently powered off. For example... Figure 4 As shown, the traffic signage at intersections E, F, G, J, and K is in a power-off state. The road network information within the preset range can be the road network information of the area formed by connecting E, F, G, J, and K. The road network information within the preset range can also include the road network information of the area formed by extending one intersection outward from the intersection where the power-off traffic signage is located. For example... Figure 4 As shown, when the traffic indicator equipment at intersections E, F, G, J, and K is in a power-off state, the road network information within the preset range can also be the road network information of the range formed by connecting A to N.

[0086] In some embodiments, the preset range may be obtained by magnifying the area formed by the traffic indicator device in a power-off state, and the magnification factor may vary. In some embodiments, the preset range may also be a range within a preset threshold distance from the traffic indicator device in a power-off state. In some embodiments, the preset threshold may vary.

[0087] Step 520: Process the location information based on the prediction model to determine the congestion level at the location of the traffic indicator device within a target time period. In some embodiments, the congestion level at the location of the traffic indicator device within a target time period can be determined using a prediction model. The prediction model is used to predict the congestion level within a target time period based on location information.

[0088] In some embodiments, the prediction model can be a deep neural network (DNN) model. Correspondingly, the input to the prediction model can be the location information of the traffic sign, and the output of the prediction model can be the congestion level at the location of the traffic sign that is in a power-off state.

[0089] The prediction model can be trained based on the historical location information of traffic signage devices and their corresponding historical congestion levels. For example, training samples can be historical location information from multiple sample time points, and the labels of the training samples are the historical congestion levels of the intersection where the traffic signage device is located during a power outage within a historical target time period. The historical location information from multiple sample time points can refer to the locations of traffic signage devices in a power outage state in the historical data of the urban traffic network; the historical congestion level within the historical target time period can be the congestion level at the location of the traffic signage device during the power outage period; the historical congestion level within the target time period can be manually labeled by the user or determined based on historical prediction results. During training, samples can be input into the initial prediction model, and the model output and sample labels can be input into the loss function. The parameters of the initial prediction model are iteratively updated based on the loss function until preset conditions are met and training is complete, resulting in a trained prediction model. The preset conditions can be that the loss function is less than a threshold, convergence, or the training period reaches a threshold.

[0090] Some embodiments in this specification can determine the congestion level at intersections where traffic signage is located when it is in a power-off state based on a prediction model. This can better reflect the nonlinear relationship between the location information of traffic signage and the congestion level at intersections, thereby improving the prediction accuracy of intersection congestion levels.

[0091] In some embodiments, the prediction model can be a graph neural network (GNN). Correspondingly, the input to the prediction model can be graph data in the graph theory sense, and the output can be a predicted value of the congestion level of at least one node in the graph data.

[0092] The graph data can be a data structure composed of nodes and edges / paths. The graph data can include multiple nodes and multiple edges / paths connecting these nodes, thereby describing the characteristics of the locations of traffic signage devices and the characteristics of the roads between traffic signage devices. In the graph data, nodes correspond to the locations of traffic signage devices (e.g., intersections), and edges can correspond to roads between the locations of two traffic signage devices.

[0093] Combination Figure 4 The traffic network shown can be used to input map data when the traffic indicator equipment at intersections E, F, G, J, and K is in a power-off state. This data can include the characteristics of intersections E, F, G, J, and K as well as the characteristics of the roads between the intersections.

[0094] In some embodiments, graphical data for input prediction models can be generated based on the location information of various traffic signage devices within a preset range of road network information. For example... Figure 4The traffic network shown can be further divided into road network information within a preset range and road network information within a range connecting A to N. The input graph data can include the features of intersections A to N and the features of the roads between them. Each intersection can correspond to a node in the graph data, and the roads between intersections can correspond to edges in the graph data.

[0095] In some embodiments, the output of the prediction model may be a predicted level of congestion at the location of a traffic signage device that is currently experiencing a power outage in the graph data. For example... Figure 4 The traffic network shown can be analyzed using a prediction model when the input graph data consists of intersections A through N. If the traffic signage at intersections E, F, G, J, and K is powered off, the prediction model can output predicted congestion levels for intersections E, F, G, J, and K. In some embodiments, the output of the prediction model can also be predicted congestion levels at the locations of the traffic signage at each node in the graph data.

[0096] The location of traffic signage can be described by the characteristics of its nodes. Node characteristics can include the throughput of vehicles at the intersection, i.e., the number of vehicles passing through that node to another node. For example, node E's characteristics can include the throughput of nodes EA, DE, EF, and EJ. Node relationships can include the proximity and positional relationships between two nodes. For instance, edge DE can indicate that node D is adjacent to node E, and the length of edge DE can describe the distance from node D to node E. For more details on throughput, please refer to [link to relevant documentation]. Figure 6 Related descriptions.

[0097] The characteristics of roads between traffic signage devices can be described by the features of their edges. Edge features can include road length, intersections at both ends of the road, and other relevant information. For example... Figure 4 The traffic network shown can be characterized by road DE, which may include the intersections at both ends of the road (intersection D and intersection E), the direction of the road (road DE represents the road between node D and node E), and the length of the road.

[0098] Graph neural networks are a type of neural network that operates directly on a graph. Based on information propagation mechanisms, each node in the graph exchanges attribute information with each other through edges, thereby continuously updating its node information until a stopping condition is met.

[0099] Graph neural network-based prediction models can be trained using historical graph data. Correspondingly, training samples can be historical graph data, which may include historical location information of traffic signs during power outages and related traffic sign information. For more information on training graph neural network-based prediction models, please refer to the aforementioned content on prediction model training; it will not be repeated here.

[0100] Some embodiments of this specification can determine the congestion level of relevant intersections based on graph data including traffic indication devices in a power-off state, which can better reflect the correlation between various intersections and thus improve the prediction accuracy of intersection congestion level.

[0101] Figure 6 This is an exemplary flowchart of an intersection throughput determination method according to some embodiments of this specification. In some embodiments, process 600 may be executed by a traffic management platform 210. Figure 6 As shown, process 600 includes the following steps:

[0102] Step 610: Obtain the traffic volume in each direction at the location of the traffic indicator device within a preset time period.

[0103] Throughput, also known as traffic volume, refers to the traffic flow through a specific cross-section of a road per unit time, i.e., the number of vehicles passing through that cross-section per unit time. In some embodiments, a specific cross-section of a road can be the location of various traffic indicator devices at an intersection (such as a zebra crossing). For example, the throughput of node E can include the number of vehicles passing through the zebra crossing at the intersection leading to node D.

[0104] The preset time period can refer to the time period when the traffic indicator equipment is powered on and the time is similar to the power outage period. For example, when the traffic indicator equipment is powered off from 3 pm to 4 pm on a certain workday, the preset time period can be from 3 pm to 4 pm on the previous workday.

[0105] The traffic management platform can continuously acquire the throughput at the locations of various traffic indication devices. In some embodiments, the throughput can be determined by a counter, which can be installed at a counting location on the road (a cross-section of the road). In some embodiments, the counter mainly consists of a detector and a counter. The detector can include pneumatic, photoelectric, radar, ultrasonic, piezoelectric, and electromagnetic detectors. For example, the detector can include a piezoelectric detector installed at a zebra crossing. When a vehicle passes, the piezoelectric detector generates an electrical signal, and the counter can count in response to the electrical signal, thereby calculating the throughput at the intersection. In some embodiments, the throughput at an intersection can be determined by a camera device. That is, a camera can acquire video of the intersection over a preset time period, perform image recognition on the video of the intersection, and determine the throughput in each direction of the intersection based on the vehicle recognition results.

[0106] In some embodiments, the traffic volume determination model can also be used to process the surveillance video of the intersection acquired by the camera device to obtain the traffic volume in each direction of the intersection. The traffic volume determination model may include at least one machine learning model, and the input of the traffic volume determination model may be, for example, the surveillance video of the intersection (or an image sequence of the intersection), and the output may be the traffic volume in each direction of the intersection.

[0107] In some embodiments, the throughput determination model may include a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). When the intersection's surveillance video is input into the throughput determination model, the CNN can first process the video to obtain image features of each video frame, and then input these features into the RNN. The RNN then determines the throughput of the intersection in each direction based on the image features of each video frame. In some embodiments, the image features of the video frames may include vehicle features of the intersection in each direction within the video frame. Vehicle features may include the vehicle's position and image features in the current video frame. The RNN can track and determine vehicle trajectories based on these vehicle features, and count the number of vehicle trajectories in each direction as the throughput of the intersection. The throughput of the intersection is adjusted when new vehicle trajectories appear in a consecutive frame sequence.

[0108] In some embodiments, an initial throughput determination model can be trained to obtain a trained throughput determination model. Training samples can be surveillance images of historical roads, and training sample labels can be the throughput of each intersection in the surveillance images of historical roads. The labels of the training samples can be manually assigned. During training, samples can be input into the initial throughput determination model, and the model output and sample labels can be input into a loss function. The parameters of the initial throughput determination model are iteratively updated based on the loss function until preset conditions are met, resulting in a trained throughput determination model. The preset conditions can be that the loss function is less than a threshold, convergence, or the training period reaches a threshold.

[0109] Step 620: Based on the traffic volume in each direction at the intersection before the power outage, estimate the traffic volume in each direction at the intersection after the power outage. In some embodiments, step 620 can be performed by the traffic management platform 210.

[0110] When a power outage occurs at an intersection, the traffic volume may decrease compared to the normal traffic volume. In some embodiments, the reduction in traffic volume can be estimated based on the road type. For example, the traffic volume during a preset time period before the power outage can be denoted as T, the average reduction in traffic volume can be denoted as R, and the reduction coefficient can be denoted as α. Then, the traffic volume during the preset time period after the power outage is T′=T-αR, where α is a coefficient related to the intersection type and greater than 0, and R can be a preset value.

[0111] In some embodiments, the specific value of the reduction coefficient α can be set according to the road type. For example, when the intersection is a crossroads, the value of α can be relatively large (e.g., α = 1); if the intersection is a pedestrian crossing set in the road, the value of α can be relatively small (e.g., α = 0.2). In some embodiments, the average reduction value R of the throughput can be determined by statistically analyzing the average reduction value of the intersection's throughput after a power outage in historical data.

[0112] In some embodiments, the recording time of traffic volume before the power outage and the duration of the power outage can affect the estimated traffic volume. For example, if the recording time of traffic volume before the power outage is 3-4 PM, and the traffic signage system experiences a power outage between 5-7 PM, considering that the power outage occurs during the evening rush hour, the corresponding traffic volume is theoretically greater than the traffic volume before the power outage. In some embodiments, this can be determined by a time variation coefficient β, i.e., the vehicle traffic volume T′ = βT within a preset time period after the power outage. In some embodiments, the time variation coefficient β can be related to the recording time of traffic volume before the power outage and the duration of the power outage. For example, by using the historical traffic volume of the intersection in various time periods, after the power outage, the corresponding value (e.g., T) can be determined from the historical traffic volume based on the recording time of traffic volume before the power outage and the duration of the power outage. 前 T 后 If the time variation coefficient β = T, then 后 / T 前 .

[0113] In some embodiments, the aforementioned estimation method can be combined, for example, the vehicle throughput T′=βT-αR within a preset time period after a power outage.

[0114] The intersection throughput determination method provided in some embodiments of this specification takes into account the impact of power outages on intersection throughput, thereby improving the accuracy of predicting intersection congestion levels.

[0115] Figure 7 This is an exemplary flowchart illustrating a temporary power supply method according to some embodiments of this specification. In some embodiments, process 700 may be executed by a traffic management platform 210. Figure 7 As shown, process 700 includes the following steps:

[0116] Step 710: Obtain the location information of the drone, the location information of each traffic sign, and the congestion level of each traffic sign.

[0117] Drones can be unmanned aerial vehicles capable of performing power supply tasks, such as multi-rotor drones and industrial drones. Drones can be equipped with an external power source. When a drone approaches traffic signage that is currently powered off, the external power source can connect to the traffic signage, which is in a state of power failure.

[0118] The drone's location information may include its current location, such as its flight path or storage location in a warehouse. In some embodiments, the drone's location information can be determined by a positioning module installed on the drone. The positioning module can communicate with the traffic management platform 210 and send the drone's location information to the traffic management platform 210 in real time or periodically.

[0119] In some embodiments, the traffic management platform 210 can obtain the location information of the drone, the location information of each traffic sign, and the congestion level of each traffic sign through the network 120. For example, the traffic management platform 210 can obtain the congestion level of each traffic sign determined by the processor 140 through the network 120.

[0120] Step 720: Based on the location information of the UAV, the location information of each traffic sign device, and the congestion level of each traffic sign device, determine the power supply path for the UAV to supply power to each traffic sign device.

[0121] The drone can navigate along a power supply path, passing various traffic signage devices during its flight. In some embodiments, the power supply path can be determined using a path planning algorithm, which may include graph search, RRT, artificial potential field, and other algorithms.

[0122] In some embodiments, the power supply sequence can be determined based on the congestion level of each traffic sign, for example, based on the congestion level. The closer the congestion level is to Level I, the higher the priority of the power supply sequence. In some embodiments, for intersections with the same congestion level, the power supply path can be planned with the shortest possible power supply path as the planning objective.

[0123] In some embodiments, traffic signage devices in a power-off state can be grouped according to the location information of each traffic signage device and the location information of the drone, and a power supply path can be planned for each group. Each group of traffic signage devices may include traffic signage devices within a preset range.

[0124] Step 730: Based on the power supply path, control the drone to supply power to each traffic sign device.

[0125] Drones can navigate along power supply paths. When a drone passes the location of traffic signage devices that require power, it can place its onboard external power supply on the traffic signage device and connect the power supply circuit of the traffic signage device to the external power supply, so that the external power supply can power the traffic signage device.

[0126] In some embodiments, the drone may also carry alternative traffic signage devices. As the drone passes the locations of the traffic signage devices that require power, it can place the alternative traffic signage devices in the center of the intersection where the traffic signage devices require power.

[0127] According to some embodiments of this specification, a temporary power supply method can be provided to power traffic signage equipment using drones, thereby reducing the workload of staff.

[0128] This specification also provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the IoT-based smart city traffic management method described in any of the foregoing embodiments.

[0129] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0130] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0131] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0132] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0133] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0134] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0135] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A smart city traffic management method based on the Internet of Things, characterized in that, The method, applied to a traffic management platform, includes: Obtain power supply detection information for traffic signage equipment through a sensor network platform; When the power supply detection information indicates a power outage, the graph data is processed based on a prediction model to determine the predicted congestion level of at least one node in the graph data within a target time period. The graph data includes multiple nodes and edges connecting these nodes. The characteristics of the multiple nodes include vehicle throughput at the location of the traffic indicator device. The characteristics of the edges include the length of the road where the traffic indicator device is located, the two end intersections, and the road direction. The vehicle throughput is related to the throughput in each direction of the intersection before the power outage, the corresponding average decrease in throughput, the road type, the recording time of the throughput in each direction of the intersection before the power outage, and the power outage time. The throughput in each direction of the intersection before the power outage is determined using a throughput determination model based on the intersection's surveillance video within a preset time period. The throughput determination model is a machine learning model. Based on the level of congestion, determine whether temporary power supply is needed for the traffic signage equipment; wherein, the temporary power supply includes: Acquire the location information of the drone, the location information of the traffic signage device, and the congestion level of the traffic signage device; Based on the location information and the degree of congestion, determine the power supply path for the drone to supply power to the traffic signage equipment; Based on the power supply path, the drone is controlled to supply power to the traffic signage equipment.

2. The method according to claim 1, characterized in that, The process of obtaining power supply detection information for traffic indication devices through a sensor network platform includes: The system accesses the monitoring object platform through a sensor network platform and obtains the power supply detection information of the traffic indication equipment detected by the power supply detection device on the monitoring object platform.

3. A smart city traffic management system based on the Internet of Things, the system comprising a traffic management platform, a sensor network platform, and a monitoring object platform, wherein the traffic management platform is configured to perform the following operations: Obtain power supply detection information for traffic signage equipment through a sensor network platform; When the power supply detection information indicates a power outage, the graph data is processed based on a prediction model to determine the predicted congestion level of at least one node in the graph data within a target time period. The graph data includes multiple nodes and edges connecting the nodes. The nodes are characterized by vehicle throughput at the location of the traffic indicator, and the edges are characterized by the length of the road where the traffic indicator is located, the two end intersections, and the road direction. The vehicle throughput is related to the throughput in each direction of the intersection before the power outage, the corresponding average decrease in throughput, the road type, the recording time of the throughput in each direction of the intersection before the power outage, and the power outage duration. The throughput in each direction of the intersection before the power outage is determined using a throughput determination model based on the intersection's surveillance video within a preset time period; this throughput determination model is a machine learning model. Based on the level of congestion, determine whether temporary power supply is needed for the traffic signage equipment; wherein, the temporary power supply includes: Acquire the location information of the drone, the location information of the traffic signage device, and the congestion level of the traffic signage device; Based on the location information and the degree of congestion, determine the power supply path for the drone to supply power to the traffic signage equipment; Based on the power supply path, the drone is controlled to supply power to the traffic signage equipment.

4. The system according to claim 3, characterized in that, The traffic management platform is configured to further perform the following operations: The system accesses the monitoring object platform through a sensor network platform and obtains the power supply detection information of the traffic indication equipment detected by the power supply detection device on the monitoring object platform.

5. A computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer executes the Internet of Things-based smart city traffic management method as described in any one of claims 1 to 2.

Images