An internet-of-things-based smart city unmanned aerial vehicle data transmission method and system

Abstract

The embodiment of the present specification provides a smart city unmanned aerial vehicle data transmission method based on Internet of Things, the method is executed by the management platform, the method comprises the following steps: obtaining demand information of a user from the user platform through the service platform; determining different time domains and different airspaces in which at least two unmanned aerial vehicles execute tasks based on the demand information, the different time domains have overlapping intervals, and the different airspaces have overlapping intervals; controlling the at least two unmanned aerial vehicles to execute different tasks in the different time domains and different airspaces, and collecting task data corresponding to the different tasks; obtaining image data in the task data through the sensing network platform, and determining the priority of data transmission based on a judgment model, wherein the judgment model is a machine learning model.

Description

[0001] Divisional Statement

[0002] This application is a divisional application of the Chinese application with the application number 202210819002.8, the application date of 2022.07.13, and the invention name of "A smart city unmanned aerial vehicle management method and system based on Internet of Things". TECHNICAL FIELD

[0003] The present specification relates to the technical field of unmanned aerial vehicles, in particular to a smart city unmanned aerial vehicle data transmission method and system based on Internet of Things. BACKGROUND

[0004] With the development of unmanned aerial vehicle technology, unmanned aerial vehicles are increasingly used in data collection or applied to various monitoring scenarios. Due to its high flexibility and strong maneuverability, unmanned aerial vehicles can collect and transmit data that are difficult for humans to achieve. However, the efficiency and effectiveness of data transmission are often not guaranteed due to signal interference, transmission path, transmission distance, etc.

[0005] Therefore, it is desirable to provide a smart city unmanned aerial vehicle data transmission method and system based on Internet of Things, which can better transmit data. SUMMARY

[0006] The summary includes a smart city unmanned aerial vehicle data transmission method based on Internet of Things, which is realized by a smart city unmanned aerial vehicle management system based on Internet of Things, the smart city unmanned aerial vehicle management system based on Internet of Things includes a user platform, a service platform, a management platform, a sensing network platform, and an object platform, the method is executed by the management platform, the management platform includes a management general platform and a management sub-platform, the method includes: the management general platform obtains demand information of users from the user platform through the service platform, and distributes to the corresponding management sub-platform; the management sub-platform determines different time zones and different airspaces for at least two unmanned aerial vehicles to execute tasks based on the demand information, the different time zones have overlapping intervals, and the different airspaces have overlapping intervals; the management sub-platform controls the at least two unmanned aerial vehicles to execute different tasks in the different time zones and different airspaces, and collects task data corresponding to the different tasks; the management sub-platform obtains image data in the task data through the sensing network platform, and determines the priority of data transmission based on a judgment model, which is a machine learning model.

[0007] The summary of the invention also includes an Internet of Things-based smart city unmanned aerial vehicle data transmission system, the system comprising a user platform, a service platform, a management platform, a sensing network platform and an object platform, wherein the management platform comprises a management general platform and a management sub-platform, wherein the management general platform is configured to obtain demand information of users from the user platform through the service platform and distribute to the corresponding management sub-platform; the management sub-platform is configured to determine different time zones and different airspaces for at least two unmanned aerial vehicles to execute tasks based on the demand information, the different time zones having overlapping intervals, and the different airspaces having overlapping intervals; control the at least two unmanned aerial vehicles to execute different tasks in the different time zones and different airspaces, and collect task data corresponding to the different tasks; obtain image data in the task data through the sensing network platform, and determine the priority of data transmission based on a judgment model, which is a machine learning model. BRIEF DESCRIPTION OF DRAWINGS

[0008] The present specification will be further illustrated in the manner of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers represent the same structures, wherein:

[0009] Figure 1 is an application scenario diagram of an Internet of Things-based smart city unmanned aerial vehicle management system according to some embodiments of the present specification;

[0010] Figure 2 is an exemplary structural diagram of an Internet of Things-based smart city unmanned aerial vehicle management system according to some embodiments of the present specification;

[0011] Figure 3 is an exemplary flowchart of an Internet of Things-based smart city unmanned aerial vehicle management method according to some embodiments of the present specification;

[0012] Figure 4 is a schematic diagram of a prediction model according to some embodiments of the present specification;

[0013] Figure 5 is a schematic diagram of a method for determining data transmission priority according to some embodiments of the present specification. DETAILED DESCRIPTION

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, the present specification can also be applied to other similar scenarios without creative labor on the basis of these drawings. Unless it is clear from the language environment or otherwise stated, the same reference numbers in the drawings represent the same structure or operation.

[0015] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, sections or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0016] As shown in the specification and claims, unless the context clearly indicates otherwise, the words "one", "a", "an", and / or "the" do not refer to the singular, but can also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and components explicitly identified, and these steps and components do not constitute an exclusive list, and the method or device can also include other steps or components.

[0017] Flowcharts are used in the present specification to illustrate the operations performed by the system according to the embodiments of the present specification. It should be understood that the preceding or subsequent operations are not necessarily performed in sequence. On the contrary, each step can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps of operation can be removed from these processes.

[0018] Figure 1 is an application scenario diagram of an Internet of Things-based smart city unmanned aerial vehicle management system according to some embodiments of the present specification. In some embodiments, the scenario 100 can include a server 110, a storage device 120, a network 130, a group of unmanned aerial vehicles 140, and a terminal 150.

[0019] In some embodiments, the server 110 can be used to process information and / or data related to the scenario 100. For example, the server 110 can access information and / or data stored in the storage device 120 via the network 130. For another example, the server 110 can be directly connected to the storage device 120 to access the stored information and / or data.

[0020] The storage device 120 can be used to store data and / or instructions related to drone management. In some embodiments, the storage device 120 can store data obtained / acquired from the drone group 140 and / or the management platform. In some embodiments, the storage device 120 can store data and / or instructions used by the server 110 to perform or use to complete the exemplary methods described in this application. In some embodiments, the storage device 120 can be implemented on a cloud platform.

[0021] In some embodiments, the storage device 120 can be connected to the network 130 to communicate with one or more components of the scenario 100 (e.g., the server 110, the drone group 140, the terminal 150). One or more components of the scenario 100 can access data or instructions stored in the storage device 120 via the network 130. In some embodiments, the storage device 120 can be part of the server 110. In some embodiments, the storage device 120 can be a separate memory.

[0022] The network 130 can facilitate the exchange of information and / or data. In some embodiments, one or more components of the scenario 100 (e.g., the server 110, the drone group 140, the terminal 150) can send information and / or data to other components of the scenario 100 via the network 130. By way of example only, the network 130 can include a cable network. In some embodiments, the scenario 100 can include one or more network access points. For example, base stations and / or wireless access points, one or more components of the scenario 100 can connect to the network 130 to exchange data and / or information.

[0023] The drone group 140 can be a group of one or more drones, for example, the drone group 140 can include drone 140-1, drone 140-2, …, drone 140-n. In some embodiments, the number of drones in the drone group 140 can be determined based on the data acquisition requirements obtained by the management platform. In some embodiments, the drones in the drone group 140 can be drones performing the same task, or can be drones performing different tasks. In some embodiments, the drone group 140 can obtain data acquisition instructions from the management platform via the network 130. In some embodiments, the drone group 140 can transmit data to the corresponding management platform according to the data type, or can transmit data to the drone management platform, and then the drone management platform classifies and transmits the data to the corresponding platform.

[0024] Terminal 150 may include one or more terminals or software used by a user. In some embodiments, the user (such as staff of a drone management platform, traffic management platform, fire management platform, or pollution management platform) may be the owner of terminal 150. In some embodiments, terminal 150 may include mobile devices, tablets, laptops, wearable smart terminals, or any combination thereof. In some embodiments, the user may obtain drone management-related information through terminal 150.

[0025] It should be noted that scenario 100 is provided for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can make various modifications or variations based on the description in this specification. For example, the IoT-based smart city drone management system may also include a cloud server. As another example, the IoT-based smart city drone management system may implement similar or different functions on other devices. However, these changes and modifications will not depart from the scope of this application.

[0026] An Internet of Things (IoT) system is an information processing system that includes some or all of the following platforms: user platform, service platform, management platform, sensor network platform, and object platform. The user platform is the leader of the entire IoT operation system, responsible for acquiring user needs. User needs are the foundation and prerequisite for the formation of the IoT operation system, and the connections between the various platforms are all aimed at meeting these needs. The service platform acts as a bridge between the user platform and the management platform, providing input and output services to users. The management platform coordinates and manages the connections and collaboration between various functional platforms (such as the user platform, service platform, sensor network platform, and object platform). It aggregates information from the IoT operation system and provides sensing and control management functions. The sensor network platform connects the management platform and the object platform, functioning as a sensing and communication platform for both sensing and control information. The object platform is the functional platform for generating sensing information and executing control information.

[0027] Information processing in an IoT system can be divided into two flows: 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 sensing information and transmitting it to the management platform via the sensor network platform. The management platform then transmits the processed sensing information to the service platform, and finally to the user platform. The user analyzes and interprets the sensing information to generate control information. Control information, on the other hand, is generated by the user platform and sent to the service platform. The service platform then transmits the control information to the management platform, which processes it and sends it back to the object platform via the sensor network platform, thereby enabling control of the corresponding object.

[0028] In some embodiments, when the Internet of Things system is applied to city management, it can be referred to as a smart city Internet of Things system.

[0029] Figure 2 is an exemplary structural diagram of an Internet of Things-based smart city unmanned aerial vehicle management system according to some embodiments of the present specification. As shown in Figure 2 The Internet of Things-based smart city unmanned aerial vehicle management system 200 includes a user platform 210, a service platform 220, a management platform 230, a sensing network platform 240, and an object platform 250. In some embodiments, the Internet of Things-based smart city unmanned aerial vehicle management system 200 can be part of or implemented by the server 110.

[0030] In some embodiments, the Internet of Things-based smart city unmanned aerial vehicle management system 200 can be applied to various scenarios such as unmanned aerial vehicle management. In some embodiments, the Internet of Things-based smart city unmanned aerial vehicle management system 200 can obtain data collection requirement information. In some embodiments, the Internet of Things-based smart city unmanned aerial vehicle management system 200 can control at least two unmanned aerial vehicles to perform a data collection task based on the data collection requirement information.

[0031] Various scenarios of unmanned aerial vehicle management can include collection and transmission of data such as traffic accidents, fire, pollution, and image data. It should be noted that the above scenarios are only examples and do not limit the specific application scenarios of the Internet of Things-based smart city unmanned aerial vehicle management system 200. Those skilled in the art can apply the Internet of Things-based smart city unmanned aerial vehicle management system 200 to any other suitable scenario based on the content disclosed in the present embodiment.

[0032] The Internet of Things-based smart city unmanned aerial vehicle management system 200 will be described in detail below.

[0033] The user platform 210 can be a user-oriented service interface configured as a terminal device. In some embodiments, the user platform 210 can receive information from a user. For example, the user platform 210 can receive demand information input by a user. For another example, the user platform 210 can receive a UAV management strategy query instruction input by a user. In some embodiments, the user platform 210 can interact with the service platform 220. For example, the user platform 210 can send demand information input by a user to the service platform. For another example, the user platform 210 can send a UAV management strategy query instruction to the service platform, and receive a UAV management strategy fed back by the service platform 220. The UAV management strategy can include, for example, distribution of UAVs, number allocation of UAVs, data transmission mode of UAVs (e.g., splitting strategy for large amount of data, data relay transmission based on a second UAV, etc.).

[0034] The service platform 220 can be a platform for preliminary processing of demand information, configured as a first server. In some embodiments, the service platform is generally arranged in a centralized manner. In some embodiments, the service platform 220 can send demand information from the user platform 210 to a general platform of the management platform. In some embodiments, the service platform 220 can interact with the management platform 230. For example, a UAV management strategy query instruction obtained from the user platform 210 is sent to the management platform 230, and a UAV management strategy fed back by the management platform 230 is received.

[0035] The management platform 230 can be an Internet of Things platform for overall planning and coordination of contact and cooperation between functional platforms, and for providing perception management and control management. In some embodiments, the management platform 230 can be configured as a second server. In some embodiments, the management platform 230 performs corresponding management work in response to demand information of a user sent by the service platform 220. For example, a general platform of the management platform 230 aggregates demand information, allocates different demand information to corresponding management sub-platforms for further processing, and determines corresponding management strategies.

[0036] In some embodiments, the management platform is generally arranged in a front-splitting manner. The front-splitting manner refers to that the management platform is provided with a general platform and a plurality of sub-platforms (including self-owned databases), the plurality of sub-platforms respectively store and process different types of data sent by the sensing network platform, the general platform stores and processes data of the plurality of sub-platforms after aggregation, and transmits the data to the service platform. The management sub-platforms are set based on different task types or demand types, and each management sub-platform has a corresponding sensing network sub-platform. Data obtained by the object platform is uploaded to the corresponding sensing network sub-platform, aggregated by the sensing network general platform, and then uploaded to the corresponding management sub-platform. For example, the management sub-platforms can include a traffic accident management sub-platform, a fire accident management sub-platform, a pollution accident management sub-platform, etc.

[0037] In some embodiments, each management sub-platform receives relevant task data collected by the UAVs from the total database of the sensing network platform, and processes and manages the task data collected by the UAVs. In some embodiments, each management sub-platform (including its own database) further uploads the processed data to the management total platform, and the management total platform uploads the aggregated processed data to the service platform. The data uploaded to the service platform can include UAV management strategy information.

[0038] In some embodiments, the management total platform can also be referred to as the total platform of the management platform, and the management sub-platform can also be referred to as the sub-platform of the management platform.

[0039] In some embodiments, the management platform 230 can interact with the sensing network platform 240. The management platform 230 can issue task data acquisition instructions to the sensing network platform, and receive the aggregated task data collected by the UAVs uploaded by the sensing network total platform. For example, for a UAV device performing a traffic accident data collection task, the task data collected by the UAV device is processed by the traffic accident sensing network sub-platform and uploaded to the sensing network total platform, and the task data is associated with an identifier indicating the type of task. When the traffic accident management sub-platform issues an instruction to acquire traffic accident data collection tasks, the sensing network total platform can filter the corresponding data according to the identifier of the type of task and upload the data to the traffic accident management sub-platform.

[0040] In some embodiments of the present specification, the management sub-platform processes the UAV monitoring data of different task types, and then aggregates the data into the total database, which can reduce the data processing pressure of the entire management platform, and can also aggregate the data of each independent sub-platform for unified processing, thereby realizing the collaborative work and deployment of the management platform for UAVs of different types of tasks.

[0041] In some embodiments, the management platform 230 is used for the smart city UAV management method described in some embodiments of the present specification, and processes the task data uploaded by the sensing network platform in response to the query requirements of the user, and determines the UAV management strategy.

[0042] In some embodiments, the management platform 230 is further configured to determine different time zones and different airspaces for at least two UAVs to perform tasks, and control the at least two UAVs to perform the tasks and collect task data.

[0043] In some embodiments, the management platform 230 is further configured to determine a data transmission method based on the data requirements of different tasks and the characteristics of the at least two UAVs.

[0044] For more information about the management platform 230, please refer to Figures 3-5 and the related description thereof.

[0045] The sensing network platform 240 can be a platform that manages the interaction between the platform and the object platform. In some embodiments, the sensing network platform adopts a front-end arrangement, including a sensing network general platform and sensing network sub-platforms, which correspond to the management sub-platforms one-to-one, and can include a traffic accident sensing network sub-platform, a fire accident sensing network sub-platform, a pollution accident sensing network sub-platform, etc. In some embodiments, the sensing network platform 240 is configured as a communication network and a gateway, and each sensing network sub-platform can be configured with an independent gateway. In some embodiments, the general platform of the sensing network platform 240 can aggregate the management strategies from each management sub-platform and distribute different management strategies to the corresponding sensing network sub-platforms for processing.

[0046] In some embodiments, the sensing network general platform aggregates the different time domains and different airspaces in which at least two UAVs perform tasks determined by the management sub-platforms, and further processes them through the sensing network sub-platforms. The sub-platforms of the sensing network platform send the processed different time domains and different airspaces in which at least two UAVs perform tasks to the object platform.

[0047] In some embodiments, the sensing network general platform can also be referred to as the general platform of the sensing network platform, and the sensing network sub-platforms can also be referred to as the sub-platforms of the sensing network platform.

[0048] In some embodiments, the task data collected by the UAV device is directly uploaded to the corresponding sensing network sub-platform for processing and operation management. For example, the UAV device dispatched to the destination to perform traffic accident monitoring uploads its data to the traffic accident sensing network sub-platform; for another example, the UAV device dispatched to the destination to perform fire accident monitoring uploads its data to the fire accident sensing network sub-platform. In some embodiments, the sensing network sub-platform (including its own database) further uploads the data to the sensing network general platform, and the sensing network general platform uploads the aggregated and processed task data to the management platform 230.

[0049] In some embodiments of the present specification, the large amount of task data collected by the UAV is first processed by the sensing network sub-platform and then aggregated to the general database, which can reduce the data processing pressure of the entire sensing network platform and avoid heavy workload caused by disordered data.

[0050] The object platform 250 can be a functional platform for generating and controlling the final execution of the sensing information. In some embodiments, the object platform 250 can be configured as a functional platform composed of at least one unmanned aerial device. Each of the at least one unmanned aerial device is configured with a unique number, which can be used to manage the unmanned aerial device (e.g., deployment, grouping, and cooperative transmission, etc.). The unmanned aerial device can include a positioning device for obtaining real-time position information (e.g., area, coordinates, etc.) of the unmanned aerial device. The unmanned aerial device can also include a camera device for collecting real-time image information. In some embodiments, the object platform can interact with the sensing network platform 240, receive the task data collection instructions issued by the sensing network platform, and upload the collected task data to the corresponding sensing network sub-platform.

[0051] For those skilled in the art, after understanding the principle of the system, the Internet of Things-based smart city unmanned aerial vehicle management system 200 can be applied to any other suitable scenario without departing from the principle.

[0052] It should be noted that the above description of the system and its components is for convenience of description only, and cannot limit the present specification to the scope of the embodiments. It can be understood that, for those skilled in the art, after understanding the principle of the system, the components can be combined arbitrarily or connected with other components to form a subsystem without departing from the principle. For example, the components can share a storage device, and the components can also have their own storage devices. Such variations are within the scope of the present specification.

[0053] Figure 3 is an exemplary flowchart of an Internet of Things-based smart city unmanned aerial vehicle management method according to some embodiments of the present specification. As shown in Figure 3 , the flow 300 includes the following steps. In some embodiments, the flow 300 can be performed by the management platform 230.

[0054] Step 310: The total platform of the management platform obtains the demand information of the user from the user platform through the service platform, and distributes it to the corresponding management sub-platform.

[0055] The demand information refers to information that can reflect the data collection demand. In some embodiments, the demand information can include the data collection demand from the user. In some embodiments, the user platform can obtain the demand information input by the user and send the demand information to the service platform. In some embodiments, the total platform of the management platform can obtain the demand information of the user through the service platform. In some embodiments, the demand information can include the data collection demand for collecting the water quality condition of a water area based on pollution management, the data collection demand for collecting the fire situation of a fire scene and the traffic condition of a surrounding road section based on fire management, and the like.

[0056] In some embodiments, the management platform can also automatically generate demand information by processing the information returned by the unmanned aerial vehicle group. For example, the management platform can analyze the fire information collected by the unmanned aerial vehicle to generate a data collection demand for collecting the traffic condition around the fire scene.

[0057] In some embodiments, the total platform of the management platform distributes different demand information to different management sub-platforms for further processing to determine the management strategy of the unmanned aerial vehicle. The management strategy can include at least two different time domains and different air domains in which the unmanned aerial vehicles perform tasks.

[0058] In step 320, the management sub-platform determines the different time domains and different air domains in which the at least two unmanned aerial vehicles perform tasks based on the demand information, wherein the different time domains have overlapping intervals and the different air domains have overlapping intervals.

[0059] The time domain refers to the time interval in which the unmanned aerial vehicle performs a task. In some embodiments, the time domain can include at least one time interval. In some embodiments, the at least two unmanned aerial vehicles perform tasks in different time domains, and the different time domains have overlapping intervals. For example, the time domain in which the unmanned aerial vehicle 140-1 performs a task is from 9:00 to 12:00 on May 1, 2022, and the time domain in which the unmanned aerial vehicle 140-2 performs a task is from 11:00 to 13:00 on May 1, 2022. The two unmanned aerial vehicles perform tasks in different time domains, but have overlapping intervals, i.e., from 11:00 to 12:00 on May 1, 2022.

[0060] The air domain refers to the flight space range in which the unmanned aerial vehicle performs a task. For example, the flight height range and the task area range. In some embodiments, the air domain can include at least one space range. In some embodiments, the at least two unmanned aerial vehicles perform tasks in different air domains, and the different air domains have overlapping intervals. For example, the unmanned aerial vehicle 140-1 performs a task of collecting fire information of a fire scene, and the unmanned aerial vehicle 140-2 performs a task of collecting traffic condition information of a road section adjacent to the fire scene. The two unmanned aerial vehicles perform tasks in different air domains, but have overlapping intervals.

[0061] In some embodiments, the sub-platform of the management platform can determine different time domains and different airspace in which the at least two UAVs perform tasks based on the demand information.

[0062] In some embodiments, the sub-platform of the management platform can determine different time domains and different airspace in which the at least two UAVs perform tasks based on the time information and the task area information in the demand information. For example, based on the traffic management platform issuing a demand for collecting traffic information of a section A from 8:00 to 10:00 am and from 5:00 to 7:00 pm every day, the time domain in which the UAVs perform tasks can be determined as from 8:00 to 10:00 am and from 5:00 to 7:00 pm every day, and the airspace information can be determined as the section A.

[0063] In some embodiments, the sub-platform of the management platform can determine the time domain information in which the UAVs perform tasks based on the time information in the demand information and the power condition of the UAVs. For example, the task time period in the demand information is from 8:00 to 12:00 am for 4 hours, and the power of the UAVs can support 3 hours at most, and thus the time domain in which the UAVs perform tasks can be determined as 3 hours from 8:00 to 12:00 am.

[0064] In some embodiments, the sub-platform of the management platform can determine the airspace in which the UAVs perform tasks based on the demand information and the legal and regulatory information. For example, the task area information can be determined based on the demand information, and whether the task area contains a no-fly area can be determined based on the legal and regulatory information, and thus the airspace in which the UAVs perform tasks can be determined. In some embodiments, determining the airspace in which the UAVs perform tasks further includes determining the flight route of the UAVs. In some embodiments, the flight route of the UAVs can be determined based on the task area information in the demand information and the legal and regulatory information. For example, whether the task area contains a no-fly area can be determined based on the legal and regulatory information, and if the task area contains a no-fly area, the flight route is determined to avoid the no-fly area.

[0065] In step 330, the at least two UAVs are controlled to perform different tasks in different time domains and different airspace, and collect task data corresponding to the different tasks.

[0066] In some embodiments, the management platform 230 can control the at least two UAVs to perform different tasks in different time domains and different airspace, and collect task data corresponding to the different tasks. For example, based on the pollution management platform issuing a demand for collecting water quality information of a certain water area, the UAVs can be controlled to collect water samples at different positions of the water area and perform detection, take pictures of the color of the water, etc. For another example, based on the fire management platform issuing a demand for collecting fire information of a certain factory, the UAVs can be controlled to take pictures of the fire scene at different directions of the factory and collect information such as wind power and wind direction.

[0067] In some embodiments, the management platform 230 can process the data returned by the UAVs, control the at least two UAVs to perform different tasks in different time domains and different airspace based on the processing result, and collect task data corresponding to the different tasks. For example, the fire data of a factory returned by the UAVs can be processed, and based on the processing result, it is found that the fire is heavy and more fire fighting forces need to be called to rescue the fire, and the traffic situation around the fire site needs to be monitored to clear the rescue channel. At this time, the adjacent UAVs can be controlled to perform the task of collecting traffic data to the factory.

[0068] In step 340, a data transmission mode is determined based on the data requirement of the different task data and the characteristics of the at least two UAVs, wherein the characteristics of the at least two UAVs include at least one of the distance between the UAVs, the distance between the UAVs and the management platform, the bandwidth of the UAV transmission, and the positioning of the UAVs.

[0069] The data requirement refers to the data to be collected corresponding to the task performed by the UAV. In some embodiments, the data requirement includes the amount of data to be transmitted.

[0070] The amount of data to be transmitted refers to the amount of data to be transmitted corresponding to the task performed by the UAV. For example, the amount of data to be transmitted of the traffic situation of the region A in the last 10 minutes is 1.5 GB; the amount of data to be transmitted of the pollution situation of the river C in the last month is 45 GB.

[0071] In some embodiments, the total amount of data to be transmitted can be obtained by summing the amount of data to be transmitted of all types of tasks being executed. For example, the types of tasks 410 being executed include traffic management and disaster rescue, and the amount of data to be transmitted of traffic management and disaster rescue is 400 GB and 500 GB respectively, and the total amount of data to be transmitted is 900 GB.

[0072] In some embodiments, the amount of data to be transmitted of each type of task can be obtained by summing the amount of data to be transmitted of the UAVs executing the task. For example, the number of UAVs executing the traffic management task is 35, and the number of UAVs executing the disaster rescue task is 30. The amount of data to be transmitted of each UAV executing traffic management and disaster rescue is 10 GB and 5 GB respectively, and the amount of data to be transmitted of traffic management is 350 GB, and the amount of data to be transmitted of disaster rescue is 150 GB.

[0073] In some embodiments, the amount of data to be transmitted can be determined by a prediction model. For more information about the prediction model, please refer to Figure 3 and the related description.

[0074] The UAV feature refers to a feature of the UAV when transmitting data. For example, the UAV feature can include at least one of a distance between UAVs, a distance between the UAV and the management platform, a bandwidth condition of the UAV transmitting data, and positioning information of the UAV.

[0075] The data transmission mode refers to a mode adopted by the UAV when transmitting data. For example, the data transmission mode can include relay transmission, split transmission, and combined transmission.

[0076] The relay transmission refers to data transmission by relay between at least two data transmission points. For example, when the UAV performing the task is too far away from the corresponding management platform to transmit data, data transmission can be performed by using other UAVs or platforms between the UAV and the management platform as a relay.

[0077] The split transmission refers to transmitting data after splitting. For example, when a large video is transmitted and the transmission bandwidth is small, the video can be split into multiple segments and transmitted separately. In some embodiments, the split data can be transmitted by the same UAV in sequence after splitting. In some embodiments, the split data can be transmitted by multiple UAVs separately after splitting. In some embodiments, the split data can be combined based on data tags. In some embodiments, the data can be tagged based on the task type, task location, and data collection time when the UAV collects the data.

[0078] The combined transmission refers to transmitting the same type of data after combining. The same type of data refers to data of the same type and collected at the same location. The data type can include text, image, voice, video, etc. For example, multiple image data taken at the same location and angle can be combined and transmitted.

[0079] In some embodiments, the data transmission mode can be determined based on the data requirements of different task data and the features of at least two UAVs.

[0080] In some embodiments, the data transmission mode can be determined based on the features of at least two UAVs. In some embodiments, the data transmission mode can be determined as relay transmission based on a comparison between the distance between the UAV and the corresponding management platform, the distance between the at least two UAVs, and a threshold value. For example, based on the distance between the UAV and the corresponding management platform being greater than a threshold value, the distance between the UAV and another UAV being less than a threshold value, and the distance between the other UAV and the management platform being less than a threshold value, it can be determined that the other UAV is used as a relay to transmit data. The threshold value can be a preset value, such as 10 km.

[0081] In some embodiments, the data transmission mode can be determined based on the data requirements of different task data and the features of the UAV.

[0082] In some embodiments, the data amount can be determined based on the data requirement of different task data. In some embodiments, the data transmission manner can be determined as split transmission based on the data amount and the transmission bandwidth of the UAV. For example, the data amount collected by the UAV is 10 GB, and the current transmission bandwidth of the UAV is 1 Mbps. In order to improve the data transmission efficiency, the data can be split into five 2 GB data, and transmitted by five adjacent UAVs respectively.

[0083] In some embodiments, the data transmission manner can be determined as merged transmission based on the data being the same type of data and the transmission bandwidth of the UAV. For example, the plurality of data are the same type of data, and the data amount is large, and the transmission bandwidth of the UAV is small, so the same type of data can be merged and then transmitted.

[0084] In some embodiments, whether the data is the same type of data can be determined based on the data requirement. In some embodiments, whether the data is the same type of data can be determined based on the data type and the data collection location in the data requirement. For example, the plurality of data can be determined as the same type of data based on the type of the plurality of data being pictures and the same shooting location. In some embodiments, the coordinate information of the data collection location can be determined based on the positioning information when the UAV collects the data. If the distance between two coordinates is less than a threshold value, the two groups of data corresponding to the coordinates are determined as the same type of data.

[0085] In some embodiments of the present specification, the time domain and the space domain of the UAV performing the task are determined based on the data requirement, and the UAV is controlled to collect the data corresponding to the task, which can enhance the pertinence and rationality of the UAV dispatch, ensure the reasonable allocation of resources, improve the timeliness of the overall scheduling of the UAV in emergency situations, and help to timely and comprehensively obtain information by calling the nearest UAV to assist in performing the task, so as to gain time for emergency rescue.

[0086] In some embodiments of the present specification, the data is transmitted by using multiple transmission manners based on the data requirement and the characteristics of the UAV, which can improve the efficiency and flexibility of data transmission and ensure the completeness and accuracy of the data. The relay transmission avoids the situation that the UAV cannot transmit data when it is far away from the management platform. The split transmission and the merged transmission can reduce the data amount transmitted by a single UAV and improve the transmission efficiency.

[0087] It should be noted that the above description of the process 300 is only for example and illustration, and does not limit the scope of the present specification. Various modifications and changes can be made to the process 300 by those skilled in the art under the guidance of the present specification. However, these modifications and changes are still within the scope of the present specification. For example, the process 300 can also include a UAV dispatching process.

[0088] In some embodiments, determining the data transmission manner further comprises determining a priority of the data transmission. More embodiments about determining the priority of the data transmission can be found in Figure 5 and the related description.

[0089] Figure 4 is a schematic diagram of a prediction model according to some embodiments of the present specification. As shown in the figure, the execution and training of the prediction model 400 can at least include the following.

[0090] In some embodiments, the prediction model 430 can be used to determine the amount of data to be transmitted 440 for each drone. In some embodiments, the input of the prediction model can include at least one type of task performed by the drone 410 and the monitoring data corresponding to the performed task 420, for example, the input of the prediction model 430 can include the type of task 410, the monitoring data 420. The output can include the amount of data to be transmitted 440 for each drone.

[0091] In some embodiments, the prediction model 430 can be a trained machine learning model. The prediction model 430 can include, but is not limited to, one or a combination of convolutional neural networks, deep neural networks, etc.

[0092] The type of task 410 refers to the type of task performed by the drone. For example, traffic management, disaster relief, pollution monitoring, etc.

[0093] The monitoring data 420 refers to the working condition of the drone during the execution of the task. In some embodiments, the monitoring data 420 can include one or more of the target area information (such as the location of area A), the monitoring matter (such as the image of all roads in area A), the monitoring duration or time (such as 10 minutes, 12:00-18:00, etc.), the monitoring frequency (such as once an hour, once a day, etc.).

[0094] In some embodiments, the parameters of the prediction model 430 can be obtained by training. The initial prediction model 431 can be trained based on a plurality of sets of training samples with labels, which can be historical task types 431-2 and historical monitoring data 431-1 corresponding to the historical task types 431-2 in historical execution of tasks by the UAVs, and the labels of the training samples can be the actual transmitted data amount corresponding to the task types 410 and the monitoring data 420. It should be understood that the UAVs in the samples and the UAVs for which the data amount to be transmitted needs to be determined should be UAVs of the same model. For example, the processing device can collect, as training samples, historical task types 431-2 and historical monitoring data 431-1 corresponding to the historical task types 431-2 in a plurality of tasks executed by the UAVs in a historical period of time (such as a day, a week, a month, etc.), and the actual transmitted data amount 431-3 corresponding to the task types 410 and the monitoring data 420 in the period of time as labels of the training samples, which can be obtained by querying or detecting the amount of data transmitted in the period of time. The plurality of training samples are input into the initial prediction model 431, a loss function is constructed based on the output of the initial prediction model 431 and the labels, and the parameters of the initial prediction model 431 are iteratively updated based on the loss function, and when the trained model meets a preset condition, the training is ended, and the trained prediction model 430 is obtained. The preset condition can include, but is not limited to, convergence of the loss function, the loss function value being less than a preset value, or the number of training iterations reaching a threshold, etc.

[0095] The method described in some embodiments of the present specification can quickly and accurately determine the data amount to be transmitted by the UAVs through the model, which facilitates subsequent accurate adjustment of the number of UAVs to ensure smooth completion of the UAV tasks.

[0096] In some embodiments, determining the data amount to be transmitted by each UAV 440 can be implemented based on other manners. In some embodiments, the processing device can also determine the data amount to be transmitted based on artificial experience. For example, UAV-related personnel (such as UAV industry experts, UAV operators, etc.) determine the data amount to be transmitted based on past experience. In some embodiments, the processing device can determine the data amount to be transmitted according to historical data. The historical data can include the task types 410, the monitoring data 420, and the corresponding actual data amount. It can be understood that the processing device can take the actual data amount corresponding to the historical data similar to the task types 410 and the monitoring data 420 of this time as the data amount to be transmitted this time.

[0097] In some embodiments, the processing device can determine the optimal number of drones currently performing tasks based on the total amount of data to be transmitted. In some embodiments, the processing device can pre-set a preset range interval of the total amount of data to be transmitted, and when the total amount of data to be transmitted is within a certain preset range, the optimal number of drones can be the number value corresponding to the preset range. For example only, when the total amount of data to be transmitted is within the range of 0-100GB, the optimal number of drones is 5; when the total amount of data to be transmitted is within the range of 100GB-500GB, the optimal number of drones is 15; and when the total amount of data to be transmitted exceeds 500GB, the optimal number of drones is 20. It should be understood that the larger the total amount of data to be transmitted, the more the optimal number of drones, which can increase the data collection efficiency and transmission efficiency.

[0098] In some embodiments, the processing device can adjust the number of drones currently performing tasks based on the optimal number and the proportion of the amount of data to be transmitted of each type of task in the total amount of data to be transmitted. For example, there are currently 11 drones performing tasks, of which 7 drones are performing traffic management tasks and 4 drones are performing sewage monitoring tasks. The total amount of data to be transmitted is currently 604GB, so according to the aforementioned correspondence, the optimal number of drones is 20. The amount of data to be transmitted corresponding to traffic management and sewage monitoring is currently 397G and 207G, respectively, accounting for 34.3% and 65.7% of the total amount of data to be transmitted, respectively, so the optimal number of drones performing traffic management should be 13, and the optimal number of drones performing sewage monitoring should be 7. Therefore, based on the current basis, the drones performing traffic management and sewage monitoring need to be increased by 6 and 3, respectively.

[0099] Some methods described in the embodiments of the present specification can accurately allocate the number of drones based on the amount of data to be transmitted for tasks, and achieve reasonable allocation of resources.

[0100] In some embodiments, the processing device can determine the data transmission method based on the total amount of data to be transmitted. In some embodiments, when the total amount of data to be transmitted is greater than a preset threshold, the processing device can split or combine the data before transmitting it to the data receiving platform.

[0101] For more information on data splitting and combining, see Figure 3 Related content.

[0102] Some methods described in the embodiments of the present specification can determine the transmission method based on the amount of data to be transmitted for tasks, and improve transmission efficiency.

[0103] Some methods described in embodiments of the present specification can determine the data amount to be transmitted of different task data through a prediction model, determine the number of UAVs and different data transmission modes according to the data amount to be transmitted, and improve the efficiency of data transmission and the flexibility of UAV resource allocation.

[0104] Figure 5 is a schematic diagram of a method for determining data transmission priority according to some embodiments of the present specification. As shown in Figure 5 the method 500 for determining data transmission priority can include the following. In some embodiments, the priority of data transmission can be determined based on the obtained image data.

[0105] In some embodiments, the processing device can obtain data features of the task data, and determine the priority of data transmission based on the data features.

[0106] Data features refer to the characteristics of the task data. In some embodiments, the data features include at least one of importance, urgency, and data size.

[0107] Importance can refer to the degree of influence of the task data on the execution result of the task. For example, the importance of the image data of a road with heavy traffic of people and vehicles in traffic management is higher than that of a road with small traffic of people and vehicles.

[0108] Urgency can reflect whether the task data needs to be quickly transmitted and analyzed for the execution of the task. For example, in disaster relief, when the rescue team sets off, the urgency of the image data of the road from the location of the rescue team to the dangerous area is higher than that of the road from the dangerous area to the hospital.

[0109] Data size refers to the data amount of the task data. For example, 500MB, 30GB, etc.

[0110] In some embodiments, the importance and urgency of the task data are determined by the information contained in the task data. In some embodiments, the importance and urgency of different task data can be preset to correspond to the information contained in the task data. By way of example only, the importance and urgency of the task type 410 contained in the task data are preset. For example, the importance and urgency of the task type 410 for disaster relief are the highest, the importance and urgency of the task type 410 for traffic management are the second highest, and the importance and urgency of the task type 410 for pollution monitoring are the lowest. For another example, the importance and urgency can each be represented as a value in 0-100. The greater the value, the higher the importance and urgency of the task type 410. The importance and urgency of the task type 410 for disaster relief are both 100, the importance and urgency of the task type 410 for traffic management are both 70, and the importance and urgency of the task type 410 for pollution monitoring are both 50.

[0111] In some embodiments, the data size can be directly determined by the data volume of the task data. For example, the data volume of the task data for pollution monitoring is 200 MB, and the data volume of the task data for disaster relief is 1 GB, then the data size of the task data for disaster relief is greater than that of the task data for pollution monitoring.

[0112] The priority of data transmission refers to the order of transmission of the task data. For example, the priority of data transmission of data A is the highest, and the priority of data transmission of data B is the lowest, then data A is transmitted before data B.

[0113] In some embodiments, the priority of data transmission can be determined based on the importance, urgency, and data size.

[0114] In some embodiments, the priority of data transmission can be determined based on the importance and urgency. By way of example only, the importance and urgency can each be represented as a value in 0-100. The greater the value, the higher the importance and urgency. When the sum of the values of the importance and urgency of one task data is greater than the sum of the values of the importance and urgency of another data, then the priority of the former is higher than that of the latter. For example, the urgency of task data A is 60, the importance is 80, the urgency of task data B is 90, and the importance is 75, the sum of the values of the importance and urgency of task data A is 140, which is less than the sum of the values of the importance and urgency of task data B, which is 165, then the priority of task data B is higher than that of task data A.

[0115] In some embodiments, when at least two task data sets have the same priority based on importance and urgency, priority can be determined based on the data size. In some embodiments, task data with a smaller data size has a higher priority than task data with a larger data size. For example, if task data C has a data size of 800MB and task data D has a data size of 1.4GB, then task data C has a higher priority than task data D.

[0116] In some embodiments, the priority 540 of data transmission can be determined by the judgment model 520.

[0117] In some embodiments, based on image data 510 from the task data collected by the UAV, a judgment model 520 can determine the data transmission priority 540 of each type of task data. The judgment model 520 is a trained machine learning model. The judgment model 520 may include, but is not limited to, one or more combinations of convolutional neural networks, deep neural networks, etc.

[0118] like Figure 5 As shown, in some embodiments, the judgment model 520 may include a feature recognition layer 520-1 and a priority determination layer 520-2.

[0119] The feature recognition layer 520-1 can extract features from image data 510 to obtain image features 521. Image features may include color features, texture features, shape features, spatial relationship features, etc. The feature recognition layer 520-1 can be a convolutional neural network. Figure 5 As shown, the input to the feature recognition layer 520-1 may include image data 510, and the output of the feature recognition layer 520-1 may include image features 521.

[0120] The priority determination layer 520-2 processes the demand information 530 and image features 521 to determine the corresponding data transmission priority 540. The priority determination layer 520-2 can be a deep neural network. For example... Figure 5 As shown, the input to the priority determination layer 520-2 can be demand information 530 and image features 521, and the output of the priority determination layer 520-2 can be the priority of data transmission 540.

[0121] In some embodiments, the feature recognition layer 520-1 and the priority determination layer 520-2 can be obtained through joint training. The training sample includes image data in the historical task data of the unmanned aerial vehicle and historical demand information, and the label can be the priority corresponding to the actual transmission of the image data under the influence of the historical demand information. For example only, the priority can be represented by a number 0-10. 0 represents the highest priority, and 10 represents the lowest priority. The smaller the number, the higher the priority, and the earlier the order of task data transmission. The image data in the training sample is input into the initial feature recognition layer. Then the output of the initial feature recognition layer and the demand information in the training sample are input into the initial priority determination layer, and a loss function is constructed based on the output of the initial priority determination layer and the label. The parameters of each layer in the initial judgment model are iteratively updated based on the loss function until a preset condition is met, and a trained judgment model is obtained. The preset condition can include but is not limited to loss function convergence, loss function value less than a preset value, or training iteration number reaching a threshold.

[0122] The method described in some embodiments of the present specification can ensure the priority transmission of important and urgent data by determining the data transmission priority, prevent important data loss, and facilitate the timely processing of emergency situations by the management platform by prioritizing the transmission of urgent data. When the data is important and urgent, the data with smaller data volume is prioritized for transmission to ensure complete transmission and avoid situations such as transmission speed decline and transmission interruption caused by transmission of data with larger data volume, so as to ensure the transmission of as much data as possible and complete transmission.

[0123] The above has described the basic concepts. Obviously, for those skilled in the art, the above detailed disclosure is only as an example, and does not constitute a limitation on the present specification. Although it is not explicitly stated here, those skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0124] At the same time, specific words are used in the present specification to describe the embodiments of the present specification. As "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that the "an embodiment" or "one embodiment" or "one alternative embodiment" mentioned in different places in the present specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present specification can be properly combined.

[0125] Each patent, patent application, patent publication, and other material cited in this specification is hereby incorporated by reference in its entirety herein for the teachings relevant to the sentence and / or paragraph in which the reference is presented. Document histories, to the extent not inconsistent with the pertinent U.S. patent application file history, are also incorporated by reference herein. To the extent that material incorporated by reference contradicts or contradicts any portion of this specification, including definition, the portion of the material incorporated by reference prevails. Note, however, that in the event of inconsistencies between any such material and the present specification, including definitions, the present specification, including definitions, will control.

[0126] Finally, it should be understood that the embodiments described herein are merely exemplary of the principles of the present description. Other embodiments can be devised without departing from the scope of the present description. Accordingly, the embodiments described herein are not intended to limit the scope of the present description, but rather are intended to be exemplary thereof.

Claims

1. A data transmission method for unmanned aerial vehicles (UAVs) in smart cities based on the Internet of Things (IoT), implemented by an IoT-based smart city UAV management system, characterized in that... The IoT-based smart city drone management system includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The method is executed by the management platform, which includes a central management platform and sub-management platforms. The overall management platform obtains user demand information from the user platform through the service platform and distributes it to the corresponding management sub-platforms; The management sub-platform determines, based on the demand information, different time domains and different air domains for at least two UAVs to perform tasks, wherein the different time domains have overlapping intervals and the different air domains have overlapping intervals. The management sub-platform controls the at least two UAVs to perform different tasks in different time domains and different air domains, and collects task data corresponding to the different tasks; The management sub-platform acquires image data from the task data through the sensor network platform, and determines the priority of data transmission based on a judgment model, which is a machine learning model. The management sub-platform determines the data transmission method based on the data requirements of the different task data and the characteristics of the at least two drones. The characteristics of the at least two drones include at least one of the following: the distance between the drones, the distance between the drones and the management platform, the bandwidth of the drone transmission, and the drone's positioning. The data transmission method includes relay transmission, split transmission, and merged transmission. The data requirements include the amount of data to be transmitted. The amount of data to be transmitted is determined based on a prediction model that processes the task type and the monitoring data of the drones. The prediction model is a machine learning model. Determining the data transmission method includes: When the total amount of data to be transmitted from at least two drones exceeds a preset threshold, the data will be split for transmission or merged for transmission. Splitting for transmission means transmitting the data after splitting it, and merging for transmission means transmitting data of the same type after merging it. The data of the same type refers to data with the same data type and collection location. The distance between the drones and the distance between the drones and the management platform are compared with a threshold to determine whether to use relay transmission; The management sub-platform determines the optimal number of drones currently performing a task based on the total amount of data to be transmitted; the management sub-platform adjusts the number of drones performing a task based on the optimal number and the proportion of the amount of data to be transmitted for each task type to the total amount.

2. The method according to claim 1, characterized in that, The judgment model includes a feature recognition layer and a priority determination layer. in, The feature recognition layer takes the image data as input and outputs image features as output. The priority determination layer takes as input the image features and the requirement information, and outputs the priority of the data transmission.

3. The method as described in claim 1, wherein the monitoring data includes: One or more of the following: target area information, monitoring items, monitoring duration or time, and monitoring frequency.

4. A smart city drone data transmission system based on the Internet of Things, characterized in that, The system includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The management platform includes a central management platform and sub-management platforms. The overall management platform is configured as follows: The service platform obtains user demand information from the user platform and assigns it to the corresponding management sub-platform. The management sub-platform is configured as follows: Based on the aforementioned demand information, at least two UAVs are determined to perform tasks in different time domains and different air domains, wherein the different time domains have overlapping intervals and the different air domains have overlapping intervals. Control at least two UAVs to perform different tasks in different time domains and different air domains, and collect task data corresponding to the different tasks; The sensor network platform acquires image data from the task data and determines the priority of data transmission based on a judgment model, which is a machine learning model. The management sub-platform determines the data transmission method based on the data requirements of the different task data and the characteristics of the at least two drones. The characteristics of the at least two drones include at least one of the following: the distance between the drones, the distance between the drones and the management platform, the bandwidth of the drone transmission, and the drone's positioning. The data transmission method includes relay transmission, split transmission, and merged transmission. The data requirements include the amount of data to be transmitted. The amount of data to be transmitted is determined based on a prediction model that processes the task type and the monitoring data of the drones. The prediction model is a machine learning model. Determining the data transmission method includes: When the total amount of data to be transmitted from at least two drones exceeds a preset threshold, the data will be split for transmission or merged for transmission. Splitting for transmission means transmitting the data after splitting it, and merging for transmission means transmitting data of the same type after merging it. The data of the same type refers to data with the same data type and collection location. The distance between the drones and the distance between the drones and the management platform are compared with a threshold to determine whether to use relay transmission; The management sub-platform determines the optimal number of drones currently performing a task based on the total amount of data to be transmitted; the management sub-platform adjusts the number of drones performing a task based on the optimal number and the proportion of the amount of data to be transmitted for each task type to the total amount.

5. The system according to claim 4, characterized in that, The judgment model includes a feature recognition layer and a priority determination layer. in, The feature recognition layer takes the image data as input and outputs image features as output. The priority determination layer takes as input the image features and the requirement information, and outputs the priority of the data transmission.

Images