A blockchain-based multi-unmanned aerial vehicle cooperative urban sensing method

Abstract

The present application relates to a kind of multi-robot collaborative city sensing methods based on blockchain, comprising the following steps: (10) multi-robot task and state information sharing.(20) Distributed problem modeling of unmanned aerial vehicle.(30) Multi-robot consensus mechanism under blockchain.(40) Multi-robot smart contract rules under blockchain.(50) Distributed generation of unmanned aerial vehicle collaborative sensing scheme: unmanned aerial vehicle uses iterative mechanism, periodically executes information consensus and smart contract, until consensus information is no longer updated, and the consistency collaborative sensing scheme formed is counted into blockchain.The present application is aimed at multi-robot collaborative city sensing scene, introduces blockchain technology into unmanned aerial vehicle cluster collaborative sensing problem, realizes the distributed control of multi-robot collaborative sensing by designing consensus mechanism and smart contract scheme, enhances the robustness of unmanned aerial vehicle cluster system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicles, and particularly relates to a multi-unmanned aerial vehicle cooperative city sensing method based on a blockchain. BACKGROUND

[0002] In recent years, as the degree of urban intelligence is increasingly high, the requirement for urban environment sensing is also increasingly high. The traditional information source for urban environment sensing is mainly a deployed sensor, such as a camera, which has a limited range and a fixed position and is difficult to cover the whole city environment with complex and multiple occlusions. Meanwhile, the deployed sensor is mostly an optical device, which has a single sensing mode and limited performance and cannot meet the growing sensing demand.

[0003] The rapid development of unmanned aerial vehicle cluster technology provides a new idea for urban environment sensing. By carrying multiple types of sensing devices, the unmanned aerial vehicle can obtain high-quality sensing information by using multi-source information fusion processing technology. Meanwhile, by using the mobility of the unmanned aerial vehicle, the whole city environment can be sensed, which provides a basis for subsequent intelligent decision-making of the city. The main difficulty in sensing the city environment by using the unmanned aerial vehicle cluster technology is how to intelligently control the unmanned aerial vehicle according to the sensing demand so that the multi-unmanned aerial vehicle can cooperatively complete the city sensing task.

[0004] The traditional cooperative control scheme of the unmanned aerial vehicle often needs a central controller to collect the information such as the resource, position and function of the unmanned aerial vehicle, make a centralized decision control, and issue the control scheme to the unmanned aerial vehicle to cooperatively complete the city sensing task. Although the centralized decision algorithm can obtain a globally optimal unmanned aerial vehicle cooperative sensing scheme, the algorithm has a high complexity and excessively depends on the central controller, which has poor robustness and is difficult to better adapt to the complex city environment. Therefore, it is necessary to design a distributed algorithm so that the multi-unmanned aerial vehicle can make a distributed decision control without a central controller. Meanwhile, due to the high dynamics of the city environment, the unmanned aerial vehicle cluster needs a low-complexity method to quickly generate a sensing scheme to respond to the environmental changes.

[0005] The distributed architecture and rapid response are two important features of the blockchain technology, and therefore the blockchain technology is suitable for application in the multi-unmanned aerial vehicle cooperative city sensing problem. The unmanned aerial vehicle is regarded as a node in the blockchain, and the unmanned aerial vehicle realizes the consensus among the nodes by using the consensus mechanism through the regular broadcast of information. Meanwhile, by designing an unmanned aerial vehicle control algorithm, the cooperative sensing scheme is intelligently generated by using the smart contract, and is automatically executed. The multi-unmanned aerial vehicle cooperative city sensing based on the blockchain technology can well realize the distributed and rapid cooperative control of the multi-unmanned aerial vehicle to effectively sense the city environment.

[0006] At present, there is no related research on the multi-unmanned aerial vehicle cooperative city sensing scheme based on the blockchain technology. SUMMARY

[0007] To solve the existing technical problems, the application provides a multi-unmanned aerial vehicle cooperative urban sensing method based on a block chain.

[0008] The specific content of the application is as follows: a multi-unmanned aerial vehicle cooperative urban sensing method based on a block chain, comprising the following steps:

[0009] (10) Multi-unmanned aerial vehicle task and state information sharing: each unmanned aerial vehicle serves as a node to jointly form a block chain; the unmanned aerial vehicle generates a sensing task by receiving information published by a city situation center and publishes the sensing task to the block chain; at the same time, the unmanned aerial vehicle periodically publishes its own state information to the block chain; the unmanned aerial vehicle shares its own task information and state information by using broadcast communication and writes the information into a block to form a consensus;

[0010] (20) Distributed problem modeling of unmanned aerial vehicles: the unmanned aerial vehicles model the cooperative urban sensing problem of the unmanned aerial vehicles in a distributed manner according to the shared task information and the state information of the unmanned aerial vehicles;

[0011] (30) Multi-unmanned aerial vehicle consensus mechanism under a block chain: the unmanned aerial vehicles achieve consistent consensus of the information of the unmanned aerial vehicles by receiving broadcast information of other unmanned aerial vehicles based on a consensus mechanism of a block chain and pack the consensus information into the block chain;

[0012] (40) Multi-unmanned aerial vehicle intelligent contract rule under a block chain: based on the consistent information after the consensus, the task intention degree vector, the pre-allocated task vector, and the highest intention unmanned aerial vehicle vector are updated according to an intelligent contract rule;

[0013] (50) Distributed generation of a cooperative sensing scheme by unmanned aerial vehicles: the unmanned aerial vehicles periodically execute information consensus and an intelligent contract by using an iteration mechanism until the consensus information is no longer updated, and the consistent cooperative sensing scheme formed is recorded in the block chain.

[0014] Further, the step (10) of sharing the task and state information of the multi-unmanned aerial vehicles comprises the following steps:

[0015] (11) The unmanned aerial vehicles generate a sensing task set by using their own sensing information and information published by the city situation center Each task contains task importance, demand for a sensor type, resource consumption, and information to be sensed, such as area search, target tracking, target identification, and the like;

[0016] (12) The unmanned aerial vehicles periodically publish their own state information to the block chain, wherein the set of the unmanned aerial vehicles is denoted as The state information of the unmanned aerial vehicle n includes a sensing radius S n , a type of a mounted sensor, and a resource capacity R net al. n R represents the maximum sensing range of UAV n n R represents the upper limit of the number of tasks that UAV n can perform

[0017] (13) UAV periodically shares its task information and state information using broadcast communication, and writes it into the block by encrypting the data, forming consensus.

[0018] Further, step (20) includes the following steps:

[0019] (21) Define the UAV-task connection matrix A M×N , where element a m,n ∈{0,1} represents whether UAV n can perform task m; if the sensor carried by UAV n meets the sensor type requirement of the task, and the task m corresponding area is within the sensing radius S n of the UAV, then a m,n =1. Otherwise, a m,n =0;

[0020] (22) Define the UAV sensing scheme matrix X M×N , where element x m,n ∈{0,1} represents that UAV n performs sensing task m; if UAV successfully performs sensing task m, i.e. achieves sensing of the relevant environment, and obtains the corresponding sensing benefit q m ;

[0021] (23) Define the city sensing benefit as the sum of the benefits of multiple UAVs cooperating to complete sensing tasks, and under the constraint of UAV resources, optimize the sensing scheme to maximize the city sensing benefit; the optimization problem can be modeled as:

[0022] P1:

[0023] s.t.

[0024]

[0025]

[0026] Where the objective function is the city sensing benefit, the first constraint represents that UAV n has limited resources and can only complete R n tasks at most, and the second constraint indicates that in order to avoid conflict between multiple UAVs performing the same sensing task, a sensing task can be performed by at most one UAV;

[0027] (24) Introduce the task intention degree r m,nrepresents the degree of willingness of the UAV n to the perception task m, and the higher the degree of task intention means the greater the UAV is willing to pay the cost; the matching degree of the UAV and the task is defined as s m,n = a m,n q m -r m,n , that is, the greater the difference between the task benefit and the task intention, the higher the matching degree of the UAV and the task, and the task is more inclined to be assigned to the corresponding UAV; at this time, the optimization problem P1 can be decomposed into N independent sub-problems to complete the distributed problem modeling; wherein the sub-problem of the UAV n is:

[0028] P2:

[0029] s.t.

[0030]

[0031] Further, the step (30) blockchain multi-UAV consensus mechanism step includes:

[0032] (31) Each UAV n needs to maintain the task intention degree vector r n = [r 1,n , r 2,n ,..., r M,n ], the pre-allocated task vector x n = [x 1,n , x 2,n ,... x M,n ], the highest intention UAV vector f n = [f 1,n , f 2,n ,... f M,n ], wherein the element represents the UAV with the highest intention for the task m;

[0033] (32) In the tthconsensus phase, each UAV n broadcasts its last round of information r n (t-1), x n (t-1) and f n (t-1) to other UAVs, and receives its own information broadcast by other UAVs;

[0034] (33) Each UAV n takes the highest intention degree of the task as the task intention degree of the global consensus, that is:

[0035]

[0036] (34) Each UAV n selects the UAV with the highest intention degree of the task as the highest intention UAV of the current consensus, that is:

[0037]

[0038] Furthermore, step (40) of the multi-drone smart contract rule steps under the blockchain includes:

[0039] (41) If the task intention of UAV n itself is lower than that of other UAVs, then the pre-assignment of these tasks is considered to have failed, and these tasks need to be removed from the set of pre-assigned tasks in the previous round. The set of tasks to be removed is denoted as:

[0040]

[0041] in Let n be the set of pre-assigned tasks for drone n in the (t-1)th round;

[0042] (42) The drone n updates its reserved set of pre-assigned tasks and its set of selectable assigned tasks:

[0043]

[0044]

[0045] (43) The drone n calculates the number of remaining tasks it can perform:

[0046]

[0047] (44) The tasks of UAV n in the set of selectable assigned tasks In the middle, according to the matching degree s between the drone and the mission m,n Sort the data in descending order and select R based on a greedy strategy. n,rest The task with the highest matching degree to itself is selected as the newly added pre-assigned task.

[0048] (45) The drone n updates the pre-assigned task vector, i.e. Otherwise x m,n (t) = 0;

[0049] (46) For each newly added pre-assigned task, the drone n Update task intent, i.e.

[0050] r m,n (t)=r m,n (t)+s m,n (t)-s indexn,n (t)+ε,

[0051] Among them inde n x represents the set of tasks that can be assigned. The matching degree between the UAV and the mission (s)m,n R n,rest a large task sequence number, and epsilon is an arbitrarily small quantity;

[0052] (47) The UAV n updates its highest intention UAV f for each newly added pre-allocated task (48) The UAV n updates its highest intention UAV f for each newly added pre-allocated task m,n (t) = n.

[0053] Further, the step (50) of the UAV distributed generation of the collaborative perception scheme step includes:

[0054] (51) The UAV n initializes the task intention degree vector r n (t), the pre-allocated task vector x n (t) and the highest intention UAV vector f n (t) is a zero vector.

[0055] (52) The UAV n repeatedly executes the consensus mechanism in step (30) and the smart contract in step (40) until the consensus information is no longer updated, forming the final perception scheme.

[0056] (53) The UAV writes the final consensus collaborative perception scheme into the blockchain, and performs collaborative perception on the environment according to the scheme.

[0057] The beneficial effects of the present application are: The present application is aimed at the multi-UAV collaborative urban perception scene, introduces the blockchain technology into the UAV cluster collaborative perception problem, realizes the distributed control of the multi-UAV collaborative perception through the design of the consensus mechanism and the smart contract scheme, and enhances the robustness of the UAV cluster system. BRIEF DESCRIPTION OF DRAWINGS

[0058] The specific embodiments of the present application will be further illustrated below in combination with the drawings.

[0059] Figure 1 The overall workflow diagram of the present application;

[0060] Figure 2 The system model schematic diagram of the present application;

[0061] Figure 3 The performance comparison diagram of the method of the present application and other algorithms under different resource capacities. DETAILED DESCRIPTION

[0062] In combination Figure 1 , a multi-UAV collaborative urban perception method based on the blockchain, comprising the following steps:

[0063] (10) Multi-UAV task and state information sharing: Each UAV as a node, together to form a blockchain. UAVs receive information published by the city situation center, generate perception tasks, and publish them to the blockchain. At the same time, UAVs periodically publish their state information to the blockchain. UAVs share their task information and state information using broadcast communication and write them into blocks to form consensus. The network system model diagram is shown in FIG. 8. Figure 2

[0064] (11) UAVs generate a set of perception tasks based on their own perception information and information published by the city situation center Each task contains task importance, sensor type requirements, resource consumption, and information to be perceived, such as area search, target tracking, target identification, etc.

[0065] (12) UAVs periodically publish their state information to the blockchain, where the set of UAVs is denoted as The state information of UAV n includes the sensing radius S n , the type of sensor carried, and the resource capacity R n , etc. S n represents the maximum sensing range of UAV n, and R n represents the upper limit of the number of tasks that UAV n can perform.

[0066] (13) UAVs periodically share their task information and state information using broadcast communication and write them into blocks to form consensus through encryption and other processing.

[0067] (20) Distributed problem modeling for UAVs: UAVs distribute the modeling of the city perception problem based on shared task information and their own state information.

[0068] (21) Define the UAV and task connection matrix A M×N , where the element a m,n ∈{0,1} indicates whether UAV n can perform task m. If the sensor carried by UAV n meets the sensor type requirement of the task and the area corresponding to task m is within the sensing radius S n of the UAV, then a m,n =1. Otherwise, a m,n =0.

[0069] (22) Define the UAV perception scheme matrix X M×N , where the element x m,n ∈{0,1} indicates that UAV n performs perception task m. If UAV successfully performs perception task m, i.e., achieves perception of the relevant environment and obtains the corresponding perception benefit q m . ​

[0070] (23) The urban sensing benefit is defined as the sum of the benefits of multiple UAVs completing sensing tasks cooperatively. Under the constraint of UAV resources, the urban sensing benefit is maximized by optimizing the design of sensing schemes. The optimization problem can be modeled as:

[0071] P1:

[0072] s.t.

[0073]

[0074]

[0075] where the objective function is the urban sensing benefit, the first constraint indicates that the UAV n has limited resources and can only complete at most R n tasks, and the second constraint indicates that to avoid conflicts between multiple UAVs performing the same sensing task, at most one UAV can perform a sensing task.

[0076] (24) The task intention degree r m,n is introduced to represent the degree of willingness of the UAV n to perform the sensing task m. The higher the task intention degree, the greater the cost the UAV is willing to pay. The matching degree between the UAV and the task is defined as s m,n = a m,n q m -r m,n , i.e., the greater the difference between the task benefit and the task intention, the higher the matching degree between the UAV and the task, and the more likely the task is assigned to the corresponding UAV. At this time, the optimization problem P1 can be decomposed into N independent sub-problems to complete the distributed problem modeling. The sub-problem of the UAV n is:

[0077] P2:

[0078] s.t.

[0079]

[0080] (30) Multi-UAV consensus mechanism under blockchain: UAVs achieve consistent consensus of multi-UAV information based on the consensus mechanism of the blockchain by receiving broadcast information from other UAVs, and package the consensus information into the blockchain.

[0081] (31) Each UAV n needs to maintain a task intention degree vector r n = [r 1,n , r 2,n ,..., r M,n ] inside, and a pre-allocated task vector x n = [x1,n ,x 2,n ,...x M,n The highest intended drone vector f n =[f 1,n ,f 2,n ,...f M,n ], where elements This indicates the drone with the highest level of interest in task m.

[0082] (32) In the t-th round of consensus phase, each drone n transmits its information r from the previous round. n (t-1), x n (t-1) and f n (t-1) broadcasts to other drones across the network and simultaneously receives its own information broadcast by other drones.

[0083] (33) For each drone n, the highest task intention is taken as the consensus task intention across the entire network, i.e.:

[0084]

[0085] (34) For each drone n, the drone with the highest mission intention is selected as the highest intention drone in this round of consensus, that is:

[0086]

[0087] (40) Blockchain-based multi-drone smart contract rules: Based on the consensus information, update the task intention vector, pre-allocated task vector and the highest intention drone vector according to the smart contract rules.

[0088] (41) If the task intention of UAV n itself is lower than that of other UAVs, then the pre-assignment of these tasks is considered to have failed, and these tasks need to be removed from the set of pre-assigned tasks in the previous round. The set of tasks to be removed is denoted as:

[0089]

[0090] in Let be the set of pre-assigned tasks for drone n in round t-1.

[0091] (42) The drone n updates its reserved set of pre-assigned tasks and its set of selectable assigned tasks:

[0092]

[0093]

[0094] (43) The drone n calculates the number of remaining tasks it can perform:

[0095]

[0096] (44) The tasks of UAV n in the set of selectable assigned tasks In the middle, according to the matching degree s between the drone and the mission m,n Sort the data in descending order and select R based on a greedy strategy. n,rest The task with the highest matching degree to itself is selected as the newly added pre-assigned task.

[0097] (45) The drone n updates the pre-assigned task vector, i.e. Otherwise x m,n (t) = 0.

[0098] (46) For each newly added pre-assigned task, the drone n Update task intent, i.e.

[0099]

[0100] Where index n Indicates the set of tasks that can be selected for assignment. The matching degree between the UAV and the mission (s) m,n R n,rest +1 is the larger task number, and ε is an arbitrary small quantity.

[0101] (47) For each newly added pre-assigned task, the drone n Update its highest intended drone f m,n (t) = n.

[0102] (50) Distributed generation and collaborative perception scheme of UAVs: UAVs use an iterative mechanism to periodically execute information consensus and smart contracts until the consensus information is no longer updated, and the resulting consistent collaborative perception scheme is recorded in the blockchain.

[0103] (51) Initialize the task intention vector r of UAV n. n (t), pre-assigned task vector x n (t) and the vector f of the highest intended UAV n (t) is a vector of all zeros.

[0104] (52) The drone n repeatedly executes the consensus mechanism in step (30) and the smart contract in step (40) until the consensus information is no longer updated, thus forming the final perception scheme.

[0105] (53) The drone writes the final consistent collaborative perception scheme into the blockchain and performs collaborative perception of the environment according to the scheme.

[0106] Figure 3The proposed blockchain-based collaborative sensing scheme was compared with two other schemes to assess urban sensing benefits. The centralized optimal collaborative sensing scheme utilizes a central controller to collect information from all UAVs and uses a branch-and-bound algorithm to centrally find the optimal solution; the urban sensing benefits of this scheme can be considered as the upper bound of performance. The distributed greedy collaborative sensing scheme refers to UAVs greedily choosing tasks that maximize their own sensing benefits based solely on their own perceived information. It can be seen that the algorithm proposed in this invention can improve system robustness and reduce algorithm complexity in a distributed decision-making environment, while almost achieving the performance of a centralized algorithm. Furthermore, compared to similar distributed algorithms, this invention shows a significant performance improvement. These results fully demonstrate the superiority of the blockchain-based collaborative sensing scheme.

[0107] Compared with existing technologies, the present invention has the following significant advantages: it introduces blockchain technology into the optimization of drone control schemes, and through the reasonable design of consensus mechanisms and smart contract schemes, it realizes distributed and rapid control of drones, greatly improving the robustness and speed of drone collaborative perception, and can effectively perform collaborative perception of complex and highly dynamic urban environments.

[0108] Many specific details have been set forth in the foregoing description to provide a thorough understanding of the present invention. However, the above description is merely a preferred embodiment of the present invention, and the present invention can be implemented in many other ways different from those described herein. Therefore, the present invention is not limited to the specific embodiments disclosed above. Furthermore, any person skilled in the art can make many possible variations and modifications to the technical solutions of the present invention, or modify them into equivalent embodiments, using the methods and techniques disclosed above, without departing from the scope of the present invention. Any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention, without departing from the content of the present invention, shall still fall within the protection scope of the present invention.

Claims

1.A blockchain-based multi-UAV cooperative urban sensing method, characterized in that, Comprising the following steps: (10) Multi-UAV task and state information sharing: Each UAV as a node, together to form a blockchain; UAVs receive information published by the city situation center, generate perception tasks, and publish them to the blockchain; At the same time, the UAV periodically publishes its state information to the blockchain; UAVs share their task information and state information using broadcast communication and write them into blocks to form consensus; (20) Distributed problem modeling of UAVs: UAVs distribute the modeling of the UAV cooperative city perception problem based on shared task information and their own state information; (30) Multi-UAV consensus mechanism under blockchain: UAVs receive broadcast information from other UAVs, achieve consensus on multi-UAV information based on the consensus mechanism of the blockchain, and package the consensus information into the blockchain; (40) Multi-UAV smart contract rules under the blockchain: Based on the consensus information, update the task intention vector, pre-allocated task vector, and highest intention UAV vector according to the smart contract rules; (50) Distributed generation of cooperative perception scheme by UAVs: UAVs use an iterative mechanism to periodically execute information consensus and smart contracts until the consensus information no longer updates, and the formed consensus cooperative perception scheme is recorded in the blockchain; Step (10) Multi-UAV task and state information sharing step includes: (11) The UAV generates a set of perception tasks by itself sensing information and the information released by the city situation center Each task contains task importance, demand for sensor type, resource consumption and information to be perceived; (12) The UAV periodically publishes its state information to the blockchain, where the set of UAVs is denoted as ; the state information of a UAV includes its sensing radius , the types of sensors it carries, and its resource capacity ; represents the maximum sensing range of a UAV , represents the upper limit of the number of tasks a UAV can perform; (13) UAVs periodically share their task information and state information using broadcast communication, and write them into blocks through encryption processing to form consensus; Step (20) Distributed problem modeling of UAVs includes: (21) Define the connection matrix between UAV and mission , of which elements Indicates drone Is the task executable? If drone The onboard sensors meet the mission's sensor type requirements, and the mission... The corresponding area is within the drone's sensing radius. Inside, there is Conversely, there is ; (22) defining a drone perception scheme matrix wherein an element represents a drone performing a perception task ; if the drone successfully performs the perception task , i.e. achieves perception of the relevant environment, obtaining a corresponding perception reward ; (23) Define the city perception benefit as the sum of the benefits of multiple UAVs completing the perception task cooperatively. Under the constraint of UAV resources, optimize the perception scheme to maximize the city perception benefit. The optimization problem can be modeled as: ， ， ， ， where the objective function is the urban perception revenue, the first constraint indicates that the UAVs have limited resources and can only complete up to one task at a time, and the second constraint indicates that a perception task can only be performed by one UAV to avoid conflict between multiple UAVs performing the same perception task; (24) Introducing task intention Indicates drone For perception tasks The degree of willingness to perform a task; a higher level of task intention means the greater the cost the drone is willing to pay. The matching degree between the drone and the task is defined as... In other words, the greater the difference between the task reward and the task intention, the higher the matching degree between the drone and the task, and the more likely the task will be assigned to the corresponding drone; at this time, the optimization problem P1 can be decomposed into Each independent sub-problem is used to complete the distributed problem modeling; among them, the drone... The subproblems are: ， ， 。 2.The blockchain-based multi-UAV cooperative urban sensing method of claim 1, wherein, Step (30) Multi-UAV consensus mechanism under blockchain includes: (31) each drone internal maintenance task intention degree vector , pre-allocated task vector , highest intention drone vector wherein element represents the drone with the highest intention degree for task the highest intention drone; (32) In the first round of consensus, each UAV consensus phase, each UAV broadcasts its own information to other UAVs in the network, and receives the information broadcast by other UAVs in the network. , With the information of the last round, each UAV calculates the consensus result of the current round, and broadcasts the consensus result to other UAVs in the network. (33) each drone The task intention degree with the highest intention degree is taken as the task intention degree of the whole network, that is, ； (34) each UAV select the UAV with the highest degree of task intention as the highest intention UAV of the current round of consensus, that is: 。 3.The blockchain-based multi-UAV cooperative urban sensing method of claim 2, wherein, Step (40) Multi-UAV smart contract rules under the blockchain includes: (41) Unmanned aerial vehicle If the task intention degree of the self is lower than the task intention degrees of other unmanned aerial vehicles, it is considered that the task pre-allocation fails, and the tasks need to be removed from the task set pre-allocated in the last round. The removed task set is denoted as: ， wherein is a drone first pre-allocated task set of the wheel; (42) Unmanned aerial vehicle updating its set of reserved pre-allocated tasks and its set of selectable allocated tasks: ， ； (43) drone count the number of tasks remaining to be performed by the self: ； (44) unmanned aerial vehicle In the task set of the optional allocation task In the task set of the optional allocation task In the task set of the optional allocation task In the task set of the optional allocation task ; (45) drone updating the pre-allocated task vector, i.e. , else ; (46) drone for each newly added pre-allocated task , update the task intention degree, i.e. ， Wherein, representing a set of selectable allocation tasks the matching degree between the UAV and the task in the set the first the large task serial number, an arbitrarily small amount; (47) drone for each newly added pre-allocated task , update its highest intended drone . 4.The blockchain-based multi-UAV cooperative urban sensing method of claim 3, wherein, Step (50) Distributed generation of cooperative perception scheme by UAVs includes: (51) drone initialization task intention vector , pre-allocated task vector with the highest intention drone vector is a full zero vector; (52) Unmanned aerial vehicle The consensus mechanism in step (30) and the smart contract in step (40) are repeatedly executed until the consensus information is no longer updated, forming a final perception scheme. (53) UAVs write the final consensus cooperative perception scheme into the blockchain and perform cooperative perception on the environment according to the scheme.

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