CN114727359A

CN114727359A - Unmanned aerial vehicle-assisted post-disaster clustering mine Internet of things data acquisition method

Info

Publication number: CN114727359A
Application number: CN202210355564.1A
Authority: CN
Inventors: 赵清; 杨维
Original assignee: Beijing Jiaotong University
Current assignee: Beijing Jiaotong University
Priority date: 2022-04-06
Filing date: 2022-04-06
Publication date: 2022-07-08

Abstract

The invention provides an unmanned aerial vehicle-assisted post-disaster clustering mine Internet of things data acquisition method. The method comprises the following steps: clustering all Internet of things nodes in the mine after the disaster through a post-disaster mine Internet of things node clustering algorithm; finding a group of cluster heads minimizing energy consumption of the mine internet of things data acquisition system in each cluster through a post-disaster mine internet of things unmanned aerial vehicle data acquisition path planning algorithm, and determining the sequence of the group of cluster heads as a data acquisition path of each cluster head by the unmanned aerial vehicle; the unmanned aerial vehicle starts from the starting point, traverses all cluster heads according to the planned flight path for data acquisition, and finally returns to the starting point. According to the invention, the unmanned aerial vehicle is used for assisting the mine Internet of things to complete the post-disaster data acquisition task, so that the energy consumption of the cluster head node is reduced, and the unmanned aerial vehicle path planning algorithm comprehensively considers the data acquisition energy consumption of the Internet of things node and the unmanned aerial vehicle, thereby prolonging the network lifetime of the post-disaster mine Internet of things.

Description

Unmanned aerial vehicle-assisted post-disaster clustering mine Internet of things data acquisition method

Technical Field

The invention relates to the technical field of media communication, in particular to a post-disaster clustering mine Internet of things data acquisition method assisted by an Unmanned Aerial Vehicle (UAV).

Background

When the mine disaster is rescued, the key element of accident rescue is to quickly and accurately determine the position of trapped personnel. The traditional rescue mode is that rescue workers enter a disaster area through a roadway to carry out rescue. However, in the disaster period, the roadway environment is complex and changeable, and if the roadway environment passes through the disaster tradition, the danger is high, secondary damage is easily caused, and great difficulty is brought to disaster area rescue detection. At present, rescue robots are used for replacing rescuers to enter disaster areas, acquiring various environmental parameters in time and transmitting the environmental parameters back to rescue commands in real time. However, the rescue robot has the disadvantages of large volume, poor flexibility, weak obstacle-crossing capability and the like, and cannot effectively meet the actual requirements. The unmanned aerial vehicle has the remarkable advantages of small size, low manufacturing cost, no casualty risk, high flexibility and the like, and is widely applied to various fields such as target tracking, emergency communication, environment monitoring and the like.

The mine detection unmanned aerial vehicle can enter an underground roadway to carry out real-time detection, the sensors are utilized to collect and process underground information such as gas concentration, oxygen concentration, environment temperature, wounded position and the like, and the information is transmitted to a rescue command center in real time through data and video and audio, so that decision basis is provided for rescue command personnel, and the possibility of casualty accidents can be reduced to the maximum extent.

At present, in the prior art, a routing method for post-disaster mine internet of things node multi-hop transmission includes: aiming at the situation that the energy of the sensing node of the internet of things in the mine underground after disaster is difficult to supplement in time, an energy-saving clustering routing protocol based on two-stage cluster head election is provided. In an initial cluster head election stage, a node with energy level higher than the average energy of a network is screened out by introducing a node energy factor to serve as a candidate cluster head node; and introducing a communication factor according to the communication condition of adjacent nodes, designing an election probability function of the post-disaster reconstruction network based on the energy factor and the communication factor, and performing cluster head election through calculation of the election probability function to form an initial clustering structure. In the network operation stage, an intra-cluster election cluster head mechanism is constructed, and whether cluster heads are replaced or not is determined by periodically comparing the residual energy of each cluster head node with the threshold energy.

The routing method for multi-hop transmission of the nodes of the internet of things of the mine after disaster in the prior art has the following defects: the method ignores the problem that the number of residual nodes after disaster cannot meet the coverage of the whole roadway area, and the transmission of a large amount of monitoring data can cause the death of the surviving nodes too fast, thereby interrupting the data communication process between the ground and the accident roadway.

Therefore, in order to meet the reliability requirement of data acquisition of the internet of things of the mine after the disaster, a data acquisition system architecture of the internet of things of the mine after the disaster, which is assisted by an Unmanned Aerial Vehicle (UAV), is constructed.

In the prior art, a wireless sensor network data acquisition method based on unmanned aerial vehicle assistance includes: and taking the robot or the unmanned aerial vehicle as a mobile sink node, and acquiring data of all nodes of the Internet of things through a planned path. The use of the robot or the unmanned aerial vehicle obviously reduces the energy consumption caused by multi-hop transmission between nodes, prolongs the service life of the network and reduces the transmission delay. In addition, the method can solve the problems of routing holes and hot spots which often appear in the Internet of things of the mine after disasters. At present, application research of unmanned aerial vehicles in mine internet of things mainly focuses on positioning and hardware analysis, and few researches are conducted on post-disaster mine internet of things data acquisition methods assisted by unmanned aerial vehicles.

The above-mentioned wireless sensor network data acquisition method based on unmanned aerial vehicle is supplementary among the prior art's shortcoming does: this method cannot be directly applied downhole. The following problems exist in the post-disaster mine for auxiliary data acquisition by using an unmanned aerial vehicle: firstly, the service range of the unmanned aerial vehicle is limited by the long-time and large-range flight limitation of a mine tunnel due to the limited energy carried by the unmanned aerial vehicle; secondly, the battery life of the internet of things node that survives is limited, and the battery is difficult to change under most circumstances, and frequent data communication with unmanned aerial vehicle will lead to node energy to be exhausted rapidly, and then interrupt with unmanned aerial vehicle's data transmission.

Disclosure of Invention

The embodiment of the invention provides an unmanned aerial vehicle-assisted post-disaster clustering mine Internet of things data acquisition method, which aims to effectively utilize an unmanned aerial vehicle-assisted mine Internet of things to complete a post-disaster data acquisition task.

In order to achieve the purpose, the invention adopts the following technical scheme.

An unmanned aerial vehicle-assisted post-disaster clustering mine Internet of things data acquisition method comprises the following steps:

clustering all Internet of things nodes in the mine after the disaster through a post-disaster mine Internet of things node clustering algorithm;

finding a group of cluster heads minimizing the total energy consumption of the mine Internet of things data acquisition system in each cluster through a post-disaster unmanned aerial vehicle data acquisition path planning algorithm, and determining the sequence of the group of cluster heads as the flight path of the unmanned aerial vehicle;

the unmanned aerial vehicle starts from the starting point, traverses all cluster heads according to the planned flight path for data acquisition, and finally returns to the starting point.

Preferably, the clustering of all internet of things nodes in the post-disaster mine through the post-disaster mine internet of things node clustering algorithm includes:

setting a post-disaster roadway area into a strip-shaped rectangular space with the length of L and the width of W, randomly dispersing N Internet of things nodes surviving in the post-disaster roadway area in the whole network, dividing the N Internet of things nodes into K clusters, and expressing the cluster set as { Q₁,Q₂,…,Q_k,…,Q_KDenotes the number of nodes contained in each cluster as | Q }_k|，k∈[1,K]The cluster head set is denoted as { C }₁,C₂,C_k,,C_K}；

The optimized objective function for the value of K is as follows:

where φ is a weighting factor that balances node energy consumption and UAV trajectory length, E_k ⁿIs Q_kMiddle node S_k ⁿEnergy consumption of d_UAV→kRepresenting unmanned aerial vehicle to cluster Q_kThe distance of the center;

assume that K takes the value [ K ]_min,K_max]，0＜K_min≤K≤K_max< N, using the variance of the distance from the node to the cluster center as the value of the cost function, is calculated as follows:

drawing a relation curve of the value SSE of the cost function and the K, and determining the K value corresponding to the inflection point of the elbow as a clustering number K value when the relation curve is elbow-shaped;

and dividing N nodes of the Internet of things into K clusters through a K-means clustering algorithm based on the K value of the clustering number.

Preferably, the dividing N internet of things nodes into K clusters by a K-means clustering algorithm based on the clustering number K value includes:

1) determining the number N of nodes of the post-disaster Internet of things and the number K of clusters;

2) randomly selecting K nodes from N nodes of the Internet of things as initial cluster centers, wherein the distance between the K initial cluster centers is larger than a preset distance threshold value d_th；

3) For each node remaining, compute it to the center of each cluster

And selecting the closest cluster to join, wherein the distance from the node newly joining the cluster to the cluster head must be less than the communication radius r₀；

4) After the division of all the nodes is completed, the cluster center of each cluster is updated

The new cluster center coordinate is the average value of the cluster node coordinates, and a round of iteration is completed at the moment;

5) and 3) taking the new cluster center as a similarity measure, and repeating the steps 3) and 4) until the nodes in each cluster are not changed any more, and determining the clustering result of the current Internet of things node as a final clustering result.

Preferably, the finding a group of cluster heads that minimizes the total energy consumption of the mine internet of things data acquisition system in each cluster through a post-disaster unmanned aerial vehicle data acquisition path planning algorithm, and determining the sequence of the group of cluster heads as the flight path of the unmanned aerial vehicle includes:

the unmanned aerial vehicle collects all clusters (Q) in the roadway according to a certain path₁,Q₂…,Q_KGiven the initial position S of the drone₀Then the original flight path of the drone is represented as

Total energy consumption E of mine Internet of things data acquisition system assisted by post-disaster unmanned aerial vehicle is defined_totThe input sequence (S) of the post-disaster mine Internet of things unmanned aerial vehicle data acquisition path planning algorithm is the sum of the total energy consumption of the Internet of things nodes and the total energy consumption of the unmanned aerial vehicle under one round of data acquisition₀,Q₁,Q₂,…Q_KThe sequence is composed of the starting point of the unmanned plane and all clusters, and the sequence is output (S)₀,S₁,S₂,…S_K,S₀The access sequence of the selected cluster heads, namely the data acquisition path of the unmanned aerial vehicle, is formed by an encoder and a decoder network, wherein the encoder network is used for acquiring a pointer network of a post-disaster mine Internet of things unmanned aerial vehicle data acquisition path planning algorithm

Representing each element, and adopting a recurrent neural network constructed by long and short time memory LSTM units as an encoder network;

the post-disaster mine Internet of things unmanned aerial vehicle data acquisition path planning algorithm is described as follows:

step 1, an original path is processed

Inputting into encoder network, inputting sequence by using LSTM unit

A sequence of e converted to a latent memory state{e₀,…,e_K}，

Step 2, the output sequence epsilon of the encoder is changed to { e }₀,,e_KIs input into the decoder network, at each instant t, the decoder is based on the hidden state h of the previous instant knowledge_tAnd { e₀,…,e_tOutput an access decision pi_tObtaining an input sequence

According to the input sequence

Constructing a (K +2) search graph of one layer for all clusters;

step 3, in order to traverse all nodes, in the path searching process, a candidate node set containing the nodes to be inquired is set as an OPEN list, the inquired nodes are set as a CLOSED list, the neighbor nodes of any node positioned in any layer are defined as all nodes in the upper layer and the lower layer, each node is provided with a pointer pointing to the father node of the node, and the mapping is realized by COME _ FROM;

initialize OPEN, CLOSED, and COME _ FROM, let f (S)₀) 0, starting point S₀Adding into an OPEN table;

and 4, searching a node q with the minimum value of f (q) in an OPEN table, acquiring all adjacent nodes of the node q, and calculating the total energy consumption Cost for any adjacent node m, namely Cost ═ g (q) + E (q → m), wherein E (q → m) represents the energy consumption generated by the UAV from the node q to the node m.

Step 5, if the adjacent node m is in OPEN and cost is less than g (m), removing the node from OPEN; if the neighbor node m is in the CLOSED and cost < g (m), then the node is removed from the CLOSED; otherwise, let g (m) cost, f (m) g (m) + h (m), add node m to OPEN with its parent set to q;

repeating steps 4-5 until the OPEN table is empty, if q is S₀Then construct the slave S by COME _ FROM₀To S₀One path of (1), output minimum energy consumption E_totAnd a new acquisition path

According to the technical scheme provided by the embodiment of the invention, the energy consumption of the cluster head node is reduced by using the unmanned aerial vehicle to assist the mine Internet of things to complete the post-disaster data acquisition task, and the proposed unmanned aerial vehicle path planning algorithm comprehensively considers the data acquisition energy consumption of the Internet of things node and the unmanned aerial vehicle, so that the network lifetime of the post-disaster mine Internet of things is prolonged.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic diagram of an architecture of a post-disaster unmanned aerial vehicle-assisted clustered mine internet of things system provided by an embodiment of the present invention;

fig. 2 is a flowchart of a post-disaster mine internet of things node clustering algorithm provided by the embodiment of the invention;

fig. 3 is a schematic diagram of an algorithm for planning a data acquisition path of an unmanned aerial vehicle for a post-disaster mine internet of things according to an embodiment of the invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.

Under normal conditions, the mine Internet of things utilizes a large number of Internet of things nodes randomly deployed in a roadway to complete data acquisition and transmission tasks of underground environment and personnel information. However, most of internet of things nodes are damaged due to mine accidents, and reliable data transmission cannot be guaranteed for the surviving internet of things nodes, so that the unmanned aerial vehicle can be used for entering underground to complete the task of acquiring and transmitting data information of the accident roadway after the accidents. The energy efficiency optimization of the unmanned aerial vehicle and the Internet of things node is a key problem to be solved urgently in the data acquisition of the mine Internet of things after disaster. In order to solve the energy consumption problem of the post-disaster mine Internet of things data acquisition system and prolong the network lifetime of the mine Internet of things, the embodiment of the invention provides an unmanned aerial vehicle-assisted post-disaster clustering mine Internet of things data acquisition method. The method plays an important role in improving the energy efficiency and reliability of the post-disaster mine Internet of things data acquisition system.

The architecture of the post-disaster unmanned aerial vehicle-assisted clustered mine Internet of things system constructed in the embodiment of the invention is shown in fig. 1, a post-disaster roadway area is set to be a strip-shaped rectangular space with the length of L and the width of W, and 1 rescue unmanned aerial vehicle is used for data acquisition after a disaster. The N surviving nodes of the Internet of things are randomly dispersed in the whole network and used for collecting and transmitting the environmental parameters of the accident scene. Assuming that all AP (Access Point, wireless Access Point) devices in the roadway are damaged after disaster, only one Sink node is deployed at the road junction. The surviving nodes of the Internet of things are divided into K clusters according to a certain clustering algorithm, only one node in each cluster is selected as a cluster head, and the member nodes transmit the acquired data to the cluster heads. The drone then moves at a constant speed v_UAVFrom a starting point S₀Starting, traversing all cluster heads according to a certain acquisition path to acquire data, and finally returning to the starting point S₀。

Denote the cluster set in FIG. 1 as { Q₁,Q₂,…,Q_k,,Q_KThe number of nodes contained in each cluster is denoted as | Q_k|，k∈[1,K]The cluster head set is denoted as { C₁,C₂…,C_k,,C_K}. Denote the sampling point set of the drone as S₁,,S_K}. Defining the positions of all nodes and the unmanned aerial vehicle by adopting a three-dimensional coordinate system, and then clustering Q_kCluster head coordinate of C_k(x_k,y_k,0). Suppose that the drone is in cluster head C_kData acquisition is carried out right above the sampling point S_kHas the coordinates of (x)_k,y_kH), where H is the fixed height at which the drone is flying. Cluster Q_kMiddle nth member node

Has the coordinates of

n＝1,…,|Q _k1. Assuming that the Sink node knows the initial information of all the surviving nodes, each Internet of things node has a unique ID and the same communication radius r₀And the nodes can exchange information with other nodes or unmanned planes within the communication range. In addition, all nodes calculate and store the position information of the nodes according to the existing positioning algorithm, and the unmanned aerial vehicle is provided with satellite positioning equipment to be positioned in real time. Because the batteries of the nodes in the mine environment after disaster are difficult to replace, the nodes of the Internet of things die if the initial energy of the nodes of the Internet of things is exhausted.

On the basis of fig. 1, the embodiment of the invention provides an unmanned aerial vehicle-assisted post-disaster clustering type mine internet of things energy-saving data acquisition method, firstly provides a post-disaster mine internet of things node clustering algorithm to complete clustering of all internet of things nodes, then provides a post-disaster mine internet of things unmanned aerial vehicle data acquisition path planning algorithm, finds a group of cluster heads minimizing energy consumption of a mine internet of things data acquisition system in determined clusters, and the sequencing of the cluster heads is a data acquisition path of an unmanned aerial vehicle.

The flow chart of the post-disaster mine internet of things node clustering algorithm provided by the embodiment of the invention is shown in fig. 2, and comprises the following processing procedures:

as can be seen from fig. 1, under the condition that the roadway area is fixed, as K increases, the distance between the nodes in the cluster becomes closer and closer, so that the transmission distance between the nodes and the cluster head gradually decreases, and the energy consumption of the nodes naturally decreases. However, at the same time, the number of cluster heads that the drone needs to visit increases, the trajectory length of the drone becomes longer, and the energy consumption of the drone also increases. Therefore, the confirmed K value affects the performance of the mine Internet of things clustering after disasters, and the optimization of the K value needs to comprehensively consider the energy consumption of the Internet of things nodes and the track length factor of the unmanned aerial vehicle. The objective function is as follows:

wherein, phi is a weighting factor used for balancing the node energy consumption and the unmanned aerial vehicle track length, and is set to be 0.5.

Is Q_kMiddle node

Energy consumption of d_UAV→kIndicating unmanned aerial vehicle to cluster Q_kThe distance of the center.

Assume that K takes the value [ K ]_min,K_max]，(0＜K_min≤K≤K_max< N), and then obtaining a value influencing the cost function by drawing a relation curve between the cost and the K in the interval. Using the variance of the distances from the node to the cluster center, i.e., the sum of squared distance errors (SSE), as a cost function, the following is calculated:

SSE reflects the density degree of nodes in each cluster after clustering, and the smaller the SSE value is, the denser the sample points in each cluster are. The basis for selecting the optimal K is as follows: when K is less than the optimal cluster number, the increase of K greatly accelerates the convergence of the nodes in each cluster, thereby greatly reducing the SSE of the nodes. When K reaches the optimal cluster number, the convergence by increasing K is rapidly smaller, so the SSE drops sharply and then gradually flattens as K continues to increase. This means that the curve of the relationship between SSE and K has an elbow shape, and the value corresponding to this inflection point is the optimum K.

Then, the nodes of the Internet of things after the disaster are divided through a K-means clustering algorithm, the purpose is to divide N nodes into K clusters, and the nodes in the clusters in the Internet of things system of the mine after the disaster are ensured to be as compact as possible, and the nodes among different clusters are separated as far as possible. Using Euclidean distance as similarity measurement of clustering, and putting nodes into clustersThe distance of the centers serves as an objective function for optimization. In FIG. 1, clusters are assumed to be

Then Q is_kMiddle nth node

To the center of its cluster

Is calculated as

And obtaining the optimal cluster with the minimum node distance through multiple iterations according to the distance from the node to the cluster center. Then the objective function of the clustering is

Therefore, the node clustering algorithm of the post-disaster internet of things based on the given K value is described as follows:

1) and determining the number N of the nodes of the post-disaster Internet of things and the number K of clusters.

2) And randomly selecting K nodes from the N nodes of the Internet of things as an initial cluster center. Since the initial cluster centers affect the clustering result and the iteration number, the distance between the K initial cluster centers must be greater than a preset distance threshold d_th. Assuming that p (0 < p < K) initial cluster centers are selected, only p cluster centers are spaced at least d apart from the p cluster centers when the p +1 th cluster center is selected_thMay be selected.

3) For each node remaining, compute it to each cluster center

And selecting the closest cluster to join. In order to ensure coverage, the distance from the node newly joining the cluster to the cluster head must beLess than the communication radius r₀。

The new cluster center coordinates are the average of the cluster node coordinates, and a round of iteration is completed at the moment.

5) And 3) taking the new cluster center as a similarity measure, repeating the steps 3) and 4) until the nodes in each cluster are not changed any more, and determining the clustering result of the current nodes of the Internet of things as a final clustering result.

The schematic diagram of the data acquisition path planning algorithm of the post-disaster mine Internet of things unmanned aerial vehicle provided by the embodiment of the invention is shown in fig. 3, and the specific processing process comprises the following steps: in the system shown in fig. 1, after a disaster, all nodes of the internet of things are divided into K clusters, and an unmanned aerial vehicle needs to collect all clusters { Q ] in a roadway according to a certain path₁,Q₂…,Q_KThe data of. Giving initial position S of drone₀Then the original flight path of the drone is represented as

The total energy consumption of the mine internet of things data acquisition system assisted by the post-disaster unmanned aerial vehicle is defined as the sum of the total energy consumption of the internet of things nodes and the total energy consumption of the unmanned aerial vehicle under one round of data acquisition, and is calculated as follows:

wherein, omega (omega is more than or equal to 0 and less than or equal to 1) is a weight coefficient for adjusting energy consumption of all internet of things nodes and energy consumption of the unmanned aerial vehicle after the disaster, and the value is 0.5.

Is a member node

To cluster head C_kEnergy consumption for data transmission，E_kIs a cluster head C_kEnergy consumption to receive data),

is a cluster head C_kEnergy consumption for transmitting data to unmanned aerial vehicle, E_flightFor unmanned aerial vehicle flight energy consumption),

for unmanned aerial vehicle at S_kThe sampling energy consumption of (c).

The node energy consumption model of the Internet of things is as follows:

and calculating the energy consumption of the nodes of the Internet of things by adopting a first-order radio model. The energy consumption of the transmitting node comprises a transmitting circuit and a power amplifier, and the energy consumption of the receiving node is generated by a receiving circuit. If the distance between the transmitting node and the receiving node is less than a threshold value, a free space model is adopted. Otherwise, a multipath fading model is adopted. In FIG. 1, member nodes

To cluster head C_kThe energy consumption for transmitting l-bit data is calculated as follows:

wherein E is_elecIs the circuit energy consumed to transmit or receive 1bit data.

Is a member node n (n ═ 1, …, | Q_k1) with cluster head C_kThe distance between them. d₀Is the distance threshold value that is set to be,

ε_fsand epsilon_mpEnergy parameters of the radio frequency amplifier of the free space model and the multipath fading model are respectively represented. Thus, cluster head C_kThe energy consumption for receiving the l-bit data is calculated as

E_k＝lE_elec (8)

Assuming that the data acquisition amount of each round of all member nodes is l bit, the cluster head C_kThe energy consumed to transmit data to the UAV is expressed as

Wherein, T_kIs UAV at S_kThe time of the hover.

In a complete data acquisition task, the member nodes transmit data to respective cluster heads, the cluster heads transmit the data to the UAVs, and then the total energy consumption E of all nodes of the Internet of things_IoTNIs shown as

The energy consumption model of the unmanned aerial vehicle is as follows: as shown in FIG. 1, when the UAV is at a fixed speed v_UAVFly to the sampling point S_kThen, it transmits a beacon frame to transmit the corresponding cluster head C_kWaking up from a sleep mode to an active mode. Then, cluster head C_kData is initially collected from its member nodes by time division multiple access and the collected data is forwarded to the UAV. The energy consumption of one-round data acquisition of the UAV mainly comprises flight energy consumption and sampling energy consumption, wherein the sampling energy consumption of the UAV comprises hovering energy consumption and communication energy consumption.

The hovering power of the UAV is calculated as follows

Wherein g is the gravity of the earth, ρ is the air density, n_pIs the number of rotors, r_pIs the radius of the rotor, m_totMass of UAV. For ease of analysis, assume that the UAV hover time at a time is equal to the cluster head to UAV data transmission time. Then UAV is at S_kThe energy consumption of the process is calculated as

Wherein, P_hoverAnd P_comRespectively the hover power and the communications power of the UAV,

is a cluster head C_kTotal amount of data transmitted to the UAV.

Assuming horizontal movement power as UAV flight velocity v_UAVIs expressed as

Wherein v is_maxIs the maximum flight speed, P, of the UAV_maxAnd P_idleHardware power for the UAV when moving at full speed and in an idle state, respectively. The UAV needs to start after hovering at the sampling point, so that the flight energy consumption of the UAV consists of hovering energy consumption and mobile energy consumption, and the flight energy consumption of the UAV is calculated as follows

E_flight＝T_flight(P_hover+P_move) (14)

Wherein, T_flightIs the total time of flight of the UAV, expressed as

Wherein S is { S ═ S₀,S₁,…,S_K}，S_kFrom C_kIt is determined that,

is S_iAnd S_jThe Euclidean distance between the two electrodes,

is a binary indicator function if the UAV is from point S_iMove to point S_j，

If not, then,

therefore, the total energy consumption for a data acquisition round of the UAV is expressed as follows

In order to minimize total energy consumption E of post-disaster unmanned aerial vehicle-assisted mine Internet of things data acquisition system_totExpressing the data acquisition path after the unmanned plane planning as

Wherein pi_tRepresenting original path of unmanned aerial vehicle

Any one node is located at

The t-th position of (a). Thus, for a given input path

According to the chain rule, output path

The probability of (c) can be factorized by the product of the conditional probabilities, i.e.:

where t is the time step, parameterized by θ

And (4) determining a random strategy of the unmanned aerial vehicle acquisition sequence. Raw drone path according to inputs

And clustering information collected at the time of t-1, conditional probability P (pi)_tI) can calculate the probability that other clusters were collected at time t.

The probability of any cluster being visited at time t is modeled. Trained theta can give post-disaster unmanned aerial vehicle assisted mine internet of things data acquisition system energy consumption E_totLow path assignment higher probability, giving system energy consumption E_totHigh path assignment lower probability, where the best model strategy theta can be trained using the actor-judger method in the reinforcement learning algorithm^*。

Under the condition of giving a clustering number K, the problem of planning the data acquisition path of the post-disaster unmanned aerial vehicle can be regarded as a sequence decision problem. Fig. 3 is a schematic diagram of an algorithm for planning a data acquisition path of an unmanned aerial vehicle for the mine internet of things after disaster. In FIG. 3, the sequence S is input₀,Q₁,Q₂,…Q_KThe sequence is composed of the starting point of the unmanned plane and all clusters, and the sequence is output (S)₀,S₁,S₂,…S_K,S₀And f, the access sequence of the selected group of cluster heads is the data acquisition path of the unmanned aerial vehicle. In fig. 3, the encoder and decoder networks constitute a pointer network of the proposed drone path planning algorithm. Encoder network for acquisition

A representation of each element in (a). First, a Recurrent Neural Network (RNN) constructed by Long Short-Term Memory (LSTM) units capable of learning Long-Term dependency is used as an encoder.

Including a start position S₀And cluster set { Q₁,Q₂…,Q_KAre converted into a D-dimensional vector space, enabling the strategy to extract useful features more efficiently in the converted space. The embedded vector is then input into the LSTM unit. In each encoding step, the LSTM unit reads an embedded entry and outputs a potential memory state. Finally, the input sequence is input

A sequence e converted to a latent memory state₀,…,e_K}，

In FIG. 3, the output of the encoder { e }₀,…,e_KIs provided to the decoder network. At each decoding instant t, the LSTM unit outputs a hidden state containing knowledge of the previous instant

The decoder then applies an attention mechanism based on h_tAnd { e₀,…,e_tOutput an access decision pi_t. At decoding time t, the selection has the maximum conditional probability P (pi)_tI) as the node to be acquired, the calculation is as follows

Wherein, W₁,

In order to be the attention matrix,

as the attention vector, W₁,W₂And

denoted by theta, which is a learnable parameter in the proposed network of unmanned aerial vehicle path planning algorithms,

is node j (e) at time t_j) An associated score number.

Computing raw paths by normalizing exponential functions

Conditional probability of a node not yet visited, i.e. of

Wherein, the probability P (pi)_tJ | ·) represents the extent to which the model points to node j at decoding time t.

A search graph is constructed for all clusters, each layer consisting of nodes of one cluster, according to the output sequence of the decoder network. The start position of the first layer and the end position of the last layer are both S₀. Thus, the created pattern has a total of (K +2) layers. Then, the optimal cluster head is searched from each cluster, and the total energy consumption cost E from the starting position to the ending position is constructed_totThe smallest path. In each iteration, the cost of traversing the path and the estimated cost required to extend the path to the end need to be calculated to determine to extend its partial path into a longer path. Any node m is selected for access according to the following function

f(m)＝g(m)+h(m) (9)

Wherein g (m) denotes the unmanned aerial vehicle from the start node S₀Accurate energy consumption of the system when moving to candidate node m, h (m) is that the unmanned aerial vehicle moves from candidate node m to terminal point S₀The estimated energy consumption. Then, the node with the minimum f (m) value is selected from the candidate nodes as the next node to be visited.

In order to traverse all the nodes, in the path searching process, a candidate node set containing the nodes to be queried is set as an OPEN list. The queried node is set to the CLOSED list. The neighbor nodes of any node located at an arbitrary level are defined as all nodes in the upper and lower levels thereof. In addition, each node has a pointer to its parent node, which is implemented by the map COME _ FROM.

Therefore, the post-disaster mine internet of things unmanned aerial vehicle data acquisition path planning algorithm is described as follows:

step 1, an original path is processed

Inputting into encoder network, and inputting sequence by using LSTM unit

A sequence e converted to a latent memory state₀,…,e_K}，

Step 2, the output sequence epsilon of the encoder is changed to { e }₀,…,e_KIs input into the decoder network, at each instant t, the decoder is based on the hidden state h of the previous instant knowledge_tAnd { e₀,…,e_tOutput an access decision pi_t. Obtaining an input sequence

According to the input sequence

Constructing a (K +2) search graph of one layer for all clusters;

step 3, initialize OPEN, CLOSED table and COME _ FROM, order f (S)₀) 0, starting point S₀Adding into an OPEN table;

Step 5, if the adjacent node m is in OPEN and cost is less than g (m), removing the node from OPEN; if the neighbor node m is in CLOSED and cost < g (m), then the node is removed from CLOSED. Otherwise, let g (m) cost, f (m) g (m) + h (m), add node m to OPEN with its parent set to q.

In summary, the unmanned aerial vehicle is used for assisting data acquisition of the post-disaster mine internet of things, so that energy consumption of the cluster head nodes is reduced, the unmanned aerial vehicle path planning algorithm comprehensively considers the energy consumption of the internet of things nodes and the data acquisition of the unmanned aerial vehicle, and the network lifetime of the post-disaster mine internet of things is prolonged. The networking method has the advantages that the networking method is used for reconstructing the nodes of the Internet of things which survive after the disaster, and the data acquisition energy consumption of the nodes of the Internet of things is remarkably reduced.

The clustering problem of the residual nodes in the post-disaster unmanned aerial vehicle auxiliary mine Internet of things data acquisition system is solved. A post-disaster Internet of things node clustering algorithm is provided, and the optimal clustering number K is determined by comprehensively considering the data acquisition energy consumption of post-disaster nodes and the unmanned aerial vehicle acquisition path length. The problem of route planning of unmanned aerial vehicle in the post-disaster unmanned aerial vehicle auxiliary mine thing networking data acquisition system is solved, a post-disaster mine thing networking unmanned aerial vehicle data acquisition route planning algorithm is provided, and the selected group of cluster heads and cluster head sequencing comprehensively considers the acquisition energy consumption of thing networking nodes and the acquisition energy consumption of unmanned aerial vehicle.

Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.

From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of software products, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement without inventive effort.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An unmanned aerial vehicle-assisted post-disaster clustering mine Internet of things data acquisition method is characterized by comprising the following steps:

2. The method of claim 1, wherein the clustering of all internet of things nodes in the post-disaster mine through a post-disaster mine internet of things node clustering algorithm comprises:

setting a post-disaster roadway area into a strip-shaped rectangular space with the length of L and the width of W, randomly dispersing N Internet of things nodes surviving in the post-disaster roadway area in the whole network, dividing the N Internet of things nodes into K clusters, and expressing the cluster set as { Q₁,Q₂,...,Q_k,...,Q_KThe number of nodes contained in each cluster is denoted as | Q_k|，k∈[1,K]The cluster head set is denoted as { C₁,C₂...,C_k,...,C_K}；

The optimized objective function for the value of K is as follows:

wherein phi is a weighting factor used for balancing the node energy consumption and the unmanned aerial vehicle track length,

is Q_kMiddle node

Energy consumption of d_UAV→kIndicating unmanned aerial vehicle to cluster Q_kThe distance of the center;

3. The method according to claim 2, wherein the dividing N internet of things nodes into K clusters by a K-means clustering algorithm based on the K-value of the clustering number comprises:

3) For each node remaining, compute it to each cluster center

4. The method according to claim 2 or 3, wherein the step of finding a group of cluster heads which minimize the total energy consumption of the mine Internet of things data acquisition system in each cluster through a post-disaster unmanned aerial vehicle data acquisition path planning algorithm, and determining the sequence of the group of cluster heads as the flight path of the unmanned aerial vehicle comprises the following steps:

the unmanned aerial vehicle collects all clusters (Q) in the roadway according to a certain path₁,Q₂...,Q_KGiven the initial position S of the drone₀Then the original flight path of the drone is represented as

Total energy consumption E of mine Internet of things data acquisition system assisted by post-disaster unmanned aerial vehicle is defined_totThe input sequence (S) of the post-disaster mine Internet of things unmanned aerial vehicle data acquisition path planning algorithm is the sum of the total energy consumption of the Internet of things nodes and the total energy consumption of the unmanned aerial vehicle under one round of data acquisition₀,Q₁,Q₂,...Q_KThe sequence is composed of the starting point of the unmanned plane and all clusters, and the sequence is output (S)₀,S₁,S₂,...S_K,S₀The access sequence of the selected cluster heads, namely the data acquisition path of the unmanned aerial vehicle, is formed by an encoder and a decoder network, wherein the encoder network is used for acquiring a pointer network of a post-disaster mine Internet of things unmanned aerial vehicle data acquisition path planning algorithm

step 1, an original path is processed

Inputting into encoder network, inputting sequence by using LSTM unit

A sequence e converted to a latent memory state₀,...,e_K}，

Step 2, the output sequence epsilon of the encoder is changed to { e }₀,...,e_KIs input into the decoder network, at each instant t, the decoder is based on the hidden state h of the previous instant knowledge_tAnd { e₀,...,e_tOutput an access decision pi_tObtaining an input sequence

According to the input sequence

Constructing a (K +2) search graph of one layer for all clusters;

step 3, in order to traverse all nodes, in the process of path search, a candidate node set containing the nodes to be inquired is set as an OPEN list, the inquired nodes are set as a CLOSED list, the neighbor nodes of any node positioned in any layer are defined as all nodes in the upper layer and the lower layer, each node is provided with a pointer pointing to the father node of the node, and the mapping is realized by COME _ FROM;