CN110418390B

CN110418390B - Data transmission optimization method and system for low-altitude remote sensing and ground sensing

Info

Publication number: CN110418390B
Application number: CN201910522388.4A
Authority: CN
Inventors: 胡月明; 张飞扬; 陈联诚
Original assignee: South China Agricultural University
Current assignee: South China Agricultural University
Priority date: 2019-06-17
Filing date: 2019-06-17
Publication date: 2020-12-29
Anticipated expiration: 2039-06-17
Also published as: CN110418390A

Abstract

The invention discloses a data transmission optimization method and a data transmission optimization system for farmland quality monitoring low-altitude remote sensing and ground sensing, wherein the method comprises the following steps: calculating the maximum data transmission quantity of each ground node based on the low-altitude remote sensing route and the positions of the ground wireless sensor nodes; classifying based on the maximum data transmission quantity and the storage data quantity of each ground node to obtain classification nodes; performing close range data transmission optimization on the assistance nodes and the demand nodes based on a polling data distribution-maximum remaining energy routing algorithm; performing medium-distance data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-maximum remaining energy routing algorithm; and carrying out remote data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-diffusion path searching algorithm. In the embodiment of the invention, the sampling flight time and times are reduced, and the data transmission efficiency and energy loss between ground nodes are optimized.

Description

Data transmission optimization method and system for low-altitude remote sensing and ground sensing

Technical Field

The invention relates to the technical field of data output and optimization, in particular to a data transmission optimization method and system for farmland quality monitoring low-altitude remote sensing and ground sensing.

Background

The monitoring indexes for monitoring the farmland quality are large in number and variety, although most of farmland quality monitoring index data can be obtained by the conventional field investigation and laboratory test analysis method, the subjective influence of the field investigation is large, and the laboratory test is time-consuming and labor-consuming. If the long-term ground monitoring data and the high-spatial-resolution low-altitude remote sensing data of the monitoring area can be obtained, a new monitoring and analyzing method can be provided for the index of the farmland quality monitoring, and a more objective, convenient and efficient monitoring method and system are provided for the farmland quality monitoring. The existing wireless sensor network and the unmanned aerial vehicle can respectively provide ground long-term monitoring data and low-altitude remote sensing data.

A Wireless Sensor Network (WSN) for providing ground long-term monitoring data for monitoring the quality of cultivated land has the advantages of automatically acquiring and transmitting data, realizing long-term monitoring and the like, and is widely applied to the fields of environment monitoring, precision agriculture, cultivated land quality monitoring and the like.

The influence of the data volume of the farmland quality ground node long-term monitoring data on the convergence process is not considered in the prior art. The unmanned aerial vehicle has high flying speed, short time for passing through the effective communication range of the ground nodes, and limited data transmission rate of communication between the nodes, and in addition, the data of all nodes of the whole cluster need to be transmitted by the head node in the existing method, so that the situation that all data accumulated by long-term monitoring cannot be acquired by each round of flying in the existing method and the aggregation of a large amount of data accumulated by long-term monitoring needs to be completed by multiple rounds of flying in the existing method is likely to occur. This has just increased the operating time of arable land quality monitoring field sampling and unmanned aerial vehicle's energy consumption.

Disclosure of Invention

The invention aims to overcome the defects of the prior art, and provides a data transmission optimization method and a data transmission optimization system for farmland quality monitoring low-altitude remote sensing and ground sensing, which can reduce the sampling flight time and times and optimize the efficiency and energy loss of data transmission between ground nodes.

In order to solve the technical problem, an embodiment of the present invention provides a data transmission optimization method for farmland quality monitoring low-altitude remote sensing and ground sensing, where the method includes:

calculating the maximum data transmission quantity of each ground node based on the low-altitude remote sensing route and the positions of the ground wireless sensor nodes;

classifying based on the maximum data transmission quantity and the storage data quantity of each ground node to obtain classification nodes, wherein the classification nodes comprise an assistance node, a demand node and an optional routing node;

performing close range data transmission optimization on the assistance nodes and the demand nodes based on a polling data distribution-maximum remaining energy routing algorithm;

performing medium-distance data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-maximum remaining energy routing algorithm;

and carrying out remote data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-diffusion path searching algorithm.

Optionally, the calculating the maximum data transmission amount of each ground node includes:

calculating the length of the flight line of the unmanned aerial vehicle flying through the effective communication range of each ground node based on the flight line data of the unmanned aerial vehicle and the coordinate data of the ground nodes;

and calculating the maximum data transmission quantity of each ground node based on the flight speed of the unmanned aerial vehicle and the communication rate of the communication module of the ground node.

Optionally, the classifying based on the maximum data transmission amount and the storage data amount of each ground node to obtain the classification node includes:

acquiring the storage data volume of each ground node;

comparing the maximum data transmission quantity of each ground node with the stored data quantity, and classifying based on the comparison result to obtain a classification node;

when the maximum data transmission quantity is larger than the stored data quantity, the ground node is classified as an assistant node; when the maximum data transmission quantity is smaller than the stored data quantity, classifying the ground node into a demand node; when the maximum data transmission amount is equal to the stored data amount, the ground node is classified as an optional routing node.

Optionally, the performing, by the polling data allocation-maximum remaining energy routing algorithm, short-distance data transmission optimization on the assist node and the demand node includes:

judging whether the required node has an assistant node within a preset one-hop distance range;

if the required nodes have the assisting nodes within the preset one-hop distance range, each required node sends data of one unit to the assisting nodes within the preset one-hop distance, the data volume of the required nodes and the assisting nodes is updated, and whether the assisting nodes exist within the preset one-hop distance range or not is judged in a returning mode;

if the required node does not have the assisting node within the preset one-hop distance range, judging whether the required node has the assisting node within the preset two-hop distance range;

if the required nodes have the assisting nodes in the preset two-hop distance range, enumerating routes from each required node to the assisting nodes in the preset two-hop distance range, selecting the assisting nodes with the most residual electric quantity as transmission routes, and sending data of one unit to the assisting nodes in the preset two-hop distance by each required node;

updating the data volumes of the demand node and the assistance nodes, and returning to judge whether the demand node has the assistance nodes in the preset two-hop distance range;

and if the demand node does not have the assisting node within the preset two-hop distance range, calculating the energy consumption in the distribution process, and estimating the residual electric quantity after distribution.

Optionally, the optimizing the medium-distance data transmission of the assisting node and the demand node based on the greedy data distribution-maximum remaining energy routing algorithm includes:

judging whether the required node has an assistant node in a preset three-hop or four-hop distance range;

if the requirement node is judged to have the assistance node in the preset three-hop or four-hop distance range, sequencing the requirement node and the assistance node from big to small;

confirming an assisting node with the maximum assisting transmission data volume and a demand node with the maximum demand assisting transmission data volume in a preset three-hop or four-hop distance range based on the sequence from big to small;

comparing the maximum demand assistance transmission data volume of the demand node with the maximum assistance transmission data volume of the assistance node, and sending data to the assistance node by the demand node according to the comparison result;

updating the data volume of the demand nodes and the assisting nodes, enumerating the route from each demand node to the assisting nodes within the preset three-hop or four-hop distance range, selecting the one with the most residual electric quantity as a transmission route, and returning to judge whether the demand nodes have the assisting nodes within the preset three-hop or four-hop distance range;

and if the required node is judged to have no assisting node in the preset three-hop or four-hop distance range, calculating the energy consumption in the distribution process, and estimating the residual electric quantity after distribution.

Optionally, the sending, by the demand node, data to the assist node according to the comparison result includes:

when the maximum required assistance transmission data volume of the demand node is larger than the maximum assistance transmission data volume of the assistance node, the demand node sends the maximum assistance transmission data volume of the assistance node to the assistance node;

and when the maximum required assistance transmission data volume of the demand node is less than or equal to the maximum assistance transmission data volume of the assistance node, the demand node sends the maximum required assistance transmission data volume of the demand node to the assistance node.

Optionally, the greedy-based data distribution-diffusion routing algorithm optimizes remote data transmission between the assisting node and the demand node, and includes:

judging whether the required node has an assistant node beyond a preset four-hop distance;

if the demand node has the assistance node beyond the preset four-hop distance, sequencing the demand node and the assistance node from large to small;

confirming an assisting node with the maximum assisting transmission data volume and a demand node with the maximum demand assisting transmission data volume beyond a preset four-hop distance based on the sequence from large to small;

updating the data volume of the demand node and the assist node, and searching a route from the demand node to the assist node;

judging whether the distance requirement node has an assistant node beyond a preset four-hop distance, if so, selecting a search route as a transmission route from the requirement node to the assistant node, and if not, increasing the preset one-hop distance and returning to search the route from the requirement node to the assistant node;

and if the required node does not have the assisting node beyond the preset four-hop distance, finishing the data transmission optimization.

In addition, the embodiment of the invention also provides a data transmission optimization system for farmland quality monitoring low-altitude remote sensing and ground sensing, which comprises:

a calculation module: the system comprises a data acquisition unit, a data transmission unit and a data transmission unit, wherein the data acquisition unit is used for acquiring data of each ground node;

a node classification module: the method comprises the steps that classification is carried out on the basis of the maximum data transmission quantity and the storage data quantity of each ground node, and classification nodes are obtained and comprise an assisting node, a demand node and an optional routing node;

the first data transmission optimizing module: the system is used for carrying out close range data transmission optimization on the assistance nodes and the demand nodes based on a polling data distribution-maximum remaining energy routing algorithm;

the second data transmission optimizing module: the method is used for carrying out medium-distance data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-maximum remaining energy routing algorithm;

a third data transmission optimization module: the method is used for carrying out long-distance data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-diffusion path searching algorithm.

In the embodiment of the invention, the ground node data and the low-altitude remote sensing data can be acquired simultaneously in one flight, compared with the existing method, the method has the advantages of large total data transmission amount and low unit energy consumption, can realize load balance of data transmission, and can also give consideration to load balance of energy consumption to a certain extent; the flight time and the times required by sampling flight are shortened, the battery consumption of the unmanned aerial vehicle is reduced, the difficulty of planning the air route of the unmanned aerial vehicle is reduced, and the workload of workers is reduced; the larger total data transmission amount represents that the unmanned aerial vehicle does not need to carry out frequent sampling flight, and the energy consumption for carrying out data transmission between the ground nodes is reduced, so that the total life cycle of the ground nodes can be obviously prolonged.

Drawings

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

Fig. 1 is a schematic flow chart of a data transmission optimization method for low-altitude remote sensing and ground sensing in an embodiment of the present invention;

FIG. 2 is a schematic structural component diagram of a data transmission optimization system for low-altitude remote sensing and ground sensing in an embodiment of the present invention;

fig. 3 is a simulation case diagram of a data transmission optimization method for low-altitude remote sensing and ground sensing in the embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Examples

Referring to fig. 1, fig. 1 is a schematic flow chart of a data transmission optimization method for low-altitude remote sensing and ground sensing in an embodiment of the present invention.

As shown in FIG. 1, a data transmission optimization method for farmland quality monitoring low-altitude remote sensing and ground sensing comprises the following steps:

s11: calculating the maximum data transmission quantity of each ground node based on the low-altitude remote sensing route and the positions of the ground wireless sensor nodes;

in the specific implementation process of the invention, the calculating the maximum data transmission quantity of each ground node based on the low altitude remote sensing route and the ground wireless sensor node position comprises the following steps: calculating the length of the flight line of the unmanned aerial vehicle flying through the effective communication range of each ground node based on the flight line data of the unmanned aerial vehicle and the coordinate data of the ground nodes; and calculating the maximum data transmission quantity of each ground node based on the flight speed of the unmanned aerial vehicle and the communication rate of the communication module of the ground node.

Specifically, the lengths of the unmanned aerial vehicle air routes passing through the effective communication ranges of each ground node are different, so that the maximum data transmission amount of each ground node is different, and in order to optimize data transmission, the maximum data transmission amount of each ground node needs to be predicted.

Calculating the length of the air route passing through the effective communication range of each ground node according to the air route data of the unmanned aerial vehicle and the GPS coordinate point data of the ground nodes; calculating the maximum data transmission quantity of each ground node according to the flight speed of the unmanned aerial vehicle and the communication rate of the ZigBee module in the ground node; the specific calculation process is as follows:

assuming that the recording convergent node on the unmanned aerial vehicle takes 1 cm as a step length, the air route of the unmanned aerial vehicle is converted into m points (x)_m,y_m) (ii) a The flying height of the unmanned aerial vehicle is fixed h_uavThe coordinates and altitude of the n ground nodes are (x)_n,y_n,h_n) (ii) a The distance S (i, j) from each node for each corresponding location point is as follows:

wherein, (i ═ 1,2,3, …, m) (j ═ 1,2, … n); when the S (i, j) is smaller than the effective communication distance r of the ZigBee module, the unmanned aerial vehicle flying to the point i is in the effective communication range of the point j; thus, the length d of the flight line of the unmanned aerial vehicle passing through the effective communication range of each ground node is calculated_nThat is, the maximum data transfer amount T can be calculated by the following formula, as follows:

wherein j is 1,2, … n; v is the flight speed of the unmanned aerial vehicle, and B is the data transmission rate of the communication of the ZigBee module.

S12: classifying based on the maximum data transmission quantity and the storage data quantity of each ground node to obtain classification nodes, wherein the classification nodes comprise an assistance node, a demand node and an optional routing node;

in a specific implementation process of the present invention, the classifying based on the maximum data transmission amount and the storage data amount of each ground node to obtain the classification node includes: acquiring the storage data volume of each ground node; comparing the maximum data transmission quantity of each ground node with the stored data quantity, and classifying based on the comparison result to obtain a classification node; when the maximum data transmission quantity is larger than the stored data quantity, the ground node is classified as an assistant node; when the maximum data transmission quantity is smaller than the stored data quantity, classifying the ground node into a demand node; when the maximum data transmission amount is equal to the stored data amount, the ground node is classified as an optional routing node.

Specifically, before data transmission task allocation, ground nodes need to be classified, and roles each ground node plays in the data allocation process are determined; in this step, according to the maximum data transmission amount and the stored data amount of each ground node, all ground nodes are divided into assist nodes which have the remaining data transmission amount and can assist, the data transmission amount is smaller than the required nodes which need the assistance of other nodes and the routing nodes which have the data transmission amount equal to the stored data amount and only provide ground data transmission transfer for other nodes. The specific method comprises the following steps:

comparing the maximum data transmission quantity T and the stored data quantity D of each ground node; if T is greater than D, the ground node is used as an assisting node, and the amount of the available assisting distribution data is T-D; if T is less than D, the ground node is a demand node needing other nodes to assist data transmission, and the data volume needing to be assisted and distributed is D-T; if T ═ D, the ground node does not participate in assisted distribution, but acts as an optional routing node.

In the specific implementation process of the invention, a one-hop distance is preset, which is a distance that a demand node and an assistant node can directly communicate; presetting a two-hop distance, which is a distance between a demand node and an assistance node and is required to be transferred through 1 routing node; by analogy, presetting a three-hop distance which is a distance for transferring the demand node and the assistance node through 2 routing nodes; and presetting a four-hop distance, namely a distance for transferring the demand node and the assistance node through 3 routing nodes, and the like.

S13: performing close range data transmission optimization on the assistance nodes and the demand nodes based on a polling data distribution-maximum remaining energy routing algorithm;

in the specific implementation process of the present invention, the performing short-distance data transmission optimization on an assisting node and a requiring node based on a polling data allocation-maximum remaining energy routing algorithm includes: judging whether the required node has an assistant node within a preset one-hop distance range; if the required nodes have the assisting nodes within the preset one-hop distance range, each required node sends data of one unit to the assisting nodes within the preset one-hop distance, the data volume of the required nodes and the assisting nodes is updated, and whether the assisting nodes exist within the preset one-hop distance range or not is judged in a returning mode; if the required node does not have the assisting node within the preset one-hop distance range, judging whether the required node has the assisting node within the preset two-hop distance range; if the required nodes have the assisting nodes in the preset two-hop distance range, enumerating routes from each required node to the assisting nodes in the preset two-hop distance range, selecting the assisting nodes with the most residual electric quantity as transmission routes, and sending data of one unit to the assisting nodes in the preset two-hop distance by each required node; updating the data volumes of the demand node and the assistance nodes, and returning to judge whether the demand node has the assistance nodes in the preset two-hop distance range; and if the demand node does not have the assisting node within the preset two-hop distance range, calculating the energy consumption in the distribution process, and estimating the residual electric quantity after distribution.

Specifically, the short distance is that the demand node and the assist node can directly communicate, that is, 1 hop distance, or only needs to transit through 1 routing node, that is, 2 hop distance; in the process of short-distance data transmission, the energy consumed in communication is minimum, so that the data transmission is carried out by maximally utilizing the short distance; in the aspect of data transmission task allocation in the step, a data-by-data polling allocation algorithm of a Round Robin Scheduling algorithm (Round Robin Scheduling) is adopted, and in the aspect of routing, a maximum residual energy routing planning algorithm is adopted; therefore, each demand node can equally distribute the data transmission tasks to each auxiliary node in a short distance, uneven distribution of the data transmission tasks is prevented, and meanwhile, the nodes with more residual energy are used as routes to assist balance of the residual energy in the cluster.

Each demand node in each round of the ground node cluster sends a data volume to the assist nodes in the peripheral 1-hop range by taking the data volume of one piece of data as a step length, and the next round is started after the data volume distribution is finished until the data volume distribution of all the demand nodes is finished or the data volume of the assist nodes in the peripheral 1-hop range is finished; then, repeatedly polling neighboring nodes of two peripheral hops until the data volume of the required node is distributed or the data volume of the assisting node in the two peripheral hops is distributed; if the demand node has a plurality of routes to the assistant node, selecting the node with the most residual energy as the route; after distribution is completed, the assisting nodes and the demand node list are updated according to distribution tasks, the data quantity required to be sent or received by each demand node or assisting node is estimated, and the estimated residual electric quantity after the first round is calculated by combining the residual electric quantity of each demand node or assisting node.

S14: performing medium-distance data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-maximum remaining energy routing algorithm;

in the specific implementation process of the invention, the optimizing the medium-distance data transmission of the assisting node and the demand node based on the greedy data allocation-maximum remaining energy routing algorithm comprises the following steps: judging whether the required node has an assistant node in a preset three-hop or four-hop distance range; if the requirement node is judged to have the assistance node in the preset three-hop or four-hop distance range, sequencing the requirement node and the assistance node from big to small; confirming an assisting node with the maximum assisting transmission data volume and a demand node with the maximum demand assisting transmission data volume in a preset three-hop or four-hop distance range based on the sequence from big to small; comparing the maximum demand assistance transmission data volume of the demand node with the maximum assistance transmission data volume of the assistance node, and sending data to the assistance node by the demand node according to the comparison result; updating the data volume of the demand nodes and the assisting nodes, enumerating the route from each demand node to the assisting nodes within the preset three-hop or four-hop distance range, selecting the one with the most residual electric quantity as a transmission route, and returning to judge whether the demand nodes have the assisting nodes within the preset three-hop or four-hop distance range; and if the required node is judged to have no assisting node in the preset three-hop or four-hop distance range, calculating the energy consumption in the distribution process, and estimating the residual electric quantity after distribution.

Further, the step of sending data to the assisting node by the demand node according to the comparison result includes: when the maximum required assistance transmission data volume of the demand node is larger than the maximum assistance transmission data volume of the assistance node, the demand node sends the maximum assistance transmission data volume of the assistance node to the assistance node; and when the maximum required assistance transmission data volume of the demand node is less than or equal to the maximum assistance transmission data volume of the assistance node, the demand node sends the maximum required assistance transmission data volume of the demand node to the assistance node.

Specifically, the medium distance is that the demand node needs 3 hops or 4 hops to send data to the assist node; because the data transmission distance is slightly long, the number of routes for data transmission at the intermediate distance needs to be reduced, that is, the number of assisting nodes at the intermediate distance is reduced as much as possible, but the data amount allocated by each assisting node needs to be as much as possible; the calculation complexity of the algorithm is reduced while the energy consumed in data transmission is reduced.

In the step, a dynamic Greedy allocation Algorithm of a Greedy Algorithm (Greedy Algorithm) is adopted in the aspect of data transmission task allocation, and the routing selection is still the maximum remaining energy routing planning Algorithm; therefore, the data transmission tasks of the demand nodes with larger data volume can be preferentially met at the middle distance, and the energy consumed in transmission routing during data transmission task allocation is reduced.

After the short-distance data distribution, the ground nodes also comprise demand nodes and assistance nodes, a demand node list and an assistance node list are sorted from large to small, and data transmission task distribution is sequentially started according to the order of the demand nodes from large to small.

Extracting a demand node with the largest data quantity demand in each round, then searching from large to small according to the assistant node list, finding out the assistant nodes with the largest data quantity in the three-hop and four-hop ranges of the demand node in the assistant node list, and distributing tasks; if the data volume of the assisting node is larger than that of the demand node, the demand node task is distributed, and the assisting node renews the position in the assisting node list according to the distributed residual data volume; if the data volume of the assisting node is smaller than that of the demand node, the assisting node task is distributed completely, and the demand node renews the position in the demand node list according to the distributed residual data volume; if the data volume of the assisting node is equal to that of the demand node, the two nodes finish allocation at the same time; and if a plurality of routes exist from the demand node to the assistant node, selecting the node with the most residual energy after the first round as a data transmission route to finish a round of distribution. And if the demand nodes and the assistance nodes still exist, starting the next round until all the demand nodes are distributed completely or no assistance nodes exist in three hops and four hops of each demand node.

And after the distribution is finished, updating the list of the assistance nodes and the demand nodes according to the distribution task.

S15: and carrying out remote data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-diffusion path searching algorithm.

In the specific implementation process of the invention, the greedy-based data distribution-diffusion routing algorithm is used for carrying out remote data transmission optimization on the assisting nodes and the demand nodes, and comprises the following steps: judging whether the required node has an assistant node beyond a preset four-hop distance; if the demand node has the assistance node beyond the preset four-hop distance, sequencing the demand node and the assistance node from large to small; confirming an assisting node with the maximum assisting transmission data volume and a demand node with the maximum demand assisting transmission data volume beyond a preset four-hop distance based on the sequence from large to small; comparing the maximum demand assistance transmission data volume of the demand node with the maximum assistance transmission data volume of the assistance node, and sending data to the assistance node by the demand node according to the comparison result; updating the data volume of the demand node and the assist node, and searching a route from the demand node to the assist node; judging whether the distance requirement node has an assistant node beyond a preset four-hop distance, if so, selecting a search route as a transmission route from the requirement node to the assistant node, and if not, increasing the preset one-hop distance and returning to search the route from the requirement node to the assistant node; and if the required node does not have the assisting node beyond the preset four-hop distance, finishing the data transmission optimization.

Specifically, the long distance is that the distance between the demand node and the assist node is more than 4 hops away, and the specific hop count and the transmission route between the two nodes are not determined. Therefore, it is still necessary to allocate the assisting node with the largest data amount to the demand node with the largest data amount as much as possible, and to find the transmission route with the smallest hop count between the two nodes.

In the step, a greedy distribution algorithm is still adopted in the aspect of data transmission task distribution, and a shortest route search algorithm similar to a directional diffusion protocol is used for route selection; therefore, the number of routes allocated for data transmission can be reduced, and the number of route nodes passed by the route transmission from the demand node to the assist node is also reduced, so that the energy consumption of the whole allocation process is reduced.

After the short-distance and medium-distance data are distributed, the ground nodes also comprise demand nodes and assistance nodes, a demand node list and an assistance node list are sorted from large to small, and data transmission task distribution is started in sequence according to the order of the demand nodes from large to small; extracting a demand node with the largest data quantity demand in each round, and distributing the demand node to an assistant node with the largest data quantity; if the data volume of the assisting node is larger than that of the demand node, the demand node task is distributed, and the assisting node renews the position in the assisting node list according to the distributed residual data volume; if the data volume of the assisting node is smaller than that of the demand node, the assisting node task is distributed completely, and the demand node renews the position in the demand node list according to the distributed residual data volume; and if the data quantity of the assisting node is equal to that of the demand node, the two nodes finish allocation at the same time.

After the assisting node corresponding to the demand node is determined, searching a new node which can be contacted by adding one hop from the demand node as a starting point by using a one-hop neighbor node list of each node until the corresponding assisting node is found; the search route from the demand node to the assist node is the minimum hop count route; and after one round of distribution is finished, if the demand nodes and the assistance nodes still exist, starting the next round until all the demand nodes or all the assistance nodes are distributed.

So far, after the algorithm is executed, no node needing other nodes to assist data transmission exists in the whole ground node cluster, or the maximum data transmission quantity of all the nodes is fully utilized.

Examples

Referring to fig. 2, fig. 2 is a schematic structural composition diagram of a data transmission optimization system for low-altitude remote sensing and ground sensing in an embodiment of the present invention.

As shown in fig. 2, a data transmission optimization system for farmland quality monitoring low-altitude remote sensing and ground sensing, the system comprises:

the calculation module 11: the system comprises a data acquisition unit, a data transmission unit and a data transmission unit, wherein the data acquisition unit is used for acquiring data of each ground node;

The node classification module 12: the method comprises the steps that classification is carried out on the basis of the maximum data transmission quantity and the storage data quantity of each ground node, and classification nodes are obtained and comprise an assisting node, a demand node and an optional routing node;

The first data transmission optimization module 13: the system is used for carrying out close range data transmission optimization on the assistance nodes and the demand nodes based on a polling data distribution-maximum remaining energy routing algorithm;

The second data transmission optimization module 14: the method is used for carrying out medium-distance data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-maximum remaining energy routing algorithm;

The third data transmission optimization module 15: the method is used for carrying out long-distance data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-diffusion path searching algorithm.

Referring to fig. 3, fig. 3 is a simulation example of a data transmission optimization method for low-altitude remote sensing and ground sensing in an embodiment of the present invention.

As shown in fig. 3, assuming that 10 ground nodes are deployed in a 500 × 500 m area, the flight speed of the unmanned aerial vehicle is 10 m/s, the precision required by the low-altitude remote sensing data is 4 cm/pixel, and the sidewise overlap rate is 60%, then the flight height is 92 m at this time, and the interval between the main routes is 112 m; assuming that each ground node carries 4 sensors, for example, A, B, C three-layer soil water content and ground surface temperature, or air temperature, humidity, illumination intensity and rainfall are collected, each piece of data is about 0.03KB, and one piece of data is collected every hour, the data volume needing to be transmitted by each node after 25 days is 18.56 KB; the actual data transmission rate of ZigBee is about 3KB/s, so that the total time for transmitting all data of the ground node cluster is about 62 seconds, namely 620 meters in the whole route are in data transmission.

Firstly, preprocessing; the length of the flight line of the unmanned aerial vehicle passing through the effective communication range of the ground nodes 0, 3, 5 and 8 is short, so that the nodes 0, 3, 5 and 8 need to send the data of the nodes to the surrounding ground nodes for transmission, namely the demand nodes. The length of the flight line of the drone through the effective communication range of the nodes 1,2, 4, 5, 6, 7, 9 is long, so that data can be transmitted to the drone for other nodes, i.e. the assisting node.

Secondly, short-distance distribution; each round of demand nodes sends a unit of data to the assisting nodes in the range of 1 hop or 2 hops.

For example, ground node 0 sends a single unit of data to ground nodes 1,2 at 1 hop distance and ground node 9 at 2 hop distance, respectively; if the next round of ground node 0 still has data to send and ground nodes 1,2, 9 can still have remaining assistance data, ground node 0 continues to send a single unit of data to nodes 1,2, 9. In the process, as two routes from the node 0 to the node 9 are selectable, namely 0 → 1 → 9 and 0 → 2 → 9, the residual capacities of the node 1 and the node 2 are compared, and the most routes are selected as the routes of the transmission.

Thirdly, distributing middle distance; the demand node with the largest residual data amount in the demand nodes firstly selects the assisting node with the largest residual data amount in the range of 3 hops and 4 hops.

If the ground node 5 still has data to send and has the most residual data amount among the demand nodes, selecting the node with the most residual data amount which can assist from the ground nodes 1,2 and 4 hops with the 3-hop distance among the ground nodes 9; assuming that the remaining data volume of the ground node 9 is large and larger than the data volume required by the ground node 5, the ground node 5 sends all the data to the ground node 9; at this time, two-hop routing is available from the ground node 5 to the ground node 9, i.e., 5 → 3 → 0 → 1 → 9 and 5 → 3 → 0 → 2 → 9, and the ground node with the largest residual capacity after the short-distance distribution is selected as the routing.

Fourthly, remote distribution; and selecting the assisting node with the maximum residual data amount in the cluster from the demand nodes with the maximum residual data amount.

If the last terrestrial node 8 needs other nodes to assist in sending data and the terrestrial node 4 has the remaining amount of data to assist, then the terrestrial node 3 uses the diffusion routing to find the route with the least number of transit nodes from the terrestrial node 8 to the terrestrial node 4 as the transmission route, i.e., 8 → 5 → 3 → 0 → 2 → 4.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.

In addition, the data transmission optimization method and system for farmland quality monitoring low-altitude remote sensing and ground sensing provided by the embodiment of the invention are introduced in detail, a specific example is adopted to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A data transmission optimization method for farmland quality monitoring low-altitude remote sensing and ground sensing is characterized by comprising the following steps:

carrying out remote data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-diffusion path-finding algorithm;

the close range data transmission optimization of the assistance nodes and the demand nodes based on the polling data distribution-maximum remaining energy routing algorithm comprises the following steps:

if the demand node does not have the assisting node within the preset two-hop distance range, calculating the energy consumption in the distribution process, and estimating the residual electric quantity after distribution;

the greedy data distribution-based maximum remaining energy routing algorithm is used for carrying out medium-distance data transmission optimization on the assisting nodes and the demand nodes and comprises the following steps:

if the fact that the required node does not have the assisting node in the preset three-hop or four-hop distance range is judged, calculating energy consumption in the distribution process, and estimating the distributed residual electric quantity;

the greedy data distribution-diffusion path-finding algorithm based remote data transmission optimization for the assisting nodes and the demand nodes comprises the following steps:

2. The data transmission optimization method of claim 1, wherein the calculating the maximum data transmission amount of each ground node based on the low altitude remote sensing route and the ground wireless sensor node position comprises:

3. The data transmission optimization method according to claim 1, wherein the classifying based on the maximum data transmission amount and the storage data amount of each ground node to obtain the classification node comprises:

acquiring the storage data volume of each ground node;

4. The data transmission optimization method of claim 1, wherein the requiring node sends data to the assisting node according to the comparison result, and the method comprises the following steps:

5. The data transmission optimization method of claim 1, wherein the requiring node sends data to the assisting node according to the comparison result, and the method comprises the following steps:

6. A data transmission optimization system for farmland quality monitoring low-altitude remote sensing and ground sensing is characterized by comprising:

a third data transmission optimization module: the method is used for carrying out long-distance data transmission optimization on the assisting nodes and the demand nodes based on a greedy data distribution-diffusion path searching algorithm;

the first data transmission optimization module: the method is also used for judging whether the required node has an assistant node within a preset one-hop distance range; if the required nodes have the assisting nodes within the preset one-hop distance range, each required node sends data of one unit to the assisting nodes within the preset one-hop distance, the data volume of the required nodes and the assisting nodes is updated, and whether the assisting nodes exist within the preset one-hop distance range or not is judged in a returning mode; if the required node does not have the assisting node within the preset one-hop distance range, judging whether the required node has the assisting node within the preset two-hop distance range; if the required nodes have the assisting nodes in the preset two-hop distance range, enumerating routes from each required node to the assisting nodes in the preset two-hop distance range, selecting the assisting nodes with the most residual electric quantity as transmission routes, and sending data of one unit to the assisting nodes in the preset two-hop distance by each required node; updating the data volumes of the demand node and the assistance nodes, and returning to judge whether the demand node has the assistance nodes in the preset two-hop distance range; if the demand node does not have the assisting node within the preset two-hop distance range, calculating the energy consumption in the distribution process, and estimating the residual electric quantity after distribution;

the second data transmission optimizing module: the method is also used for judging whether the required node has an assistant node in a preset three-hop or four-hop distance range; if the requirement node is judged to have the assistance node in the preset three-hop or four-hop distance range, sequencing the requirement node and the assistance node from big to small; confirming an assisting node with the maximum assisting transmission data volume and a demand node with the maximum demand assisting transmission data volume in a preset three-hop or four-hop distance range based on the sequence from big to small; comparing the maximum demand assistance transmission data volume of the demand node with the maximum assistance transmission data volume of the assistance node, and sending data to the assistance node by the demand node according to the comparison result; updating the data volume of the demand nodes and the assisting nodes, enumerating the route from each demand node to the assisting nodes within the preset three-hop or four-hop distance range, selecting the one with the most residual electric quantity as a transmission route, and returning to judge whether the demand nodes have the assisting nodes within the preset three-hop or four-hop distance range; if the fact that the required node does not have the assisting node in the preset three-hop or four-hop distance range is judged, calculating energy consumption in the distribution process, and estimating the distributed residual electric quantity;

the third data transmission optimization module: the method is also used for judging whether the required node has an assistant node beyond a preset four-hop distance; if the demand node has the assistance node beyond the preset four-hop distance, sequencing the demand node and the assistance node from large to small; confirming an assisting node with the maximum assisting transmission data volume and a demand node with the maximum demand assisting transmission data volume beyond a preset four-hop distance based on the sequence from large to small; comparing the maximum demand assistance transmission data volume of the demand node with the maximum assistance transmission data volume of the assistance node, and sending data to the assistance node by the demand node according to the comparison result; updating the data volume of the demand node and the assist node, and searching a route from the demand node to the assist node; judging whether the distance requirement node has an assistant node beyond a preset four-hop distance, if so, selecting a search route as a transmission route from the requirement node to the assistant node, and if not, increasing the preset one-hop distance and returning to search the route from the requirement node to the assistant node; and if the required node does not have the assisting node beyond the preset four-hop distance, finishing the data transmission optimization.