CN109068333B - Forest fire monitoring incremental node expansion method and system based on position optimization - Google Patents
Forest fire monitoring incremental node expansion method and system based on position optimization Download PDFInfo
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Abstract
The invention provides a forest fire monitoring incremental node expansion method and system based on position optimization, wherein the method comprises the following steps: verifying that the forest fire monitoring system has the characteristic of a submodule after the new monitoring node is added; acquiring a monitoring node deployment position of a target forest fire monitoring system based on the characteristics of the submodels; the deployment positions of the monitoring nodes comprise redeployment positions of the existing monitoring nodes and deployment positions of newly-built monitoring nodes; and calculating the number of monitoring nodes with different strategy requirements according to the deployment positions of the monitoring nodes to obtain a monitoring node position set. According to the invention, the monitoring nodes of the target forest fire monitoring system are deployed according to the position optimization utility of the sub-model characteristics, so that a relatively optimized deployment mode of the forest monitoring nodes can be obtained.
Description
Technical Field
The invention relates to the technical field of fire monitoring, in particular to a forest fire monitoring incremental node expansion method and system based on position optimization.
Background
With the development of the internet of things technology, the lookout tower of the high-definition video camera equipment can monitor forests and acquire real-time and comprehensive field data in time through sensing and image data processing technologies, and therefore fire monitoring efficiency is improved. In addition, the renewable energy sources are used for supplying power, the wireless network supports real-time data transmission, and the forest fire monitoring node directly reduces the labor cost and provides continuous monitoring coverage of a target area. This is currently a viable solution for early monitoring system expansion. For the purpose of maximizing the visual field, these camera-equipped monitoring nodes are usually located at a high level, such as the top of a mountain or the ridge. Monitoring node visual field analysis is important for determining the location of candidate monitoring nodes. In addition, the selection of candidate nodes is also limited by minimum overlap, maximum coverage and budget.
Forest fires cause devastating damage and irreparable damage to the environment and atmosphere. With the development of economy, people pay more and more attention to the influence on the environment particularly in developing countries. The area of early monitoring systems is also requiring continued expansion as economies develop to shorten response times and reduce potential damage and fire costs. Typically, a new monitoring node is added to maximize coverage of the area that has not yet been covered. When the original monitoring system is expanded, the original forest fire monitoring is still continued. That is, it is desirable to have higher cost-effectiveness for the added nodes, while also maximizing the performance of the original monitoring system. Therefore, how to select the deployment mode (including the deployment position, the deployment number and the like) of the fire monitoring nodes becomes a problem to be solved.
Disclosure of Invention
In view of this, the present invention aims to provide a forest fire monitoring incremental node expansion method and system based on location optimization, which deploys monitoring nodes of a forest fire monitoring system according to location optimization utility of sub-model characteristics, and can obtain a deployment mode in which forest monitoring nodes are optimized.
In a first aspect, an embodiment of the present invention provides a forest fire monitoring incremental node deployment method based on location optimization, where the method includes:
verifying that the forest fire monitoring system has the characteristic of a submodule after the new monitoring node is added;
acquiring a monitoring node deployment position of a target forest fire monitoring system based on the characteristics of the submodels; the deployment positions of the monitoring nodes comprise a redeployment position of an existing monitoring node and a newly-built monitoring node deployment position;
and calculating the number of monitoring nodes with different strategy requirements according to the deployment positions of the monitoring nodes to obtain a monitoring node position set.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the target forest fire monitoring system includes a forest fire monitoring system to be built and a forest monitoring system already built, and the step of obtaining a deployment position of a monitoring node of the forest fire monitoring system based on the characteristics of the submodel includes:
screening out candidate positions of monitoring nodes according to the terrain position of a forest area aiming at a forest fire monitoring system to be built, and selecting a monitoring node deployment position of the forest fire monitoring system from the candidate positions of the monitoring nodes based on the characteristics of the submodels;
and aiming at the established forest fire monitoring system, redeploying or expanding the monitoring nodes on the basis of the established monitoring nodes based on the characteristics of the submodels to obtain the deployment positions of the monitoring nodes of the forest fire monitoring system.
With reference to the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the step of verifying that the forest fire monitoring system has a sub-model characteristic after a new monitoring node is added includes:
evaluating the coverage quality increase value of the radius area of the newly added monitoring node;
and determining that the forest fire monitoring system has the sub-model characteristic after the monitoring nodes are newly added according to the coverage quality added value.
With reference to the second possible implementation manner of the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the step of evaluating a coverage quality increase value of the radius area of the newly added monitoring node includes:
acquiring a visual field of a candidate monitoring node of the forest fire monitoring system, and marking the monitoring area coverage quality of the candidate monitoring node;
acquiring coverage quality corresponding to the radius area of the candidate monitoring node according to the coverage quality of the monitoring area;
and evaluating the coverage quality increase value of the radius area of the newly added monitoring node according to the coverage quality corresponding to the radius area of the candidate monitoring node.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the step of obtaining a deployment position of a monitoring node of a target forest fire monitoring system based on the characteristics of the submodels includes:
acquiring the cost of a candidate monitoring node position set of the target forest fire monitoring system;
calculating the monitoring node with the maximum coverage quality increase value which obeys the preset condition constraint according to the cost of the candidate monitoring node position set;
taking the candidate position corresponding to the monitoring node which meets the preset condition and has the maximum coverage quality increment value as the deployment position of the monitoring node
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the candidate monitoring nodes include a plurality of nodes, and the step of calculating, according to the cost of the candidate monitoring node location set, the monitoring node with the maximum coverage quality increase value that is subject to a preset condition constraint includes:
acquiring a first monitoring node with a maximum coverage quality increase value in all the candidate monitoring nodes;
continuously acquiring a second monitoring node with the largest coverage quality increase value in the remaining candidate monitoring nodes except the first monitoring node;
by analogy, a plurality of monitoring nodes with the maximum coverage quality increase value and calculated for multiple times are obtained;
and selecting a monitoring node which is subject to preset condition constraint from a plurality of monitoring nodes with the maximum coverage quality increasing value according to the cost of the candidate monitoring node position set.
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where the step of calculating, according to the cost of the candidate monitoring node location set, the monitoring node with the maximum coverage quality increase value that obeys a preset condition constraint includes:
calculating the monitoring node with the maximum coverage quality increase value subject to the preset condition constraint according to the following formula:
wherein, the p is*Increasing a set of value monitoring nodes for the maximum coverage quality; f (p) is a coverage quality assessment function; the above-mentioned
Representing that P is the node with the largest coverage quality increment value; the C (p) is a cost of the set of candidate monitoring node locations; the B represents the preset condition.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a seventh possible implementation manner of the first aspect, where the step of calculating, according to the deployment location of the monitoring nodes, the number of monitoring nodes with different policy requirements includes:
aiming at a forest fire monitoring system to be built, calculating the number of monitoring nodes of a full coverage strategy at minimum cost according to the deployment positions of the monitoring nodes;
or, calculating the number of monitoring nodes of the maximum coverage strategy under the limit of cost according to the deployment position of the monitoring nodes;
aiming at the established forest fire monitoring system, calculating the number of monitoring nodes for the fully compatible system expansion strategy according to the deployment positions of the monitoring nodes;
or calculating the number of monitoring nodes of part of compatible extension strategies of cost performance constraints according to the deployment positions of the monitoring nodes.
In a second aspect, an embodiment of the present invention further provides a forest fire monitoring incremental node expansion system based on location optimization, where the system includes:
the verification module is used for verifying that the forest fire monitoring system has the sub-module characteristic after the monitoring nodes are newly added;
the position acquisition module is used for acquiring the deployment position of a monitoring node of the target forest fire monitoring system based on the characteristics of the submodels;
and the calculation module is used for calculating the number of the monitoring nodes with different strategy requirements according to the deployment positions of the monitoring nodes to obtain a monitoring node position set.
With reference to the second aspect, an embodiment of the present invention provides a first possible implementation manner of the second aspect, where the target forest fire monitoring system includes a forest fire monitoring system to be built and a forest monitoring system already built, and the location obtaining module includes:
the first position acquisition unit is used for screening out candidate positions of monitoring nodes according to the terrain position of a forest area aiming at a forest fire monitoring system to be built, and selecting a monitoring node deployment position of the forest fire monitoring system from the candidate positions of the monitoring nodes based on the characteristics of the submodels;
and the second position acquisition unit is used for redeploying or expanding the monitoring nodes on the basis of the established monitoring nodes based on the characteristics of the submodels aiming at the established forest fire monitoring system to obtain the deployment positions of the monitoring nodes of the forest fire monitoring system.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a forest fire monitoring incremental node expansion method and system based on position optimization, wherein the method comprises the following steps: verifying that the forest fire monitoring system has the characteristic of a submodule after the new monitoring node is added; acquiring a monitoring node deployment position of a target forest fire monitoring system based on the characteristics of the submodels; the deployment positions of the monitoring nodes comprise a redeployment position of the existing monitoring node and a newly-built deployment position of the monitoring node; and calculating the number of monitoring nodes with different strategy requirements according to the deployment positions of the monitoring nodes to obtain a monitoring node position set. And deploying the monitoring nodes of the target forest fire monitoring system according to the position optimization utility of the submodel characteristics, so that a deployment mode of the forest monitoring nodes which is relatively optimized can be obtained.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1(a) -1(c) are schematic diagrams illustrating a monitoring node location deployment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a forest fire monitoring incremental node expansion method based on location optimization according to an embodiment of the present invention;
FIG. 3 is a flowchart of a method in step S102 of a forest fire monitoring incremental node expansion method based on location optimization according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a candidate monitoring node position in a deployment area according to an embodiment of the present invention;
FIG. 5 is a full coverage schematic at minimum cost provided by an embodiment of the present invention;
FIGS. 6(a) -6(d) are schematic diagrams of maximum coverage under different budget constraints provided by embodiments of the present invention;
FIG. 7 is a diagram illustrating coverage results of an original system according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a system coverage result after full-compatibility extension according to an embodiment of the present invention;
FIG. 9 is a diagram illustrating system coverage results after partial compatibility expansion of cost/performance constraints, according to an embodiment of the present invention;
fig. 10 is a schematic diagram of a forest fire monitoring incremental node expansion system based on location optimization according to an embodiment of the present invention.
Icon: 10-a verification module; 20-a position acquisition module; 30-a calculation module.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. 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.
At present, how to select the deployment mode (including the deployment position, the deployment number and the like) of the fire monitoring nodes becomes a problem to be solved. Based on the method and the system for expanding the forest fire monitoring incremental node based on the position optimization, provided by the embodiment of the invention, the monitoring nodes of the forest fire monitoring system are deployed according to the position optimization effect of the sub-model characteristics, so that a deployment mode of the forest monitoring nodes which is optimized can be obtained.
In order to facilitate understanding of the embodiment, a detailed description is first given to a forest fire monitoring incremental node expansion method based on location optimization, which is disclosed in the embodiment of the present invention.
As shown in FIG. 1(b), the monitoring node position Po in the original EDS (Early Detection System) is { s }0，s1，s2}. For the newly added monitoring node position s epsilon V, the position set Pe is { s ∈ V0，s1，s2} U { s }. At which locations monitoring nodes are deployed depends on whether or not these locations maximize the system coverage area. Furthermore, when the system adds a new monitoring node, this location should have a high added value to the original system (covering the most uncovered area). Theoretically, this problem can be described by a set V of n elements, with the collection P being the m s of the set V0，s1，s2，...，smSubset, such that the union of P equals V. The area covered by the monitoring node is evaluated using the f(s) function. Aggregating systems for all locations
It keeps F (Po) less than or equal to F (Pe). It is obviously non-decrementing, and
is zero.
As shown in FIG. 1(a), the set of monitor node locations Po in the original EDS system is { s }0，s1，s2，s3}. As the economy evolves, new monitoring nodes will be added to expand the original EDS system. And for the newly added monitoring node position s ∈ V, the expanded system position set Pe is { s ∈ V0，s1，s2，s3} U { s }. They have a general character by practical observation. If the monitoring nodes in the network are sparse, the total coverage area of the system can be rapidly increased when a new monitoring node is added into the system; whereas for denser target areas, only a lower value-added level is obtained. This is because the regions with higher node density have more redundant viewable areas.
A better revenue to cost ratio may be obtained by making a trade-off between coverage and cost. If certain conditions are met (e.g., trade-off between coverage gain and cost), the total coverage area may be increased by relocating existing monitoring nodes or optimizing the location of new monitoring nodes, as shown in fig. 1 (c). In order to reduce the cost, the monitoring nodes are expected to be deployed at the positions where the information acquisition is maximum. Therefore, it is desirable to select a group
The set of locations maximizes the coverage information quantity evaluation function F (P), but is subject to a limit on the number of monitoring nodes that can be deployed, i.e., | P ≦ k.
As shown in fig. 2, the embodiment provides a forest fire monitoring incremental monitoring node expansion method based on location optimization, and the method includes the following steps:
step S101, verifying that the forest fire monitoring system has the sub-module characteristic after the monitoring nodes are newly added;
firstly, evaluating a coverage quality increase value of a radius area of a newly added monitoring node; the specific process is as follows: acquiring a visual field of a candidate monitoring node of a forest fire monitoring system, and marking the monitoring area coverage quality of the candidate monitoring node; acquiring coverage quality corresponding to the radius area of the candidate monitoring node according to the coverage quality of the monitoring area; and evaluating the coverage quality increase value of the radius area of the newly added monitoring node according to the coverage quality corresponding to the radius area of the candidate monitoring node. And then, determining that the original monitoring system has the sub-mode characteristic after the monitoring node is newly added according to the coverage quality increase value.
(1) Evaluating the visual field of the candidate monitoring node: according to input parameters including monitoring node Elevation (SPOT), monitoring node height (OffetA), monitoring node visible Radius (Radius2), vertical detection starting angle (Vert1), vertical detection ending angle (Vert2) and the like, candidate node visible field under the data of a Digital Elevation Model (DEM) of a target area can be obtained through running software. DEM is a digital simulation of the ground terrain (i.e., a digital representation of the topography of the terrain surface) through limited terrain elevation data, and is a solid ground model that represents the ground elevation in the form of an ordered set of numerical arrays. The candidate monitoring nodes refer to all the monitoring nodes which can be deployed.
(2) Marking the coverage quality of the monitoring area of the candidate monitoring node: for a forest area a and V candidate deployment positions, selecting a monitoring node candidate position s E V for deployment, and if a forest fire in a grid within a radius range can occur, determining the probability pf,s(pf,s≧ L), the monitoring node s is considered to cover the grid with the monitoring quality L. By covering quality
To represent the grid cell L covered by the monitoring node s(i)As shown in formula (1):
if a grid is positioned in the visual field of the monitoring node, the grid is considered to be covered, and the covering quality of the grid is marked
Otherwise the quality of coverage
(3) Evaluating the coverage quality corresponding to the radius area of the node: marking the coverage quality of each grid
Then, the coverage quality evaluation formula F(s) corresponding to the radius area is shown as formula (2):
(4) evaluating the coverage quality added value of the radius area of the newly added node: when described for a set V of all n candidate locations, the set P of candidate locations is the m s of the set V0，s1，s2，...，smSubset, such that the union of P equals V. F(s) is used to evaluate the area covered by the monitoring node. If the monitoring node position Po in the original EDS (Early Detection System) is { s }0，s1，…,si-1And for the newly added monitoring node position si epsilon V, the added (expanded) position set Pe is { s ∈ V0，s1，…,si-1}∪{si}. For this time, there are systems for the location aggregation
It keeps F (Po) less than or equal to F (Pe).
It is non-decreasing for the newly added monitor node position si ∈ V, F (si), and
is zero, the coverage quality is calculated by the following evaluation equation (3):
its corresponding coverage quality increase value can be evaluated and calculated by the following incremental formula (4):
(5) proving that after the newly added node of the original monitoring system is expanded, the radius area coverage quality added value corresponding to the node in the system has the characteristics of a submodule, and the formula (4) can obtain the formula (5) of the coverage quality added value after the newly added node of the original system is expanded:
based on the set operation theorem, equations (6) and (7) can be obtained:
therefore, the above equations (6) and (7) can be expressed as equations (8) and (9):
the following expressions (10) and (11) are obtained by derivation according to expressions (8) and (9):
thus, expression (12) is obtained:
F(Po∪{i})-F(Po)≥F(Pe∪{i})-F(Pe) (12)
as can be seen from the expression (12), the original monitoring system has the sub-model characteristic after the newly added nodes are expanded.
Monitoring node location optimization is a combinatorial optimization problem and therefore can be viewed as searching for discrete optimal location combinations. In this embodiment, theoretical analysis proves that the evaluation function of the coverage area of the probe point conforms to the sub-model, and the problem of optimizing the position of the monitoring node can be regarded as the problem of maximizing the sub-model function under condition constraint, that is, under the condition of meeting conditions and requirements, the problem of maximizing the sub-module set function can be regarded as the problem of allocating the position of the monitoring node. The method for combining the visible field of the monitoring nodes and the position allocation based on the sub-model optimization solves the position deployment of the forest fire monitoring nodes.
S102, acquiring a monitoring node deployment position of a target forest fire monitoring system based on the characteristics of the submodels; the deployment positions of the monitoring nodes comprise redeployment positions of the existing monitoring nodes and deployment positions of newly-built monitoring nodes;
the target forest fire monitoring system comprises a forest fire monitoring system to be built and a built forest monitoring system, and the step S102 comprises two conditions:
screening out candidate positions of monitoring nodes according to the terrain position of a forest area aiming at a forest fire monitoring system to be built, and selecting a monitoring node deployment position of the forest fire monitoring system from the candidate positions of the monitoring nodes based on the characteristics of the submodels;
and evaluating the established monitoring nodes based on the characteristics of the submodels aiming at the established forest fire monitoring system, and redeploying or expanding the monitoring nodes according to the cost and the cost performance of the monitoring nodes to obtain the deployment positions of the monitoring nodes of the forest fire monitoring system.
Specifically, according to the characteristics of the submodels, the optimized monitoring node positions in all candidate monitoring nodes are obtained to serve as deployment positions, and the candidate monitoring nodes are screened out according to the forest terrain aiming at a forest fire monitoring system to be built; for the established forest fire monitoring system, evaluating the established monitoring nodes, and taking the monitoring nodes which are redeployed or expanded according to the node cost and the cost performance as candidate monitoring nodes, as shown in fig. 3, step S102 includes the following steps:
step S201, obtaining the cost of a candidate monitoring node position set of a forest fire monitoring system;
in this step, the cost of the candidate node position set P is evaluated: when a monitoring node position s e V has an average cost C(s), the position set thereof
The cost of (c) is calculated by the following equation (13):
C(p)＝∑s∈PC(s) (13)
step S202, calculating the monitoring node with the maximum coverage quality increase value which obeys the preset condition constraint according to the cost of the candidate monitoring node position set;
step S203, using the candidate position corresponding to the monitoring node satisfying the preset condition and having the largest coverage quality increase value as the deployment position of the monitoring node.
Further, step S202 includes: acquiring a first monitoring node with a maximum coverage quality increase value in all candidate monitoring nodes; continuously acquiring a second monitoring node with the maximum coverage quality increase value in the remaining candidate monitoring nodes except the first monitoring node; by analogy, a plurality of monitoring nodes with the maximum coverage quality increase value and calculated for multiple times are obtained; and selecting the monitoring node which is subject to the preset condition constraint from the plurality of monitoring nodes with the largest coverage quality increasing value according to the cost of the candidate monitoring node position set.
In this step, the monitoring node with the maximum coverage quality increase value subject to the preset condition constraint is calculated according to the following equation (14):
wherein p is*Increasing a set of value monitoring nodes for the maximum coverage quality; f (p) is a covering quality evaluation function;
representing that P is the node with the largest coverage quality increment value; c (p) is the cost of the candidate monitoring node position set; b represents a preset condition.
Specifically, the evaluation calculates coverage quality increase values for the remaining candidate locations (at least two candidate locations) of all possible deployment monitoring nodes: and according to an equation (14), when the coverage quality meets the condition constraint according to the evaluation result, acquiring the corresponding candidate position which meets the preset condition and has the maximum coverage quality increase value as the node deployment position.
Because the newly added node can generate a redundant coverage area, based on the mutual information standard, calculating the coverage quality increase value of each position in all the remaining candidate positions;
for one k-round operation, after each round of calculation, selecting a candidate position corresponding to the maximum value in the coverage quality increase value, and putting the candidate position corresponding to the maximum value into a Pe set so that the Pe set comprises k candidate positions; and the k value depends on the cost, budget and coverage strategy limits corresponding to different application target scenes.
Step S103, calculating the number of monitoring nodes with different strategy requirements according to the deployment positions of the monitoring nodes to obtain a monitoring node position set.
Further, step S103 includes: calculating the number of monitoring nodes of a full coverage strategy at the minimum cost according to the deployment positions of the monitoring nodes; or, calculating the number of monitoring nodes of the maximum coverage strategy under the cost limit according to the deployment positions of the monitoring nodes; or, calculating the number of the monitoring nodes for the fully compatible system extension strategy according to the deployment positions of the monitoring nodes; or calculating the number of the monitoring nodes of the part of compatible extension strategies of the cost performance constraint according to the deployment positions of the monitoring nodes.
Specifically, the calculation method for selecting the k value in different strategies comprises the following steps:
(1) for a full coverage strategy at minimum cost: in order to increase the coverage area of the monitoring nodes to the maximum extent and reduce the loss caused by fire. The optimization process starts with the empty set, iteratively and incrementally finds the location s with the highest sensing quality F ({ Po } { s }) and adds it to the current set Po. Let Δ for each candidate position s that has not yet been evaluateds＝F(Po∪{s})-F(Po) Is an additional value of s. When the entire area is found to be covered, it has the least number of monitoring nodes, at which point the algorithm stops. k should be ΔsK value at thr ≦ (thr is the lowest threshold of increase value).
(2) For maximum coverage policy under cost constraints: in practical application, the EDS system needs relatively more monitoring nodes due to the large area of the forest monitoring area. Project implementation is often limited by budget, and can only be deployed maximally, but cannot achieve complete coverage of forest areas. The strategy calculates the optimal deployment location of a new monitoring node that satisfies a range of objectives and specific constraints. Assume that for a newly deployed monitoring node location s ∈ V, there is an average cost C. Location collection
The cost of (C) is C | Po |. Therefore, when a strategy of maximizing the forest fire monitoring area under the limit of the cost B is obeyed, k is deltasK value is less than or equal to thr. Alternatively, k is an integer value obtained when the project budget B is divided by the average cost of a single node B, i.e., k ≦ ((int) (B/C)).
Thus, the algorithm for maximizing the forest fire monitoring area subject to the cost B limit is as follows:
and 2, algorithm: maximum coverage under cost constraints
(3) For a fully compatible system extension policy: as the economic development and the area of the monitoring area are continuously expanded, new monitoring nodes need to be added to expand the coverage area of the early system. An algorithm is needed to adaptively adjust the coverage of each monitoring node to meet a given monitoring coverage target and meet a given budget constraint. In addition, in order to avoid interruption of forest fire monitoring, a system needs to be comprehensively expanded on the basis of recycling the existing monitoring nodes, and meanwhile, the newly added nodes are guaranteed to have the highest added value. This strategy is specific to extended algorithms that are fully compatible with the original early monitoring system. Based on the above scenario analysis, it is assumed that Po and Pe are a group of monitoring nodes in the original EDS and a group of monitoring nodes in the post-expansion network, respectively. For each newly added monitoring node s, if Pe ═ Po utou { s }, this means that all original monitoring nodes are completely retained. Therefore, there is no cost to reuse the monitoring nodes within the existing system. To meet the budget constraints that exist in practice, we extend the original system to reach the maximum coverage area subject to the budget constraints. K is then the integer value obtained when the project budget B is divided by the average cost of a single node B, i.e., k ≦ int (B/C).
(4) Part compatible extension strategy for cost performance constraint, some existing EDS systems are established empirically in the early stage of technology development, and some monitoring nodes in part regions are usually deployed by different companies without systematic consideration due to huge forest monitoring areaThe deployment location is taken into account. In addition, due to the limited budget, we need to seek an extension scheme based on the existing system as much as possible. This allows some existing monitoring nodes to be retained in the expanded system while others may be relocated to new locations, thereby optimizing overall coverage. Such a reserved reuse of monitoring nodes does not have any new construction costs, whereas relocation of existing monitoring nodes would lead to reconstruction costs. Suppose Po, Pe are a set of monitoring nodes in the original EDS and a set of monitoring nodes in the post-expansion network, respectively. Let
Is a set of reuse node locations moved to other locations in the original EDS. Let Cr be the average cost of relocating the monitoring node of the present location to a different location in the extended system Pe. The cost C of each newly added node includes an installation cost Cd and an equipment cost.
From the Cr and C (redeployed and newly added node) constraints and the total budget constraint, it is desirable to satisfy the following expression (15):
Cr|Pr|+C(|Pe|-|Po|)≤B (15)
in the cost constitution of the actual setup, since Cd of the new node is about 1/4 of C, which is about half of Cr, the expression is (16):
1*|Pr|+2*(|Pe|-|Po|)≤2B/C (16)
without loss of generality, the following formula (17) can be expressed by two parameters a1 and a 2:
a1|Pr|+a2(|Pe|-|Po|)≤2B/C (17)
the values of a1 and a2 can be obtained according to the cost composition at the time of actual deployment, and according to the cost composition ratio of the scheme, we can obtain a 1-2 and a 2-1. This means that at most two existing nodes are redeployed from the old system, or one node is added to the expanded system with the same cost in terms of cost. At the same time, both operations should increase the coverage quality by a value ΔsAnd (4) maximizing. The best candidate location selection problem is therefore to determine a location selection subject to the following budget constraints and to maximize the overall coverage area. Then the following two constraint values need to be quantized first。
(a) Calculating the maximum number of locations n1 that can be redeployed under the budget constraint is according to equation (18):
n1＝|Po-Pe|≤2B/a1C (18)
(b) calculating (assuming that all the positions Po of the original system are the optimal condition of all the optimal position sets) the maximum newly-added number of monitoring nodes n2 according to the following equation (19):
n2＝|Pe|-|Po|＝B/a2 C (19)
at this time, in order to find k optimal positions, for each newly added monitoring node position s satisfying argmaxs F (Po { S }), the search solution needs to enumerate all the monitoring node positions s
The deployment positions of the monitoring nodes, wherein C is a binomial coefficient, however, for redeployed monitoring nodes and newly added monitoring nodes with upper limits of the two numbers n1 and n2, it can be proved that the possible position number needing to be searched is calculated according to the expression (20):
and (3) proving that: the expanded monitoring node position set Pe can be obtained in two steps: firstly, i probe point positions are selected from the position set Po of the original system, and probe nodes on the positions are redeployed to different positions of Pe after the system is expanded. Therefore, in this step we have
A possible selection. For each of these options, it would need to further deploy i + n2 monitoring nodes on V-Po candidate addresses, which would have
And (4) selecting. Therefore, when i is changed from 0 to n1, we get the above expression.
At this time, the value of k should satisfy k ≦ n1+ n2,or k is ΔsK value is less than or equal to thr. The corresponding calculation steps of the specific k value are as follows:
(a) n1 positions are selected from the position set Po of the original system and are put into the candidate position set.
(b) For each subset Pr of Po after rejecting the n1 locations, a (n1+ n2) round of calculations is performed. After each round of calculation, selecting the candidate position corresponding to the maximum value in the coverage quality increase value and putting the candidate position corresponding to the maximum value into the expanded node pe (i)i≤n1And aggregating to enable the Pe aggregate to comprise k candidate positions.
(c) For all
And (c) repeating the operation process of the step (b) by the combination.
(d) Selecting
P ise (i)i≤n1As a set of candidate locations.
The partial compatibility expansion algorithm under the cost performance constraint can be as follows:
algorithm 4 partial compatibility extension algorithm
When the steps are executed, the Po initialization method under different strategies is as follows:
(1) for a full coverage strategy at minimum cost: po starts from the empty set, so
(2) For maximum coverage policy under cost constraints: po starts from the empty set, so
(3) For a fully compatible system extension policy: po is set to its initialization value by the current deployed node location.
(4) Partially compatible extension strategy for cost performance constraint: po is set to its initialization value by the current deployed node location.
With the economic and technical development, forest management investments have increased dramatically and some early monitoring systems (EDS) will gradually replace or expand, always with the desire to maximize the performance of the original system at minimal cost. Meanwhile, when the original system is expanded, the adverse effect of forest fire detection is minimized. The embodiment proposes a flexible method that combines location view and location allocation based on the concept of submodels to solve the key problem of forest fire monitoring. First, the present embodiment provides the lowest cost full coverage scheme and the maximum coverage scheme under the budget constraint scheme to meet the basic requirements of the practice. Secondly, to maintain the continuity of monitoring, the present embodiment proposes a system extension scheme fully compatible with the original EDS. Furthermore, in view of the trade-off between coverage gain and cost, a scheme is proposed to adaptively extend the system based on the local original system to meet the scheme of maximizing the monitoring coverage goal while meeting the given budget constraint.
The method for deploying forest fire monitoring nodes based on location optimization is described in detail by using specific examples.
Taking a forest park with a deployment area in a certain area of China as an example, the geographic coordinates are 118.30 degrees N, 30.40 degrees E, the total area is 80 square kilometers, and the forest coverage rate is as high as 80%. The area of the study area, 10.56 square kilometers, is a part of a forest covered park. As shown in fig. 4, 34 candidate monitoring node locations for the deployment region are displayed. The spatial resolution of the digital elevation model used in this example is 30 meters. The experiments were performed on an Intel core I72.8 GHz computer with 32GB memory and 64 bits Windows 8.1. The average cost of each monitoring node is 2 ten thousand yuan, and the observation radius is 1.0 kilometer. The horizontal observation angle of the digital video equipment is 360 degrees, and the vertical observation angle is-90 degrees to +10 degrees.
Fig. 5 shows a full coverage schematic at minimum cost. The full coverage results at minimum cost are shown in table 1.
TABLE 1
The best position solution, when completed covering the entire target area at the lowest total construction cost, requires a total cost of 32 ten thousand dollars. There was 14.9% of the area covered only once, 38.1% of the area covered twice, and 33.3% of the area covered three times, as shown in fig. 5. As shown in table 1, at most 84.7% of the area is covered by multiple monitoring nodes. In these areas, the monitoring node location is the uncovered area location that can cover the most among the candidate locations, and some area overlap with neighboring monitoring nodes may occur in order to achieve the goal of full coverage.
As described above, adding new monitoring nodes in low density areas improves overall coverage more than adding them to high density area deployments. And the obtained increase value is gradually reduced as the number of the nodes is increased. We have demonstrated that diminishing returns can be modeled by sub-model concepts. With the increasing number of the monitoring nodes, the area of the uncovered area covered by each newly added node is smaller and smaller. That is, there is an optimum between coverage and cost, and a trade-off between coverage area and cost can be made to obtain a better benefit to cost ratio.
FIG. 6 shows a diagram of maximum coverage under different budget constraints, with cost constraints of 8000, 14000, 20000 and 26000 respectively. Careful inspection of the results showed that the profit-to-cost ratio for each new monitoring node was not constant. Initially, the newly added node will rapidly increase the coverage. When the number of nodes reaches a certain value, the newly added nodes only slightly increase the overall coverage rate. This suggests that high coverage can be achieved by increasing the number of monitoring nodes, but at the expense of higher and higher unit costs. For example, coverage increased rapidly from 0 to 79.2%, while cost increased from 0 to 140000. Thereafter, increasing the cost from 140000 to 260000 affected the effect that the coverage of the monitored area increased only 16.8%, but at the cost of a new 30.7% increase.
It can also be observed from the results that the proportion of the primary coverage area increases rapidly to 65.6% from the beginning (corresponding to a coverage of 79.2%). The primary coverage then decreases to 24.2% with increasing number of monitoring nodes, corresponding to a reduction of 41.4%, table 2 being the maximum coverage under budget constraints. The reason for this change is that the overlap area increases when the number of monitor nodes exceeds a certain point. This makes it possible to conclude that: the cost of monitoring nodes can be substantially reduced by reducing the amount of coverage.
TABLE 2
Fig. 7 shows a schematic diagram of an original system coverage result, and fig. 8 shows a schematic diagram of a system coverage result after full-compatible extension. In the original early monitoring system, six monitoring nodes are located at high positions and are mainly distributed on the left side of a target area, and the coverage rate of the monitoring nodes is 51.5%. Now there is budget to implement the extension of the original system in stages, and in case of 60000 yuan budget, 3 new monitoring nodes can be added. In order not to influence the existing system to monitor forest fires, the emphasis of the extension strategy is to be completely compatible with the existing system and to improve the incremental coverage rate of new monitoring nodes to the maximum extent on the basis of the original coverage rate. As shown in table 3, the coverage of the extended system increased by 29.6% to 81.1%. In general, equipment cost is a major portion of the cost of deployment per probe point, and existing nodes are moved at a lower cost than newly added nodes. The increase in coverage rate obtained by adding a new monitoring node has a decreasing characteristic. It is possible to obtain higher coverage by moving existing towers to different locations.
TABLE 3
A partially compatible extension that takes into account cost-performance constraints. As analyzed above, in order to increase the coverage area, the expansion of the system needs to be systematically considered, which needs to consider the relocation cost, the influence of relocation on the coverage area, the coverage increment caused by adding new nodes and new nodes, and the budget limit. Therefore, an important component of the extension is to determine the number and location of existing monitoring nodes that need to be relocated. The goal of relocation implementation is to minimize the overlap area of the original system while trading off relocation cost against newly added monitoring node cost. And calculating the maximum redeployable monitoring node number under the budget constraint and the maximum newly-added monitoring node number under the budget constraint according to the expressions (18) and (19). Therefore, in the case of the budget 60000 dollars, 3 new monitoring nodes can be added or the existing 6 nodes can be redeployed, and in this interval, the primary goal is to maximize monitoring coverage.
The original system is the same as the fully compatible extended system. The results show that two new monitoring nodes are added to the extended system at locations 2 and 14. The two monitoring nodes of the original system are relocated from locations 31 and 13 to locations 30 and 26 in the extended system, the result of which is shown in fig. 9. As shown in table 3, the coverage increased from the initial value of 51.5% for the extended system to 84.3%. This extension increases the coverage by about 3.8 percentage points over a fully compatible extension at the same cost.
As shown in fig. 10, the embodiment further provides a forest fire monitoring incremental node expansion system based on location optimization, and the system includes a verification module 10, a location acquisition module 20, and a calculation module 30;
the verification module 10 is used for verifying that the forest fire monitoring system has the sub-module characteristic after the monitoring nodes are newly added;
the position acquisition module 20 is used for acquiring the deployment position of the monitoring node of the target forest fire monitoring system based on the characteristics of the submodels;
and the calculating module 30 is configured to calculate the number of monitoring nodes with different policy requirements according to the deployment positions of the monitoring nodes, so as to obtain a monitoring node position set.
Further, the target forest fire monitoring system comprises a forest fire monitoring system to be built and a built forest monitoring system, and the position acquisition module 20 comprises a first position acquisition unit and a second position acquisition unit;
the first position acquisition unit is used for screening out candidate positions of monitoring nodes according to the terrain position of a forest area aiming at a forest fire monitoring system to be built, and selecting a monitoring node deployment position of the forest fire monitoring system from the candidate positions of the monitoring nodes based on the characteristics of the submodels;
and the second position acquisition unit is used for redeploying or expanding the monitoring nodes on the basis of the established monitoring nodes based on the characteristics of the submodels aiming at the established forest fire monitoring system to obtain the deployment positions of the monitoring nodes of the forest fire monitoring system.
Further, the verification module 10 comprises an evaluation unit;
the evaluation unit is used for evaluating the coverage quality increase value of the radius area of the newly added monitoring node; and determining that the original monitoring system has the sub-mode characteristic after the new monitoring node is added according to the coverage quality increase value.
Further, the evaluation unit comprises a marking subunit, an obtaining subunit and an evaluation subunit;
the marking subunit is used for acquiring the visual field of the candidate monitoring node of the forest fire monitoring system and marking the monitoring area coverage quality of the candidate monitoring node;
the acquisition subunit is used for acquiring the coverage quality corresponding to the radius area of the candidate monitoring node according to the coverage quality of the monitoring area;
and the evaluation subunit is used for evaluating the coverage quality increase value of the radius area of the newly added monitoring node according to the coverage quality corresponding to the radius area of the candidate monitoring node.
The forest fire monitoring node deployment system based on the position optimization provided by the embodiment of the invention has the same technical characteristics as the forest fire monitoring node deployment method based on the position optimization provided by the embodiment, so that the same technical problems can be solved, and the same technical effect can be achieved.
In the description of the present invention, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The computer program product for implementing the forest fire monitoring node deployment method based on location optimization according to the embodiment of the present invention includes a computer-readable storage medium storing a non-volatile program code executable by a processor, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, and will not be described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and 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 units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A forest fire monitoring incremental node expansion method based on position optimization is characterized by comprising the following steps:
verifying that the forest fire monitoring system has the characteristic of a submodule after the new monitoring node is added;
acquiring a monitoring node deployment position of a target forest fire monitoring system based on the characteristics of the submodels; the deployment positions of the monitoring nodes comprise a redeployment position of an existing monitoring node and a newly-built monitoring node deployment position;
calculating the number of monitoring nodes with different strategy requirements according to the deployment positions of the monitoring nodes to obtain a monitoring node position set;
the step of calculating the number of the monitoring nodes with different strategy requirements according to the deployment positions of the monitoring nodes comprises the following steps:
calculating the number of monitoring nodes of a full coverage strategy at the minimum cost according to the deployment positions of the monitoring nodes; or, calculating the number of monitoring nodes of the maximum coverage strategy under the cost limit according to the deployment positions of the monitoring nodes; or, calculating the number of the monitoring nodes for the fully compatible system extension strategy according to the deployment positions of the monitoring nodes; or, calculating the number of monitoring nodes of a part of compatible extension strategies of cost performance constraints according to the deployment positions of the monitoring nodes;
the step of verifying that the forest fire monitoring system has the characteristics of the submodel after the new monitoring nodes are added comprises the following steps:
evaluating the coverage quality increase value of the radius area of the newly added monitoring node;
and determining that the forest fire monitoring system has the sub-model characteristic after the monitoring nodes are newly added according to the coverage quality added value.
2. The method as claimed in claim 1, wherein the target forest fire monitoring system comprises a forest fire monitoring system to be built and a forest monitoring system already built, and the step of obtaining deployment positions of monitoring nodes of the forest fire monitoring system based on the sub-model characteristics comprises:
screening out candidate positions of monitoring nodes according to the terrain position of a forest area aiming at a forest fire monitoring system to be built, and selecting a monitoring node deployment position of the forest fire monitoring system from the candidate positions of the monitoring nodes based on the characteristics of the submodels;
and evaluating the established monitoring nodes based on the characteristics of the submodels aiming at the established forest fire monitoring system, and redeploying or expanding the monitoring nodes according to the cost and the cost performance of the monitoring nodes to obtain the deployment positions of the monitoring nodes of the forest fire monitoring system.
3. The method of claim 1, wherein the step of evaluating the coverage quality increase value of the radius area of the newly added monitoring node comprises:
acquiring a visual field of a candidate monitoring node of the forest fire monitoring system, and marking the monitoring area coverage quality of the candidate monitoring node;
acquiring coverage quality corresponding to the radius area of the candidate monitoring node according to the coverage quality of the monitoring area;
and evaluating the coverage quality increase value of the radius area of the newly added monitoring node according to the coverage quality corresponding to the radius area of the candidate monitoring node.
4. The method of claim 2, wherein the step of obtaining a deployment location of a monitoring node of a target forest fire monitoring system based on the sub-model characteristics comprises:
acquiring the cost of a candidate monitoring node position set of the target forest fire monitoring system;
calculating the monitoring node with the maximum coverage quality increase value which obeys the preset condition constraint according to the cost of the candidate monitoring node position set;
and taking the candidate position corresponding to the monitoring node which meets the preset condition and has the maximum coverage quality increment value as the deployment position of the monitoring node.
5. The method according to claim 4, wherein the candidate monitoring nodes comprise a plurality of nodes, and the step of calculating the monitoring node with the largest coverage quality increase value subject to a preset condition constraint according to the cost of the candidate monitoring node position set comprises:
acquiring a first monitoring node with a maximum coverage quality increase value in all the candidate monitoring nodes;
continuously acquiring a second monitoring node with the largest coverage quality increase value in the remaining candidate monitoring nodes except the first monitoring node;
by analogy, a plurality of monitoring nodes with the maximum coverage quality increase value and calculated for multiple times are obtained;
and selecting a monitoring node which is subject to preset condition constraint from a plurality of monitoring nodes with the maximum coverage quality increasing value according to the cost of the candidate monitoring node position set.
6. The method according to claim 4, wherein the step of calculating the monitoring node with the largest coverage quality increase value subject to preset condition constraints according to the cost of the candidate monitoring node location set comprises:
calculating the monitoring node with the maximum coverage quality increase value subject to the preset condition constraint according to the following formula:
wherein, the p is*Increasing a set of value monitoring nodes for the maximum coverage quality; f (p) is a coverage quality assessment function; the above-mentioned
Representing that P is the node with the largest coverage quality increment value; the C (p) is a cost of the set of candidate monitoring node locations; the B represents the preset condition.
7. The method according to claim 2, wherein the step of calculating the number of monitoring nodes with different policy requirements according to the deployment location of the monitoring nodes comprises:
aiming at a forest fire monitoring system to be built, calculating the number of monitoring nodes of a full coverage strategy at minimum cost according to the deployment positions of the monitoring nodes;
or, calculating the number of monitoring nodes of the maximum coverage strategy under the limit of cost according to the deployment position of the monitoring nodes;
aiming at the established forest fire monitoring system, calculating the number of monitoring nodes for the fully compatible system expansion strategy according to the deployment positions of the monitoring nodes;
or calculating the number of monitoring nodes of part of compatible extension strategies of cost performance constraints according to the deployment positions of the monitoring nodes.
8. A forest fire monitoring incremental node expansion system based on position optimization is characterized by comprising:
the verification module is used for verifying that the forest fire monitoring system has the sub-module characteristic after the monitoring nodes are newly added;
the position acquisition module is used for acquiring the deployment position of a monitoring node of the target forest fire monitoring system based on the characteristics of the submodels;
the calculation module is used for calculating the number of monitoring nodes with different strategy requirements according to the deployment positions of the monitoring nodes to obtain a monitoring node position set;
the calculation module is further to: calculating the number of monitoring nodes of a full coverage strategy at the minimum cost according to the deployment positions of the monitoring nodes; or, calculating the number of monitoring nodes of the maximum coverage strategy under the cost limit according to the deployment positions of the monitoring nodes; or, calculating the number of the monitoring nodes for the fully compatible system extension strategy according to the deployment positions of the monitoring nodes; or, calculating the number of monitoring nodes of a part of compatible extension strategies of cost performance constraints according to the deployment positions of the monitoring nodes;
the authentication module includes:
the evaluation unit is used for evaluating the coverage quality increase value of the radius area of the newly added monitoring node; and determining that the forest fire monitoring system has the sub-model characteristic after the monitoring nodes are newly added according to the coverage quality added value.
9. A system as claimed in claim 8, wherein the target forest fire monitoring system comprises a forest fire monitoring system to be built and a forest monitoring system already built, and the location acquisition module comprises:
the first position acquisition unit is used for screening out candidate positions of monitoring nodes according to the terrain position of a forest area aiming at a forest fire monitoring system to be built, and selecting a monitoring node deployment position of the forest fire monitoring system from the candidate positions of the monitoring nodes based on the characteristics of the submodels;
and the second position acquisition unit is used for redeploying or expanding the monitoring nodes on the basis of the established monitoring nodes based on the characteristics of the submodels aiming at the established forest fire monitoring system to obtain the deployment positions of the monitoring nodes of the forest fire monitoring system.
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