Multi-platform energy optimization method based on battle differentiation weighted clustering

Abstract

The invention discloses a multi-platform energy optimization method based on combat differentiation weighted clustering, and solves the problems of large communication energy loss and non-uniform distribution in sea-land-air multi-platform cooperative combat. The method comprises the following steps: acquiring environmental parameters and platform parameters of various platforms; constructing a differential clustering index; establishing a calculation formula of a clustering weight based on the differentiated clustering index; the node determines a calculation formula of a clustering weight according to platform characteristics and combat mission requirements, adaptively adjusts the size of a weight factor corresponding to each differentiated clustering index, and calculates the clustering weight of the node; selecting the node with the maximum clustering weight as a cluster head and clustering until all the nodes are distributed; and when the residual energy of the cluster head is lower than the updated threshold value of the cluster head, recalculating the clustering weight of the node, selecting a new cluster head and clustering. According to the invention, the energy loss during communication between the platforms and the operation process of the whole combat network is reduced, and the whole combat effectiveness is improved.

Description

Technical Field

[0001] The present invention relates to the field of multi-platform differentiated combat, and in particular to an energy optimization method for sea, land and air multi-platforms in the process of performing different combat missions. Background Art

[0002] In today's highly complex and ever-changing modern sea, land and air multi-platform collaborative combat scenarios, ships, aircraft, tanks and various diversified combat platforms are involved. When each platform performs its own established diversified tasks, in order to ensure the efficiency and accuracy of the action, it must carry out continuous and frequent communication contacts to achieve instant sharing of key information and seamless collaborative operations. However, in the actual combat environment, due to the complexity and changeability of natural conditions, such as the influence of natural factors such as turbulent currents and unpredictable wind speeds, different combat platforms have different communication performance and combat capabilities, and the specific nature of the tasks they undertake, the priority of the target and the route of action are different, so that the combat network has a large communication energy loss problem during the communication process.

[0003] Some existing energy optimization methods often fail to fully consider the various characteristics of the platform and the comprehensive impact of differentiated operations on communication and energy consumption, making it difficult to achieve efficient energy optimization effects. Therefore, it is necessary to find a new multi-platform energy optimization method to improve the efficiency and quality of communications, reduce the energy loss of differentiated operations of various sea, land and air platforms, and provide more reliable and efficient communication guarantees for modern sea, land and air multi-platform coordinated operations. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-platform energy optimization method based on combat differentiation weighted clustering, which is used to solve the problem of large communication energy loss and uneven distribution caused by combat missions, complex environments and performance differences of various platforms in sea, land and air multi-platform coordinated operations. By comprehensively considering the various characteristics of the platform and combat mission factors, reasonable cluster head selection, clustering and cluster head update are carried out, the energy consumption within the cluster is balanced, the number of cluster head replacements is reduced, thereby reducing the energy loss during inter-platform communication and the operation of the overall combat network, and improving the overall combat effectiveness.

[0005] In order to achieve the above object, the technical solution of the present invention is achieved as follows:

[0006] A multi-platform energy optimization method based on combat differentiation weighted clustering includes the following steps:

[0007] Step 1: Obtain environmental parameters and platform parameters of various platforms, wherein the platform parameters include the number, speed, remaining energy, data reception quality, and initial position of each platform;

[0008] Step 2: Select the residual energy, data reception quality in the platform parameters, and the relative average distance and relative average speed calculated based on the environmental parameters and other platform parameters to form the basic clustering index for cluster head selection;

[0009] Step 3: at least one of the communication range, concealment or reconnaissance coverage and the basic clustering index constitute a differentiated clustering index for cluster head selection, and a calculation formula for clustering weights is established based on the differentiated clustering index;

[0010] Step 4: The node determines the calculation formula of clustering weight according to the platform characteristics and combat mission requirements, and adaptively adjusts the weight factor corresponding to each differentiated clustering indicator in the calculation formula of clustering weight to calculate the clustering weight of the node;

[0011] Step 5: Select the node with the largest clustering weight as the cluster head, and the cluster head node and the nodes of the cluster head node's neighbors that are not assigned cluster heads form a cluster;

[0012] Step 6: Repeat steps 4 and 5 to select cluster heads until all nodes are allocated, achieving a balance between energy utilization and communication efficiency;

[0013] When the remaining energy of the cluster head is lower than the cluster head update threshold, the cluster head update mechanism is triggered: the clustering weights of the nodes are recalculated according to steps 4 to 6, and a new cluster head is selected and clustered.

[0014] Preferably, the method for establishing a calculation formula for clustering weights based on differentiated clustering indicators is:

[0015] When the differentiated clustering index is the basic clustering index and the communication range, the calculation formula of the clustering weight W is:

[0016]

[0017] When the differentiated clustering index is the basic clustering index and concealment, the calculation formula of the clustering weight W is:

[0018]

[0019] When the differentiated clustering index is the basic clustering index and the reconnaissance coverage, the clustering weight W is calculated as:

[0020]

[0021] Among them, the subscript i represents the node i, The relative average distance The weight factor of Relative average speed The weight factor of PRQ is the data reception quality iThe weight factor The remaining energy E i The weight factor of The communication range R i The weight factor of For reconnaissance coverage C i The weight factor of For concealment i The weight factor of The relative average distance The maximum value of Relative average speed The maximum value, PRQ max PRQ is the data reception quality i The maximum value, E max The remaining energy E i The maximum value, R max The communication range R i The maximum value, R i is the communication range, C i For reconnaissance coverage, M i For concealment, α is or

[0022] Preferably, the data reception quality S i is the number of successfully received data packets, T i The number of packets sent.

[0023] Preferably, the remaining energy E i Obtained from the platform's own energy monitoring module.

[0024] Preferably, the relative average distance Among them, D in is the relative distance between the node and its neighbor nodes, N is the number of neighbor nodes, and n represents neighbor node n.

[0025] Preferably, the relative average speed Among them, V(n) is the set of neighbor node velocities.

[0026] Preferably, the reconnaissance coverage area C i The influencing parameters include the sensor range and viewing angle of the node; the communication range R i The effective communication distance is obtained by determining the performance parameters of the communication equipment of the node; the concealment M i The influencing parameters include the node's noise level, visual concealment and electronic signal concealment.

[0027] Preferably, the method for adaptively adjusting the weight factor corresponding to each differentiated clustering index in the calculation formula of the clustering weight is: when the increase in communication energy loss is due to the relative average distance The increase leads to the relative average distance The weight factor Increase Δw D ; When the increase in communication energy loss is due to the relative average speed The relative average speed The weight factor Increase Δw V ; When the increase in communication energy loss is due to the data reception quality PRQ i If the data reception quality PRQ i The weight factor Increase Δw PRQ ; When the overall energy supply is insufficient in the late stage of the battle, the remaining energy E i The weight factor Increase Δw E ; The adjustment of the weight factors corresponding to the communication range, concealment, and reconnaissance coverage is determined by the mission characteristics.

[0028] Preferably, the weight factors corresponding to the communication range, concealment, and reconnaissance coverage are adjusted as follows: when the task performed by the platform depends on concealment, the concealment M i The weight factor Increase Δw M When the tasks performed by the platform depend on the communication range, the communication range R i The weight factor Increase Δw R ; When the mission performed by the platform depends on the reconnaissance coverage, the reconnaissance coverage C i The weight factor Increase Δw c .

[0029] Preferably, the threshold for cluster head update is Among them, E0 is the energy when the cluster head node becomes the cluster head, and k is the proportional coefficient.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention fully considers the comprehensive impact of various characteristics of the platform and differentiated operations on communication and energy consumption, and designs a clustering method including basic clustering indicators and differentiated clustering indicators of communication range, concealment or reconnaissance coverage, so as to perform reasonable cluster head selection and clustering, reduce energy consumption in inter-platform communication and overall operation, and improve overall combat effectiveness. The present invention reduces the number of cluster head replacements by triggering the cluster head update mechanism only when the remaining energy of the cluster head is lower than the cluster head update threshold, thereby reducing unnecessary energy consumption in communication and system operation and extending the life of the combat network. The present invention provides more reliable and efficient communication guarantee for modern sea, land and air multi-platform collaborative operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 It is a flow chart of the present invention.

[0034] Figure 2 This is a scene diagram of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] A multi-platform energy optimization method based on combat differentiation weighted clustering includes the following steps:

[0037] Step 1: Obtain environmental parameters and platform parameters of various platforms, including the number of platforms K, speed V i 、Residual energy E i , Data reception quality PRQ i , the initial position of each platform.

[0038] The data reception quality PRQ i By calculating the number of successfully received data packets S i The number of packets sent is T i The subscript i represents the node i:

[0039]

[0040] The remaining energy E i Obtained from the platform's own energy monitoring module.

[0041] Step 2: Select the residual energy, data reception quality, and relative average distance and relative average speed calculated based on the environmental parameters and other platform parameters from the platform parameters to form the basic clustering index.

[0042] The relative average distance The calculation formula is:

[0043]

[0044] Among them, D in is the relative distance between the node and its neighbor nodes, N is the number of neighbor nodes, and n represents neighbor node n.

[0045] The relative distance D between the node and its neighboring nodes in The Euclidean distance formula is:

[0046]

[0047] x i ,y i 、z i are the values ​​of the horizontal, vertical and vertical coordinates of the node in the three-dimensional rectangular coordinate system. n ,y n 、z n They are respectively the values ​​of the horizontal coordinate, vertical coordinate, and vertical coordinate of the neighboring node in the three-dimensional rectangular coordinate system.

[0048] The relative average speed The calculation formula is:

[0049]

[0050] Among them, V(n) is the set of neighbor node velocities.

[0051] Specifically, for each node, it is necessary to obtain the speed information of its neighboring nodes, and then calculate the absolute value of the speed difference between itself and each neighboring node. After summing these absolute values, they are divided by the number of neighboring nodes. The result is the average speed change of the node relative to its surrounding neighboring nodes, that is, the relative average speed.

[0052] Step 3: Select the residual energy, data reception quality in the platform parameters, and the relative average distance and relative average speed calculated based on the environmental parameters and other platform parameters to form the basic clustering indicators for cluster head selection.

[0053] The reconnaissance coverage area C i The influencing parameters include the sensor range and viewing angle of the node.

[0054] The communication range R i It is obtained by determining the effective communication distance based on the performance parameters of the node's communication equipment.

[0055] The concealment M i The influencing parameters include the node's noise level, visual concealment and electronic signal concealment.

[0056] The reconnaissance coverage is evaluated by the relevant sensors, and the distance of the farthest communicable node in the communication range is obtained by the communication quality.

[0057] For example, the stealth of the UAV system is determined based on noise level, visual stealth, and electronic signal stealth, with a quantitative scoring standard set for each influencing parameter:

[0058] Noise level: Set a scoring standard based on the decibel value of the noise level. For example, the noise level between 0-20dB is 5 points, between 20-40dB is 3 points, and above 40dB is 1 point.

[0059] Visual concealment: A scoring standard is set based on the camouflage effect of color, shape and other characteristics. For example, complete integration into the environment is 5 points, partial camouflage is 3 points, and no camouflage is 1 point.

[0060] Electronic signal concealment: A scoring standard is set based on the signal strength and the use of covert communication technology. For example, the use of advanced covert communication technology is 5 points, general concealment is 3 points, and no concealment is 1 point.

[0061] When the differentiated clustering index is the basic clustering index and the communication range, the calculation formula of the clustering weight W is:

[0062]

[0063] When the differentiated clustering index is the basic clustering index and concealment, the calculation formula of the clustering weight W is:

[0064]

[0065] When the differentiated clustering index is the basic clustering index and the reconnaissance coverage, the clustering weight W is calculated as:

[0066]

[0067] in, The relative average distance The weight factor of Relative average speed The weight factor of PRQ is the data reception quality i The weight factor The remaining energy E i The weight factor of The communication range R i The weight factor of For reconnaissance coverage C i The weight factor of For concealment i The weight factor of The relative average distance The maximum value of Relative average speed The maximum value, PRQ max PRQ is the data reception quality i The maximum value, E max The remaining energy E i The maximum value, R max The communication range R i The maximum value, R i is the communication range, C i For reconnaissance coverage, M i For concealment; α is or

[0068] In this embodiment, the calculation formulas for the clustering weights W of platforms performing different tasks at sea, on land, and in the air are respectively:

[0069] The calculation formula of the clustering weight W of the offshore platform (a) performing antisubmarine mission is:

[0070]

[0071] The calculation formula of the clustering weight W of the offshore platform (e) performing the escort mission (Maritime escort) is:

[0072]

[0073] The calculation formula of the clustering weight W of the offshore platform (p) performing patrol mission (Sea patrol) is:

[0074]

[0075] The calculation formula of the clustering weight W of the land platform (l) performing long-range strike mission is:

[0076]

[0077] The calculation formula of the cluster weight W of the special force (s) is:

[0078]

[0079] The calculation formula of the clustering weight W of the land platform (r) performing reconnaissance mission (army reconnaissance) is:

[0080]

[0081] The calculation formula of the clustering weight W of the air platform (b) performing the bombing mission (air force bombing) is:

[0082]

[0083] The calculation formula of the clustering weight W of the aerial platform (ar) performing reconnaissance mission (air force reconnaissance) is:

[0084]

[0085] The calculation formula of the clustering weight W of the air force transportation platform (t) is:

[0086]

[0087] Step 4: The node determines the calculation formula of clustering weight according to the platform characteristics and combat mission requirements, and adaptively adjusts the weight factor corresponding to each differentiated clustering indicator in the calculation formula of clustering weight to calculate the clustering weight of the node.

[0088] Since the influence of each differentiated clustering indicator on energy optimization varies in different combat stages and environments, it is necessary to adaptively adjust the weight factor corresponding to each differentiated clustering indicator according to the actual combat situation.

[0089] When strong currents or strong winds are detected in a certain area of ​​the combat zone, causing unstable communication signal transmission, and analysis shows that the relative average distance For every unit increase, the communication energy loss will increase significantly. The weight factor Increase Δw D In this way, in the subsequent clustering operation, nodes with a relatively short average distance will be more likely to be selected and divided into the same cluster, thereby reducing the risk of high energy loss caused by long-distance communication in harsh environments.

[0090] If it is found that the communication link is frequently interrupted due to the rapid movement of the platform (such as a high-speed flying aircraft or a high-speed sailing ship), it is determined through analysis that the relative average speed The communication instability caused by the rapid change increases the consumption of communication energy (for example, frequent retransmission of data to ensure the integrity of communication). The weight factor Increase Δw V , so that in clustering and subsequent operations, more attention can be paid to selecting nodes with relatively stable average speeds, reducing the waste of communication energy caused by relative speed factors.

[0091] If during the operation, it is found that the data reception quality of some nodes in a certain area is significantly reduced due to electromagnetic interference or other reasons, resulting in a large amount of data needing to be retransmitted, which greatly increases the communication energy consumption. At this time, the data reception quality PRQ i The weight factor w PRQi Increase Δw PRQ , so that when clustering and subsequently selecting nodes for communication task allocation, nodes with high data reception quality are more likely to be selected to reduce the energy loss caused by data retransmission.

[0092] In the early stage of the operation, the overall energy supply is relatively sufficient. As the operation progresses, when entering the later stage of the operation, energy supply in some areas is difficult, and the remaining energy of the nodes becomes scarce. At this time, in order to ensure that the nodes in the cluster have enough energy to maintain communication and combat tasks after clustering, the remaining energy E i The weight factor w Ei Increase Δw E , so that in clustering and subsequent operations, nodes with higher residual energy are given priority to ensure that communication and task execution within the cluster can continue, avoiding communication interruption or task failure due to node energy exhaustion.

[0093] Concealment i The weight factor w Mi Adjustments need to be made to take into account the secrecy of the mission and the enemy's reconnaissance capabilities. In a hostile environment, especially when performing infiltration or secret reconnaissance missions, secrecy is crucial. Therefore, the secrecy M i The weight factor w Mi Δw should be increased M , in order to reduce the risk of being discovered by the enemy. i The weight factor w MiAdjustments should also be combined with the platform's stealth design, noise control and electromagnetic signal management capabilities to ensure the covert execution of the mission.

[0094] Communication range R i The weight factor w Ri Adjustments should be made based on the geographic span of the mission and the degree of communications dependency. For missions that require cross-domain coordination or operations in an electromagnetically interfered environment, the communications range R i The weight factor w Ri Δw should be increased R , ensuring the continuity of command and control and the reliability of data transmission. i The weight factor w Ri When doing so, the advancement of communication technology and the communication equipment capabilities of the platform should also be considered to achieve effective long-distance communication.

[0095] Scouting coverage C i The weight factor w Ci Adjustments should be made based on intelligence collection requirements and the characteristics of the mission area. When extensive surveillance or missions over a wide area are required, reconnaissance coverage should be C i The weight factor w Ci Δw should be increased c The weight adjustment should also take into account the performance of the sensor, the height and location of the platform, and possible environmental interference to maximize reconnaissance efficiency and coverage.

[0096] Step 5: Select the node with the largest clustering weight as the cluster head, and the cluster head node and its neighbors that are not assigned cluster head nodes form a cluster.

[0097] Step 6: Repeat steps 4 and 5 to select cluster heads until all nodes are allocated, achieving a balance between energy utilization and communication efficiency.

[0098] When the remaining energy of the cluster head is lower than the cluster head update threshold, the cluster head update mechanism is triggered: the clustering weights of the nodes are recalculated according to steps 4 to 6, and a new cluster head is selected and clustered.

[0099] The calculation formula of the threshold T of the cluster head update is:

[0100]

[0101] Among them, E0 is the energy of the cluster head node when it becomes the cluster head, and k is the proportional coefficient adjusted according to the specific needs and environment of the combat network.

[0102] The cluster head update mechanism is triggered only when the remaining energy of the cluster head is lower than the cluster head update threshold, which effectively reduces the number of cluster head replacements, reduces unnecessary energy consumption, reduces communication and system operation energy consumption, and extends the life of the combat network. The cluster head selection can be dynamically adjusted according to the node energy consumption. Effectively reduce the energy consumption caused by cluster head replacement.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-platform energy optimization method based on combat differentiation weighted clustering, characterized in that: The following steps are involved: Step 1: Obtain environmental parameters and platform parameters of various platforms, wherein the platform parameters include the number, speed, remaining energy, data reception quality, and initial position of each platform; Step 2: Select the residual energy, data reception quality in the platform parameters, and the relative average distance and relative average speed calculated based on the environmental parameters and other platform parameters to form the basic clustering index for cluster head selection; Step 3: at least one of the communication range, concealment or reconnaissance coverage and the basic clustering index constitute a differentiated clustering index for cluster head selection, and a calculation formula for clustering weights is established based on the differentiated clustering index; Step 4: The node determines the calculation formula of clustering weight according to the platform characteristics and combat mission requirements, and adaptively adjusts the weight factor corresponding to each differentiated clustering indicator in the calculation formula of clustering weight to calculate the clustering weight of the node; Step 5: Select the node with the largest clustering weight as the cluster head, and the cluster head node and the nodes of the cluster head node's neighbors that are not assigned cluster heads form a cluster; Step 6: Repeat steps 4 and 5 to select cluster heads until all nodes are allocated, achieving a balance between energy utilization and communication efficiency; When the remaining energy of the cluster head is lower than the cluster head update threshold, the cluster head update mechanism is triggered: the clustering weights of the nodes are recalculated according to steps 4 to 6, and a new cluster head is selected and clustered.

2. The multi-platform energy optimization method based on combat differentiation weighted clustering according to claim 1 is characterized in that: The method for establishing a calculation formula for clustering weights based on differentiated clustering indicators is: When the differentiated clustering index is the basic clustering index and the communication range, the calculation formula of the clustering weight W is: When the differentiated clustering index is the basic clustering index and concealment, the calculation formula of the clustering weight W is: When the differentiated clustering index is the basic clustering index and the reconnaissance coverage, the clustering weight W is calculated as: Among them, the subscript i represents the node i, is the relative average distance D i The weight factor of Relative average speed The weight factor of PRQ is the data reception quality i The weight factor The remaining energy E i The weight factor of The communication range R i The weight factor of For reconnaissance coverage C i The weight factor of For concealment i The weight factor of The relative average distance The maximum value of Relative average speed The maximum value, PRQ max PRQ is the data reception quality i The maximum value, E max The remaining energy E i The maximum value, R max The communication range R i The maximum value, R i is the communication range, C i For reconnaissance coverage, M i For concealment, α is or Ci .

3. The multi-platform energy optimization method based on combat differentiation weighted clustering according to claim 1 or 2, characterized in that: The data reception quality S i is the number of successfully received data packets, T i The number of packets sent.

4. The multi-platform energy optimization method based on combat differentiation weighted clustering according to claim 3 is characterized in that: The remaining energy E i Obtained from the platform's own energy monitoring module.

5. The multi-platform energy optimization method based on combat differentiation weighted clustering according to claim 4 is characterized in that: The relative average distance Among them, D in is the relative distance between the node and its neighbor nodes, N is the number of neighbor nodes, and n represents neighbor node n.

6. The multi-platform energy optimization method based on combat differentiation weighted clustering according to claim 5 is characterized in that: The relative average speed Among them, V(n) is the set of neighbor node velocities.

7. The multi-platform energy optimization method based on combat differentiation weighted clustering according to claim 6 is characterized in that: The reconnaissance coverage area C i The influencing parameters include the sensor range and viewing angle of the node; the communication range R i The effective communication distance is obtained by determining the performance parameters of the communication equipment of the node; the concealment M i The influencing parameters include the node's noise level, visual concealment and electronic signal concealment.

8. The multi-platform energy optimization method based on combat differentiation weighted clustering according to claim 1 or 7, characterized in that: The method of adaptively adjusting the weight factor size corresponding to each differentiated clustering index in the calculation formula of the clustering weight is as follows: when the increase in communication energy loss is due to the relative average distance The increase leads to the relative average distance The weight factor Increase Δw D ; When the increase in communication energy loss is due to the relative average speed The relative average speed The weight factor Increase Δw V ; When the increase in communication energy loss is due to the data reception quality PRQ i If the data reception quality PRQ i The weight factor w PRQi Increase Δw PRQ ; When the overall energy supply is insufficient in the late stage of the battle, the remaining energy E i The weight factor Increase Δw E ; The adjustment of the weight factors corresponding to the communication range, concealment, and reconnaissance coverage is determined by the mission characteristics.

9. The multi-platform energy optimization method based on combat differentiation weighted clustering according to claim 8 is characterized in that: The method for adjusting the weight factors corresponding to the communication range, concealment, and reconnaissance coverage is as follows: when the task performed by the platform depends on concealment, the concealment M i The weight factor Increase Δw M When the tasks performed by the platform depend on the communication range, the communication range R i The weight factor Increase Δw R ; When the mission performed by the platform depends on the reconnaissance coverage, the reconnaissance coverage C i The weight factor Increase Δw c .

10. The multi-platform energy optimization method based on combat differentiation weighted clustering according to claim 9 is characterized in that: The cluster head update threshold Among them, E0 is the energy when the cluster head node becomes the cluster head, and k is the proportional coefficient.

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