CN114722897B - Method for improving battlefield comprehensive situation information processing efficiency - Google Patents
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Abstract
The invention relates to a method for improving the processing efficiency of battlefield comprehensive situation information, which comprises the following steps: s1: acquiring clustering information groups obtained by various perceptrons in the combat process; s2: calculating the shortest distance of the mutual conversion between different clustering information groups by using the relative entropy; s3: and finishing information jump according to the shortest distance. According to the method for improving the battlefield comprehensive situation information processing efficiency, in the process of processing massive situation information, the shortest distance for mutual conversion between two clustering information groups is found by utilizing the relative entropy, so that the jump processing speed is increased, the comprehensive situation information can reach a command processing point more quickly, timely judgment is further carried out at different combat moments, and the combat efficiency value is maximized.
Description
Technical Field
The invention belongs to the field of information processing, and particularly relates to a method for improving the information processing efficiency of battlefield comprehensive situation.
Background
The arrival of informationized warfare, sensing equipment, reconnaissance means and information systems are applied to a battlefield environment, multi-source, multi-dimensional, heterogeneous and explosive growing battlefield situation data information appears, and situation information sensing relies on various types of signal sensing equipment, technical reconnaissance means and command information systems to collect and aggregate battlefield situation elements.
For combat weapons, combat efficiency is a primary consideration, combat information is in a massive growth mode in a modern war state, the information generally has clustering characteristics, meanwhile, in the combat process, information can be jumped and converted in a large amount of information, a large amount of explosive information gathering can lead to complicated information processing, time is prolonged, massive situation data processing becomes slow, efficiency of transferring and jumping processing of clustered information groups is reduced, situation judgment is delayed, and instantaneity is lacked.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for improving the battlefield comprehensive situation information processing efficiency. The technical problems to be solved by the invention are realized by the following technical scheme:
The invention provides a method for improving the processing efficiency of battlefield comprehensive situation information, which comprises the following steps:
s1: acquiring clustering information groups obtained by various perceptrons in the combat process;
S2: calculating the shortest distance of the mutual conversion between different clustering information groups by using the relative entropy;
S3: and finishing information jump according to the shortest distance.
In one embodiment of the invention, the categories of the cluster information groups include ontology information, weapon information, and target information.
In one embodiment of the present invention, the S2 includes:
S21: according to shannon's information theory, a probability distribution of a character set is set to represent a cluster information group, and a code is designed so that the number of bits required to represent the cluster information group is minimum;
S22: and measuring the distance between two clustering information groups by adopting relative entropy, and calculating the shortest distance of the mutual conversion between different clustering information groups according to the codes.
In one embodiment of the present invention, the S21 includes:
s211: let the character set be X, for a character X e X, the probability of occurrence of the character X is P (X), and the number of bits required for optimal coding of the character X is equal to the entropy of the character set:
H(X)＝∑x∈XP(x)log[1/P(x)]，
h (X) represents the information amount of the character set X;
S212: the optimal coding with the probability distribution P (X) is the character coding conforming to the probability distribution Q (X),
DKL(Q||P)＝∑x∈XQ(x)[log(1/P(x))]-∑x∈XQ(x)[log[1/Q(x)]]＝∑x∈XQ(x)log[Q(x)/P(x)],
Wherein D KL (Q||P) represents the number of optimally encoded bits required for P (X) to transition to Q (X), P (X) represents one probability distribution over the character set X, and Q (X) represents another probability distribution that exists over the character set X;
s213: the optimal coding of the probability distribution Q (X) is used for character coding conforming to the probability distribution P (X), and the bit number D KL (P||Q) of the optimal coding required by the conversion of the Q (X) to the P (X) is obtained.
In one embodiment of the present invention, the S22 includes:
And according to the bi-directional coded bit numbers D KL (Q||P) and D KL (P||Q), averaging to obtain the shortest distance of the mutual conversion between different clustering information groups.
In one embodiment of the present invention, the step S3 further includes:
S4: and carrying out normalization processing on all the shortest distances obtained by each jump, so that all the shortest distances are normalized.
Compared with the prior art, the invention has the beneficial effects that:
According to the method for improving the battlefield comprehensive situation information processing efficiency, in the process of processing massive situation information, the shortest distance for mutual conversion between two clustering information groups is found by utilizing the relative entropy, so that the jump processing speed is increased, the comprehensive situation information can reach a command processing point more quickly, timely judgment is further carried out at different combat moments, and the combat efficiency value is maximized.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention, as well as the preferred embodiments thereof, together with the following detailed description of the invention, given by way of illustration only, together with the accompanying drawings.
Drawings
FIG. 1 is a schematic diagram of a method for improving the processing efficiency of battlefield comprehensive situation information according to an embodiment of the present invention;
fig. 2 is a schematic diagram of another method for improving the processing efficiency of battlefield comprehensive situation information according to an embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects adopted by the invention to achieve the preset aim, the following describes in detail a method for improving the battlefield comprehensive situation information processing efficiency according to the invention by referring to the attached drawings and the specific embodiments.
The foregoing and other features, aspects, and advantages of the present invention will become more apparent from the following detailed description of the preferred embodiments when taken in conjunction with the accompanying drawings. The technical means and effects adopted by the present invention to achieve the intended purpose can be more deeply and specifically understood through the description of the specific embodiments, however, the attached drawings are provided for reference and description only, and are not intended to limit the technical scheme of the present invention.
Example 1
Referring to fig. 1, fig. 1 is a schematic diagram of a method for improving the processing efficiency of battlefield comprehensive situation information according to an embodiment of the present invention, as shown in the drawing, the method for improving the processing efficiency of battlefield comprehensive situation information according to the embodiment includes:
s1: acquiring clustering information groups obtained by various perceptrons in the combat process;
in this embodiment, the categories of the cluster information group include body information, weapon information, and target information.
For the fight weapon, the fight efficiency is the primary factor, and fight information is in a mass growth mode in the modern fight state, meanwhile, the information can jump and change in a large amount of information in the fight process, and the path distance among the clustered information groups is calculated at the required time in the fight process, so that the information transfer reaches the maximum efficiency.
S2: calculating the shortest distance of the mutual conversion between different clustering information groups by using the relative entropy;
in this embodiment, the information distance between two clusters is measured mainly by the relative entropy in the information entropy, and the most suitable common symmetric information distance between two clusters is not direct, that is, the smallest amount of information sufficient to switch between two clusters, thereby effectively switching the two information to each other.
The relative entropy is also called KL-distance, which is a measure of the asymmetry of the differences of the two probability distributions P and Q, the information distance being calculated reversibly. The relative entropy can measure the distance between two random distributions, and when the two random distributions are identical, their relative entropy is zero, and when the difference between the two random distributions increases, their relative entropy also increases. The relative entropy (KL-distance) can be used to compare the degree of matching. KL-distance is a measure of the number of bits that are multipurpose per character on average in this case and can therefore be used to measure the distance of two distributions.
Specifically, S2 includes:
S21: according to shannon's information theory, a probability distribution of a character set is set to represent a cluster information group, and a code is designed so that the number of bits required to represent the cluster information group is minimum;
Specifically, S21 includes:
S211: let the character set be X, for the character X ε X, the probability of occurrence of the character X is P (X), the number of bits required for optimal coding of the character X is equal to the entropy of the character set:
H(X)＝∑x∈XP(x)log[1/P(x)](1)，
h (X) represents the information amount of the character set X;
S212: the optimal coding with the probability distribution P (X) is the character coding conforming to the probability distribution Q (X),
DKL(Q||P)＝∑x∈XQ(x)[log(1/P(x))]-∑x∈XQ(x)[log[1/Q(x)]]＝∑x∈XQ(x)log[Q(x)/P(x)](2),
Wherein D KL (Q||P) represents the number of optimally encoded bits required for P (X) to transition to Q (X), P (X) represents one probability distribution over the character set X, and Q (X) represents another probability distribution that exists over the character set X;
In the present embodiment, two clusters are represented by two probability distributions (P (X) and Q (X)) of the set character set X, and the number of bits required to represent the clusters is the information amount of the clusters.
It should be noted that, if the optimal code of the probability distribution P (X) is a code length of the character X equal to log [1/P (X) ], and the optimal code of the probability distribution P (X) is a code of the character conforming to the distribution Q (X), it means that the characters are more than ideal by some bits. KL-distance is used to measure the number of bits that are averaged per character in this case, and thus can be used to measure the distance between two probability distributions P (X) and Q (X) (i.e., two clusters).
Note that for equation (2) since-log (u) is a convex function, there is the following inequality:
DKL(Q||P)＝-∑x∈XQ(x)log[P(x)/Q(x)]＝E[-logP(x)/Q(x)]≥-logE[P(x)/Q(x)]
＝-log∑x∈XQ(x)P(x)/Q(x)＝0(3)，
The difference between two random distributions is measured by using the relative entropy, when the two random distributions are the same, the relative entropy is 0, when the difference between the two random distributions is increased, namely the KL-distance is always more than or equal to 0, and when and only when the two random distributions are the same, the KL-distance is equal to 0.
S213: the optimal coding of the probability distribution Q (X) is used for character coding conforming to the probability distribution P (X), and the bit number D KL (P||Q) of the optimal coding required by the conversion of the Q (X) to the P (X) is obtained.
In this embodiment, similar to the optimally encoded bit number D KL (q||p) required for the conversion of P (X) to Q (X) in step S212, the optimally encoded bit number D KL (p||q) required for the conversion of Q (X) to P (X) is obtained, and the detailed process is not repeated here.
S22: and measuring the distance between two clustering information groups by adopting relative entropy, and calculating the shortest distance of the mutual conversion between different clustering information groups according to the codes.
Specifically, S22 includes: and according to the bi-directional coded bit numbers D KL (Q||P) and D KL (P||Q), averaging to obtain the shortest distance of the mutual conversion between different clustering information groups.
An information source is encoded according to its own probability distribution, and the average bit number of each character is the minimum, which is the concept of information entropy and measures the information content of the information source.
In addition, KL-distance does not satisfy symmetry, i.e., D (p|q) is not necessarily equal to D (q|p), although KL divergence is intuitively a measure or distance function, it is not a true measure or distance because it does not have symmetry, i.e., an identical code reversible distance when one of these calculations requires more information than the other, the smallest code cannot be exactly the same, but overlap can still be accomplished, a larger code can contain all the information in a shorter code, and some additional information.
Then, the shortest coding of the overlap is enough to switch between the two clustering information groups, namely the smaller relative entropy is the shortest distance between the two clustering information groups, the shortest distance of the mutual switching between the two clustering information groups is found, and the information skip is quickened through the shortest distance.
S3: and finishing information jump according to the shortest distance.
Specifically, the information skipping function is completed according to the obtained shortest distance direct skipping, so that the cluster information group skipping processing speed is increased, and the efficiency is improved.
After the information is jumped, a specific control operation implementation instruction is sent according to the jump result so as to achieve real-time processing and control, and further timely judgment can be carried out at different combat moments, so that the combat efficiency value is maximized.
Further, referring to fig. 2 in combination, fig. 2 is a schematic diagram of another method for improving the battlefield comprehensive situation information processing efficiency according to an embodiment of the present invention, as shown in the drawing, in another embodiment, after S3, the method further includes:
S4: and carrying out normalization processing on all shortest distances obtained by each jump, so that all shortest information distances are standardized.
According to the method for improving the battlefield comprehensive situation information processing efficiency, the characteristic of battlefield clustering is fully combined with the information entropy, the shortest distance of mutual conversion between two clustering information groups is found by utilizing the relative entropy in the massive situation information processing process, the jump processing speed is accelerated, the comprehensive situation information can reach a command processing point more quickly, timely judgment is carried out at different battlefield moments, and the battlefield efficiency value is maximized.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in an article or device comprising the element.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (2)
1. The method for improving the battlefield comprehensive situation information processing efficiency is characterized by comprising the following steps:
S1: acquiring clustering information groups obtained by various perceptrons in the combat process; the categories of the cluster information group comprise ontology information, weapon information and target information;
S2: calculating the shortest distance of the mutual conversion between different clustering information groups by using the relative entropy; the step S2 comprises the following steps:
s21: according to shannon's information theory, a probability distribution of a character set is set to represent a cluster information group, and a code is designed so that the number of bits required to represent the cluster information group is minimum; the S21 includes:
s211: let the character set be X, for a character X e X, the probability of occurrence of the character X is P (X), and the number of bits required for optimal coding of the character X is equal to the entropy of the character set:
H(X)＝∑x∈XP(x)log[1/P(x)]，
h (X) represents the information amount of the character set X;
S212: the optimal coding with the probability distribution P (X) is the character coding conforming to the probability distribution Q (X),
DKL(Q||P)＝∑x∈XQ(x)[log(1/P(x))]-∑x∈XQ(x)[log[1/Q(x)]]＝∑x∈XQ(x)log[Q(x)/P(x)],
Wherein D KL (Q||P) represents the number of optimally encoded bits required for P (X) to transition to Q (X), P (X) represents one probability distribution over the character set X, and Q (X) represents another probability distribution that exists over the character set X;
S213: the optimal coding of the probability distribution Q (X) is used for character coding conforming to the probability distribution P (X), so that the bit number D KL (P||Q) of the optimal coding required by the conversion of the Q (X) to the P (X) is obtained;
s22: measuring the distance between two clustering information groups by adopting relative entropy, and calculating the shortest distance of the mutual conversion between different clustering information groups according to the codes;
s3: completing information jump according to the shortest distance;
S4: and carrying out normalization processing on all the shortest distances obtained by each jump, so that all the shortest distances are normalized.
2. The method for improving battlefield comprehensive situation information processing efficiency according to claim 1, wherein S22 comprises:
And according to the bi-directional coded bit numbers D KL (Q||P) and D KL (P||Q), averaging to obtain the shortest distance of the mutual conversion between different clustering information groups.
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| CN106991127A (en) * | 2017-03-06 | 2017-07-28 | 西安交通大学 | A kind of knowledget opic short text hierarchy classification method extended based on topological characteristic |
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| CN110619052B (en) * | 2019-08-29 | 2021-12-28 | 中国电子科技集团公司第二十八研究所 | Knowledge graph-based battlefield situation sensing method |
| CN113987789B (en) * | 2021-10-26 | 2022-09-09 | 西北工业大学 | Dynamic threat assessment method in unmanned aerial vehicle collaborative air combat |
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| CN103514398A (en) * | 2013-10-18 | 2014-01-15 | 中国科学院信息工程研究所 | Real-time online log detection method and system |
| CN106991127A (en) * | 2017-03-06 | 2017-07-28 | 西安交通大学 | A kind of knowledget opic short text hierarchy classification method extended based on topological characteristic |
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