CN114722897A - 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 information processing efficiency of battlefield comprehensive situation, which comprises the following steps: s1: acquiring clustering information groups obtained by various sensors in the fighting process; s2: calculating the shortest distance of mutual conversion between different clustering information groups by using the relative entropy; s3: and finishing information skipping 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 of mutual conversion between two kinds of clustering information groups is found by using the relative entropy, the skip processing speed is accelerated, the comprehensive situation information can reach a command processing point more quickly, and then timely judgment is carried out at different combat moments, so that 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 the informationized war, sensing equipment, reconnaissance means and an information system are applied to a battlefield environment, the multi-source, multi-dimensional, heterogeneous and explosively-increased battlefield situation data information appears, and situation information is perceived by means of various types of signal sensing equipment, technical reconnaissance means and a command information system to collect and gather battlefield situation elements.
For the battle weapons, the battle efficiency is the primary factor to be considered, the battle information is in a mass growth mode under the modern war state, the information generally has the characteristic of clustering, meanwhile, the information can jump in a large amount of information in the battle process, the information processing is complicated due to the mass explosive information aggregation, the time is prolonged, the mass situation data processing is slow, the efficiency of the cluster information group transfer jumping processing is reduced, the situation judgment is delayed, and the real-time performance is lacked.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for improving the processing efficiency of battlefield comprehensive situation information. The technical problem to be solved by the invention is realized by the following technical scheme:
the invention provides a method for improving the information processing efficiency of battlefield comprehensive situation, which comprises the following steps:
s1: acquiring clustering information groups obtained by various sensors in the fighting process;
s2: calculating the shortest distance of mutual conversion between different clustering information groups by using the relative entropy;
s3: and finishing information skipping according to the shortest distance.
In one embodiment of the invention, the categories of the cluster information group comprise body information, weapon information and target information.
In an embodiment of the present invention, the S2 includes:
s21: according to the Shannon information theory, setting probability distribution of a character set to represent a clustering information group, and designing a code to minimize the bit number required for representing the clustering information group;
s22: and measuring the distance between the two clustering information groups by adopting relative entropy, and calculating the shortest distance of the interconversion between different clustering information groups according to the codes.
In an embodiment of the present invention, the S21 includes:
s211: and if the character set is X, for a character X belonging to X, the probability of the character X is P (X), and the average bit number required by the 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 code of the probability distribution P (X) is used for coding characters according with 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 DKL (Q | | P) represents the number of optimally coded bits required for P (X) to convert to Q (X), P (X) represents one probability distribution over the alphabet X, Q (X) represents another probability distribution that exists over the alphabet X;
s213: the optimal coding of the probability distribution Q (X) is used for coding characters according with the probability distribution P (X), and the bit number DKL (P | | Q) of the optimal coding required for converting Q (X) to P (X) is obtained.
In an embodiment of the present invention, the S22 includes:
and averaging the two-way coded bit numbers DKL (Q | | | P) and DKL (P | | Q) to obtain the shortest distance of the mutual conversion between different clustering information groups.
In an embodiment of the present invention, after S3, the method further includes:
s4: and performing normalization processing on all the shortest distances obtained by each jump to standardize all the shortest distances.
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 of mutual conversion between two kinds of clustering information groups is found by using the relative entropy, the skip processing speed is accelerated, the comprehensive situation information can reach a command processing point more quickly, and then timely judgment is carried out at different combat moments, so that the combat efficiency value is maximized.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
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Fig. 1 is a schematic diagram of a method for improving battlefield comprehensive situation information processing efficiency according to an embodiment of the present invention;
fig. 2 is a schematic diagram of another method for improving the processing efficiency of battlefield integrated situation information according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined invention purpose, the following will explain in detail a method for improving the battlefield integrated situation information processing efficiency according to the present invention with reference to the accompanying drawings and the detailed implementation.
The foregoing and other technical contents, features and effects of the present invention will be more clearly understood from the following detailed description of the embodiments taken in conjunction with the accompanying drawings. The technical means and effects of the present invention adopted to achieve the predetermined 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 used for limiting the technical scheme of the present invention.
Example one
Referring to fig. 1, fig. 1 is a schematic diagram of a method for improving battlefield integrated situation information processing efficiency according to an embodiment of the present invention, and as shown in the drawing, the method for improving battlefield integrated situation information processing efficiency according to the embodiment includes:
s1: acquiring clustering information groups obtained by various sensors in the fighting process;
in the present embodiment, the category of the cluster information group includes body information, weapon information, and target information.
For the battle weapons, the battle efficiency is the primary factor to be considered, the battle information is in a mass growth mode under the modern battle state, meanwhile, the information can jump in a large amount of information in the battle process, and the path distance between the clustering information groups is calculated at any moment in the battle process, so that the information transmission reaches the maximum efficiency.
S2: calculating the shortest distance of mutual conversion between different clustering information groups by using the relative entropy;
in the embodiment, the information distance between two clustering information groups is mainly measured by the relative entropy in the information entropy, and the most suitable general symmetric information distance between two clustering information groups is not direct, that is, the minimum information amount enough for mutually converting between two clustering information groups, thereby effectively converting two information with each other.
The relative entropy is also called KL-distance, which is a measure of the asymmetry of the difference between two probability distributions P and Q, the information distance that can be reversibly calculated. Relative entropy measures the distance between two random distributions, where the relative entropy is zero when the two random distributions are the same, and increases when the difference between the two random distributions increases. The relative entropy (KL-distance) can be used to compare the degree of matching. The KL-distance is a measure of the number of bits used per character on average in this case, and thus can be used to measure the distance between two distributions.
Specifically, S2 includes:
s21: according to the Shannon information theory, setting probability distribution of a character set to represent a clustering information group, and designing a code to minimize the bit number required for representing the clustering information group;
specifically, S21 includes:
s211: setting a character set as X, wherein the probability of the character X is P (X) for the character X belonging to X, and the average bit number required by the 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 code of the probability distribution P (X) is used for coding characters according with 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 DKL (Q | | P) represents the number of bits needed for P (X) to convert to Q (X) for optimal encoding, P (X) represents one probability distribution over the alphabet X, Q (X) represents another probability distribution that exists over the alphabet X;
in this embodiment, two cluster information groups are represented by two probability distributions (p (X) and q (X)) of a set character set X, and the number of bits required to represent the cluster information group is the amount of information of the cluster information group.
It should be noted that the optimal coding of the probability distribution p (x) is that the coding length of the character x is equal to log [1/p (x) ], and if the optimal coding of the probability distribution p (x) is the character coding conforming to the distribution q (x), the characters are represented by using a number of bits more than the ideal case. The KL-distance is a measure of the number of bits used to average each 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).
It should be noted 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)，
and (3) classifying by using relative entropy or measuring the difference between two random distributions by using the relative entropy, wherein 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 greater than or equal to 0, and when and only when the two distributions are the same, the KL-distance is equal to 0.
S213: and (3) the optimal code of the probability distribution Q (X) is used as the character code according with the probability distribution P (X), and the bit number DKL (P | | Q) of the optimal code required by converting Q (X) to P (X) is obtained.
In this embodiment, similar to the optimal encoding bit number DKL (Q | | P) required for converting P (x) to Q (x) in step S212, the optimal encoding bit number DKL (P | | Q) required for converting Q (x) to P (x) is obtained, and the detailed process is not repeated here.
S22: and measuring the distance between the two clustering information groups by using the relative entropy, and calculating the shortest distance of interconversion between different clustering information groups according to the codes.
Specifically, S22 includes: and averaging the two-way coded bit numbers DKL (Q | | P) and DKL (P | | Q) to obtain the shortest distance of mutual conversion between different clustering information groups.
The information source is coded according to the probability distribution of the information source, the average bit number of each character is minimum, and the concept of information entropy is used for measuring the information quantity 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 metric or distance function, it is not a true metric or distance because it does not have symmetry, i.e., a same coded invertible distance when one of these calculations requires more information than the other, then the smallest code cannot be exactly the same, but overlap can still be accomplished, and a larger code can contain all the information in the shorter code, as well as some additional information.
Then, the overlapped shortest codes are enough to be converted 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 mutual conversion between the two clustering information groups is found, and the information jump is accelerated through the shortest distance.
S3: and finishing information skipping according to the shortest distance.
Specifically, the information skipping function is completed according to the obtained shortest distance direct skipping, so that the skipping processing speed of the clustering information group is increased, and the efficiency is improved.
It should be noted that after the information skip is completed, a specific control operation implementation instruction is sent based on the skip result to achieve real-time processing and control, and then timely judgment can be performed at different combat moments to maximize the combat efficiency value.
Further, please refer to fig. 2 in combination, fig. 2 is a schematic diagram of another method for improving the processing efficiency of battlefield integrated situation information according to an embodiment of the present invention, as shown in the drawing, in another embodiment, after S3, the method further includes:
s4: and normalizing all the shortest distances obtained by each jump to standardize all the shortest message distances.
According to the method for improving the battlefield comprehensive situation information processing efficiency, the clustering characteristics of the battlefield information are fully combined with the information entropy, the shortest distance of mutual conversion between two kinds of clustering information groups is found by using the relative entropy in the massive situation information processing process, the skip processing speed is accelerated, the comprehensive situation information can reach command processing points more quickly, and then timely judgment is carried out at different battlefield moments, so that the battlefield comprehensive situation information processing 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 an … …" does not exclude the presence of additional like elements in the article or device in which the element is included.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, numerous simple deductions or substitutions may be made without departing from the spirit of the invention, which shall be deemed to belong to the scope of the invention.
Claims (6)
1. A method for improving the processing efficiency of battlefield comprehensive situation information is characterized by comprising the following steps:
s1: acquiring clustering information groups obtained by various sensors in the fighting process;
s2: calculating the shortest distance of mutual conversion between different clustering information groups by using the relative entropy;
s3: and finishing information skipping according to the shortest distance.
2. The method for improving the processing efficiency of the comprehensive situation information of the battlefield according to claim 1, wherein the categories of the clustering information groups comprise body information, weapon information and target information.
3. The method for improving battlefield integrated situation information processing efficiency as claimed in claim 1, wherein said S2 includes:
s21: according to the Shannon information theory, setting probability distribution of a character set to represent a clustering information group, and designing a code to minimize the bit number required for representing the clustering information group;
s22: and measuring the distance between the two clustering information groups by adopting relative entropy, and calculating the minimum distance of the interconversion between different clustering information groups according to the codes.
4. The method for improving battlefield integrated situation information processing efficiency according to claim 3, wherein the S21 includes:
s211: and if the character set is X, for a character X belonging to X, the probability of the character X is P (X), and the average bit number required by the 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 code of the probability distribution P (X) is used for coding characters according with 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 DKL (Q | | P) represents the number of bits needed for P (X) to convert to Q (X) for optimal encoding, P (X) represents one probability distribution over the alphabet X, Q (X) represents another probability distribution that exists over the alphabet X;
s213: the optimal coding of the probability distribution Q (X) is used for coding characters according with the probability distribution P (X), and the bit number DKL (P | | Q) of the optimal coding required for converting Q (X) to P (X) is obtained.
5. The method for improving battlefield integrated situation information processing efficiency according to claim 3, wherein the S22 includes:
and averaging the two-way coded bit numbers DKL (Q | | | P) and DKL (P | | Q) to obtain the shortest distance of the mutual conversion between different clustering information groups.
6. The method for improving battlefield integrated situation information processing efficiency as claimed in claim 1, wherein said S3 is followed by further comprising:
s4: and performing normalization processing on all the shortest distances obtained by each jump to standardize all the shortest distances.
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