CN112883589A - Scene association degree calculation method and device, computer equipment and storage medium - Google Patents
Scene association degree calculation method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method and a device for calculating scene relevance, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring association factors among different scenes, and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances; comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes; performing multi-dimensional information fusion on the multi-scene correlation degree according to the scene model to obtain a joint probability density function of three dimensional matrixes; and calculating the interaction degree after fusion between different scenes through the joint probability density function, and acquiring the scene association degree according to the interaction degree. According to the embodiment of the invention, the incidence relation among a plurality of scenes is described by using a network-based method, and the scene incidence degrees of different dimensions are obtained under different scenes so as to carry out strategic strength according to the incidence degrees in order to support the weight configuration in the subsequent calculation of the contribution rates of the plurality of scenes.
Description
Technical Field
The present application relates to the field of battlefield environment management technologies, and in particular, to a method and an apparatus for calculating a scene relevancy, a computer device, and a storage medium.
Background
Scenes tend to be correlated and cross-correlation may occur at multiple angles. For example: aiming at the current strategic situation of China, the south China sea direction and the south China coastal direction have certain correlation in the strategic direction and certain correlation in the strategic resource allocation. For the situation that multiple-aspect association exists, the traditional multi-scenario modeling method which assumes that scenarios are independent cannot be used for effective description and model construction. How to obtain scene relevancy of different dimensions in different scenes and further carry out strategic direction force delivery through relevancy needs further technical innovation.
Disclosure of Invention
In view of the above, it is necessary to provide a scene relevance calculating method, apparatus, computer device and storage medium for solving the above technical problems.
In a first aspect, an embodiment of the present invention provides a method for calculating a scene relevance, including:
acquiring association factors among different scenes, and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances;
comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes;
performing multi-dimensional information fusion on the multi-scene correlation degree according to the scene model to obtain a joint probability density function of three dimensional matrixes;
and calculating the interaction degree after fusion between different scenes through the joint probability density function, and acquiring the scene association degree according to the interaction degree.
Further, the obtaining of the association factors between different scenes and the dividing of the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliance includes:
obtaining strategic traction degrees in different scenes according to the strategic mutual traction degrees in different scenes;
determining strategic resource allocation according to whether strategic resources of the same type are allocated among different scenes, wherein the strategic resource allocation is generally determined by task types in strategic directions;
determining whether the interest relationship is a union relationship or an enemy relationship according to the interest relationship among strategic adversaries in different scenes, obtaining the probability of simultaneous outbreaks in different scenes, and determining the strategic adversaries to ally.
Further, the establishing of the multi-scene model of the three dimensional matrices after the comprehensive evaluation of the three divided correlation factors includes:
defining a correlation degree score set of the three correlation factors under different scenes, and performing multi-source information fusion through the correlation degree score set to obtain mathematical expectations of the three correlation factors;
defining variable Xab-t，Xab-r，Xab-kRespectively representing the strategic traction association degree, the strategic resource allocation association degree and the strategic opponent union association degree;
and calculating the covariance of the correlation degrees in three dimensions according to the mathematical expectation of the three correlation factors.
Further, the calculating the interaction degree after the fusion between different scenes through the joint probability density function and obtaining the scene association degree according to the interaction degree includes:
obtaining likelihood functions under different scenes through normal distribution of the joint probability density function;
calculating covariance matrixes under different scenes according to the likelihood functions;
and verifying normal distribution of information fusion between different scenes by using the covariance matrix to obtain a fusion association mean value, and determining the interaction degree after fusion between different scenes by using the fusion association mean value.
On the other hand, an embodiment of the present invention further provides a system for calculating a scene relevancy, including:
the dimension division module is used for acquiring the association factors among different scenes and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances;
the model establishing module is used for comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes;
the function calculation module is used for carrying out multi-dimensional information fusion on the multi-scene relevance according to the scene model to obtain a joint probability density function of three dimensional matrixes;
and the association degree acquisition module is used for calculating the interaction degree after fusion between different scenes through the joint probability density function and acquiring the scene association degree according to the interaction degree.
Further, the dimension division module includes a correlation factor determination unit, and the correlation factor determination unit is configured to:
obtaining strategic traction degrees in different scenes according to the strategic mutual traction degrees in different scenes;
determining strategic resource allocation according to whether strategic resources of the same type are allocated among different scenes, wherein the strategic resource allocation is generally determined by task types in strategic directions;
determining whether the interest relationship is a union relationship or an enemy relationship according to the interest relationship among strategic adversaries in different scenes, obtaining the probability of simultaneous outbreaks in different scenes, and determining the strategic adversaries to ally.
Further, the model building module includes an association processing unit, and the association processing unit is configured to:
defining a correlation degree score set of the three correlation factors under different scenes, and performing multi-source information fusion through the correlation degree score set to obtain mathematical expectations of the three correlation factors;
defining variable Xab-t，Xab-r，Xab-kRespectively representing the strategic traction association degree, the strategic resource allocation association degree and the strategic opponent union association degree;
and calculating the covariance of the correlation degrees in three dimensions according to the mathematical expectation of the three correlation factors.
Further, the association degree obtaining module includes an association mean calculating unit, and the association mean calculating unit is configured to:
obtaining likelihood functions under different scenes through normal distribution of the joint probability density function;
calculating covariance matrixes under different scenes according to the likelihood functions;
and verifying normal distribution of information fusion between different scenes by using the covariance matrix to obtain a fusion association mean value, and determining the interaction degree after fusion between different scenes by using the fusion association mean value.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring association factors among different scenes, and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances;
comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes;
performing multi-dimensional information fusion on the multi-scene correlation degree according to the scene model to obtain a joint probability density function of three dimensional matrixes;
and calculating the interaction degree after fusion between different scenes through the joint probability density function, and acquiring the scene association degree according to the interaction degree.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring association factors among different scenes, and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances;
comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes;
performing multi-dimensional information fusion on the multi-scene correlation degree according to the scene model to obtain a joint probability density function of three dimensional matrixes;
and calculating the interaction degree after fusion between different scenes through the joint probability density function, and acquiring the scene association degree according to the interaction degree.
The beneficial effect of this application is: the scene association degree calculation method, the scene association degree calculation device, the computer equipment and the storage medium comprise: acquiring association factors among different scenes, and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances; comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes; performing multi-dimensional information fusion on the multi-scene correlation degree according to the scene model to obtain a joint probability density function of three dimensional matrixes; and calculating the interaction degree after fusion between different scenes through the joint probability density function, and acquiring the scene association degree according to the interaction degree. According to the embodiment of the invention, the incidence relation among a plurality of scenes is described by using a network-based method, and the scene incidence degrees of different dimensions are obtained under different scenes so as to carry out strategic strength according to the incidence degrees in order to support the weight configuration in the subsequent calculation of the contribution rates of the plurality of scenes.
Drawings
FIG. 1 is a flowchart illustrating a method for calculating a scene relevance according to an embodiment;
FIG. 2 is a schematic flow chart illustrating a method for determining the association factors under different scenarios according to an embodiment;
FIG. 3 is a flow chart illustrating a method for covariance calculation of correlation in one embodiment;
FIG. 4 is a schematic flow chart of the associated mean calculation under different scenarios in one embodiment;
FIG. 5 is a block diagram of a system for calculating a degree of context relevance according to an embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a scene relevance calculation method, including the steps of:
103, carrying out multi-dimensional information fusion on the multi-scene correlation degree according to the scene model to obtain a joint probability density function of three dimensional matrixes;
and 104, calculating the interaction degree after fusion between different scenes through the joint probability density function, and acquiring the scene association degree according to the interaction degree.
In particular, there is often a correlation between scenes, and cross-correlation may occur at multiple angles. For example: aiming at the current strategic situation of China, the south China sea direction and the south China coastal direction have certain correlation in the strategic direction and certain correlation in the strategic resource allocation. For the situation that multiple-aspect association exists, the traditional multi-scenario modeling method which assumes that scenarios are independent cannot be used for effective description and model construction. The embodiment describes the incidence relation among a plurality of scenes by using a network-based method, and supports the weight configuration in the subsequent calculation of the contribution rate of the plurality of scenes. The embodiment considers the multi-scenario equipment combination planning from the viewpoint of assuming the relationship between scenarios. If the scenes are assumed to be independent from each other, namely, the scenes do not contain mutual involvement and mutual constraint relation, the performance of the equipment combination in one scene is irrelevant to the performance in another scene. However, in the embodiment, it is assumed that the scenes are not independent, and the performance of the equipment combination in one scene is likely to affect the performance of the equipment combination in other scenes, so that the problem is more complicated, and a certain modeling method needs to be adopted to consider the association between the scenes. In the embodiment, the incidence relation among a plurality of scenes is described by using a network-based method, and in order to support the weight configuration in the subsequent calculation of the contribution rates of the plurality of scenes, the scene incidence degrees of different dimensions are obtained under different scenes, so that the power in the strategic direction is carried out according to the incidence degrees.
In one embodiment, as shown in fig. 2, the method for determining the association factor under different scenarios includes:
and step 203, determining whether the relation is an allied relation or an enemy relation according to the interest relations between strategic adversaries in different scenes, obtaining the probability of simultaneous outbreaks in different scenes, and determining the allied of the strategic adversaries.
In particular, the present embodiment needs to analyze which factors make the correlation between scenes. In the field of national defense, the scenes are considered to be mainly associated in three dimensions of strategic traction, strategic resource allocation and strategic adversary union. Wherein, the strategic traction: the method refers to the strategic traction degree of the two scenes, and how the strategic traction degree of the two scenes is higher to form the situation that the whole body is dragged, so that once the scene A is exploded, the scene B is likely to be triggered. Strategic resource allocation: the method refers to whether the same type of strategic resources are configured between two scenes, and the configuration of the strategic resources is usually determined by the task type in the strategic direction, for example, the offshore and airborne strategic resources are more configured in the south-sea direction, and the onshore strategic resources are more configured in the northwest direction. If the two scenes are highly correlated in strategic resource allocation, it means that the two scenes can support each other to some extent, for example, the fighting power in south China sea can support the war in east China sea direction if necessary. The strategic adversary allies: refers to the alliance relationship between the strategic adversaries of our parties on two scenes, and if the alliance degree of the strategic adversaries of the two scenes is high, the two scenes are likely to explode simultaneously. Conversely, if the strategic opponents of two scenes belong to a hostile relationship, then the probability of two scenes bursting at the same time is small. For example, in the east and northeast directions, the strategic opponents are japan and russia, respectively, and the two countries have poor alliance relationships, so that the possibility of simultaneous outbreaks in the two directions is considered to be low.
In one embodiment, as shown in fig. 3, the covariance calculation method of the correlation includes:
For example, for two independent scenes A and B, X is definedab-tA set of strategic traction relevance scores for scenes A and B; definition of Xab-rConfiguring a relevance grade set for strategic resources of the scenes A and B; is defined as Xab-kAnd (5) scoring a set of strategic adversary alliance relevance scores of the scenes A and B. Then, all experts are required to perform multi-source fusion on the relevance scores of three aspects between any two scenes, taking scenes A and B as examples, and taking a strategy traction relevance set Xab-tFor example, the discrete score approximation of all the elements therein is regarded as a one-dimensional continuous random variable set and follows a one-dimensional normal distribution, and is f (x) according to the corresponding density functionab-t) Then obtain X firstab-tA mathematical expectation of (d); similarly, strategic resource allocation relevance set score Xab-tAnd strategic adversary union association degree score set Xab-kIs approximately regarded as a continuous type random variable. And finally, calculating the covariance of the correlation degrees under three dimensions according to the mathematical expectation of the three correlation factors.
In one embodiment, as shown in fig. 4, the step of calculating the correlation mean under different scenarios includes:
and 403, verifying normal distribution of information fusion between different scenes by using the covariance matrix to obtain a fusion association mean value, and determining the interaction degree after fusion between different scenes according to the fusion association mean value.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in the above-described flowcharts may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or the stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 5, there is provided a scene relevancy calculation system including:
the dimension division module is used for acquiring the association factors among different scenes and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances;
the model establishing module is used for comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes;
the function calculation module is used for carrying out multi-dimensional information fusion on the multi-scene relevance according to the scene model to obtain a joint probability density function of three dimensional matrixes;
and the association degree acquisition module is used for calculating the interaction degree after fusion between different scenes through the joint probability density function and acquiring the scene association degree according to the interaction degree.
In one embodiment, the dimension division module comprises an association factor determination unit configured to:
obtaining strategic traction degrees in different scenes according to the strategic mutual traction degrees in different scenes;
determining strategic resource allocation according to whether strategic resources of the same type are allocated among different scenes, wherein the strategic resource allocation is generally determined by task types in strategic directions;
determining whether the interest relationship is a union relationship or an enemy relationship according to the interest relationship among strategic adversaries in different scenes, obtaining the probability of simultaneous outbreaks in different scenes, and determining the strategic adversaries to ally.
In one embodiment, the model building module comprises an association processing unit for:
defining a correlation degree score set of the three correlation factors under different scenes, and performing multi-source information fusion through the correlation degree score set to obtain mathematical expectations of the three correlation factors;
defining variable Xab-t，Xab-r，Xab-kRespectively representing the strategic traction association degree, the strategic resource allocation association degree and the strategic opponent union association degree;
and calculating the covariance of the correlation degrees in three dimensions according to the mathematical expectation of the three correlation factors.
In one embodiment, the association degree obtaining module includes an association mean calculating unit, and the association mean calculating unit is configured to:
obtaining likelihood functions under different scenes through normal distribution of the joint probability density function;
calculating covariance matrixes under different scenes according to the likelihood functions;
and verifying normal distribution of information fusion between different scenes by using the covariance matrix to obtain a fusion association mean value, and determining the interaction degree after fusion between different scenes by using the fusion association mean value.
For specific limitations of the scene relevance calculating system, reference may be made to the above limitations of the scene relevance calculating method, which is not described herein again. The modules in the scene relevancy calculation system may be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
FIG. 6 is a diagram illustrating an internal structure of a computer device in one embodiment. As shown in fig. 6, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program that, when executed by the processor, causes the processor to implement the method of privilege anomaly detection. The internal memory may also have a computer program stored therein, which when executed by the processor, causes the processor to perform the method for detecting an abnormality of authority. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring association factors among different scenes, and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances;
comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes;
performing multi-dimensional information fusion on the multi-scene correlation degree according to the scene model to obtain a joint probability density function of three dimensional matrixes;
and calculating the interaction degree after fusion between different scenes through the joint probability density function, and acquiring the scene association degree according to the interaction degree.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
obtaining strategic traction degrees in different scenes according to the strategic mutual traction degrees in different scenes;
determining strategic resource allocation according to whether strategic resources of the same type are allocated among different scenes, wherein the strategic resource allocation is generally determined by task types in strategic directions;
determining whether the interest relationship is a union relationship or an enemy relationship according to the interest relationship among strategic adversaries in different scenes, obtaining the probability of simultaneous outbreaks in different scenes, and determining the strategic adversaries to ally.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
defining a correlation degree score set of the three correlation factors under different scenes, and performing multi-source information fusion through the correlation degree score set to obtain mathematical expectations of the three correlation factors;
defining variable Xab-t，Xab-r，Xab-kRespectively representing the strategic traction association degree, the strategic resource allocation association degree and the strategic opponent union association degree;
and calculating the covariance of the correlation degrees in three dimensions according to the mathematical expectation of the three correlation factors.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
obtaining likelihood functions under different scenes through normal distribution of the joint probability density function;
calculating covariance matrixes under different scenes according to the likelihood functions;
and verifying normal distribution of information fusion between different scenes by using the covariance matrix to obtain a fusion association mean value, and determining the interaction degree after fusion between different scenes by using the fusion association mean value.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring association factors among different scenes, and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances;
comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes;
performing multi-dimensional information fusion on the multi-scene correlation degree according to the scene model to obtain a joint probability density function of three dimensional matrixes;
and calculating the interaction degree after fusion between different scenes through the joint probability density function, and acquiring the scene association degree according to the interaction degree.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
obtaining strategic traction degrees in different scenes according to the strategic mutual traction degrees in different scenes;
determining strategic resource allocation according to whether strategic resources of the same type are allocated among different scenes, wherein the strategic resource allocation is generally determined by task types in strategic directions;
determining whether the interest relationship is a union relationship or an enemy relationship according to the interest relationship among strategic adversaries in different scenes, obtaining the probability of simultaneous outbreaks in different scenes, and determining the strategic adversaries to ally.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
defining a correlation degree score set of the three correlation factors under different scenes, and performing multi-source information fusion through the correlation degree score set to obtain mathematical expectations of the three correlation factors;
defining variable Xab-t，Xab-r，Xab-kRespectively representing the strategic traction association degree, the strategic resource allocation association degree and the strategic opponent union association degree;
and calculating the covariance of the correlation degrees in three dimensions according to the mathematical expectation of the three correlation factors.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
obtaining likelihood functions under different scenes through normal distribution of the joint probability density function;
calculating covariance matrixes under different scenes according to the likelihood functions;
and verifying normal distribution of information fusion between different scenes by using the covariance matrix to obtain a fusion association mean value, and determining the interaction degree after fusion between different scenes by using the fusion association mean value.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for calculating a scene relevance, comprising the steps of:
acquiring association factors among different scenes, and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances;
comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes;
performing multi-dimensional information fusion on the multi-scene correlation degree according to the scene model to obtain a joint probability density function of three dimensional matrixes;
and calculating the interaction degree after fusion between different scenes through the joint probability density function, and acquiring the scene association degree according to the interaction degree.
2. The method for calculating the relevance of the scenes according to claim 1, wherein the obtaining of the relevance factors between different scenes and the dividing of the relevance factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliance comprises:
obtaining strategic traction degrees in different scenes according to the strategic mutual traction degrees in different scenes;
determining strategic resource allocation according to whether strategic resources of the same type are allocated among different scenes, wherein the strategic resource allocation is generally determined by task types in strategic directions;
determining whether the interest relationship is a union relationship or an enemy relationship according to the interest relationship among strategic adversaries in different scenes, obtaining the probability of simultaneous outbreaks in different scenes, and determining the strategic adversaries to ally.
3. The method for calculating the degree of relevance of a scene according to claim 1, wherein the step of establishing a multi-scene model of three dimensional matrices after comprehensively evaluating the three divided relevance factors comprises:
defining a correlation degree score set of the three correlation factors under different scenes, and performing multi-source information fusion through the correlation degree score set to obtain mathematical expectations of the three correlation factors;
defining variable Xab-t，Xab-r，Xab-kRespectively representing the strategic traction association degree, the strategic resource allocation association degree and the strategic opponent union association degree;
and calculating the covariance of the correlation degrees in three dimensions according to the mathematical expectation of the three correlation factors.
4. The method for calculating the degree of association of the scene according to claim 1, wherein the calculating the degree of interaction after the fusion between different scenes by the joint probability density function and obtaining the degree of association of the scene according to the degree of interaction comprises:
obtaining likelihood functions under different scenes through normal distribution of the joint probability density function;
calculating covariance matrixes under different scenes according to the likelihood functions;
and verifying normal distribution of information fusion between different scenes by using the covariance matrix to obtain a fusion association mean value, and determining the interaction degree after fusion between different scenes by using the fusion association mean value.
5. A scene relevancy calculation system, comprising:
the dimension division module is used for acquiring the association factors among different scenes and dividing the association factors into three dimensions of strategic traction, strategic resource allocation and strategic adversary alliances;
the model establishing module is used for comprehensively evaluating the three divided correlation factors and then establishing a multi-scene model of three dimensional matrixes;
the function calculation module is used for carrying out multi-dimensional information fusion on the multi-scene relevance according to the scene model to obtain a joint probability density function of three dimensional matrixes;
and the association degree acquisition module is used for calculating the interaction degree after fusion between different scenes through the joint probability density function and acquiring the scene association degree according to the interaction degree.
6. The system according to claim 5, wherein the dimension division module comprises an association factor determination unit configured to:
obtaining strategic traction degrees in different scenes according to the strategic mutual traction degrees in different scenes;
determining strategic resource allocation according to whether strategic resources of the same type are allocated among different scenes, wherein the strategic resource allocation is generally determined by task types in strategic directions;
determining whether the interest relationship is a union relationship or an enemy relationship according to the interest relationship among strategic adversaries in different scenes, obtaining the probability of simultaneous outbreaks in different scenes, and determining the strategic adversaries to ally.
7. The system according to claim 5, wherein the model building module comprises an association processing unit, and the association processing unit is configured to:
defining a correlation degree score set of the three correlation factors under different scenes, and performing multi-source information fusion through the correlation degree score set to obtain mathematical expectations of the three correlation factors;
defining variable Xab-t，Xab-r，Xab-kRespectively representing the strategic traction association degree, the strategic resource allocation association degree and the strategic opponent union association degree;
and calculating the covariance of the correlation degrees in three dimensions according to the mathematical expectation of the three correlation factors.
8. The system according to claim 5, wherein the correlation obtaining module includes a correlation mean calculating unit, and the correlation mean calculating unit is configured to:
obtaining likelihood functions under different scenes through normal distribution of the joint probability density function;
calculating covariance matrixes under different scenes according to the likelihood functions;
and verifying normal distribution of information fusion between different scenes by using the covariance matrix to obtain a fusion association mean value, and determining the interaction degree after fusion between different scenes by using the fusion association mean value.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 4 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 4.
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| CN114697209A (en) * | 2022-03-30 | 2022-07-01 | 广州穗华能源科技有限公司 | Cloud edge cooperative computing resource configuration method and configuration system |
| CN116304280A (en) * | 2023-05-15 | 2023-06-23 | 石家庄学院 | Multi-dimensional data analysis method based on interactive visualization |
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| CN116304280B (en) * | 2023-05-15 | 2023-08-04 | 石家庄学院 | Multi-dimensional data analysis method based on interactive visualization |
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