CN113204924A - Complex problem oriented evaluation analysis method and device and computer equipment - Google Patents
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Abstract
The application relates to a complex problem oriented exploratory evaluation analysis method, a complex problem oriented exploratory evaluation analysis device, computer equipment and a storage medium. The method comprises the following steps: the assessment factors such as assessment indexes, assessment object attributes and assessment models are established by obtaining the problems to be assessed of the weapon system. And performing evaluation to obtain an evaluation result, if the evaluation unknown entropy is larger than a preset threshold value, performing sensitive area analysis to obtain a sensitive area, reducing the attribute of the evaluation object and the uncertainty of the evaluation model by multiple means, and iterating the evaluation analysis again until the unknown entropy of the evaluation index output by the evaluation model is smaller than the threshold value or the unknown entropy cannot be reduced continuously, thereby completing the evaluation. The method provided by the invention realizes the analysis of the uncertainty of the evaluation result, and the factors with strong sensitivity and high uncertainty are found out for updating through the analysis of the sensitive area of the evaluation factors, so that the uncertainty of the evaluation factors is reduced in a targeted manner, and then the evaluation is iterated, thereby gradually improving the precision and quality of the evaluation.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for heuristic evaluation analysis for complex problems, a computer device, and a storage medium.
Background
The weapon system is a comprehensive system composed of weapon ammunition and various auxiliary devices of military aircraft and is used for killing and destroying various targets in the air, on the ground, on the water surface and under the water. The auxiliary devices include weapon mounting or suspension devices and various software and hardware devices for ensuring the use of weapon ammunition and target hitting.
The weapon system is evaluated by a specific evaluation analysis method, and the combat effectiveness of the weapon system is evaluated, but the problems of low system modeling accuracy and low evaluation efficiency exist in the prior art.
Disclosure of Invention
In view of the above, there is a need to provide a method, an apparatus, a computer device and a storage medium for evaluating and analyzing complex problems, which can improve accuracy and efficiency of evaluating and analyzing weapon systems.
A method for complex problem oriented assessment analysis, the method comprising:
the method comprises the steps of obtaining a problem to be evaluated of a weapon system, determining an evaluation target, determining an attribute of the evaluation target according to the evaluation target, and establishing an evaluation model with the attribute of the evaluation target as input and an evaluation index as output; evaluating the object attribute as a combat capability attribute of the weapon system;
solving the evaluation model to obtain an evaluation index value;
determining the unknown entropy of the evaluation index according to the evaluation index, when the unknown entropy is larger than a preset threshold value, performing sensitive area analysis on the attribute of the evaluation object and the evaluation model to obtain a sensitive area, and calculating the unknown entropy of the sensitive area; the sensitive area is an area with large influence effect of change on the output of the evaluation model;
and updating the evaluation object attribute with higher unknown entropy in the sensitive area and the evaluation model through the supplementary information data to reduce the unknown entropy of the output evaluation index until the unknown entropy of the evaluation index output by the evaluation model is smaller than a threshold value, ending iteration, outputting the evaluation index and finishing evaluation on the evaluation object.
In one embodiment, the method further comprises the following steps: and determining the unknown entropy of the evaluation index according to the evaluation index, wherein the calculation of the unknown entropy is based on a probability distribution function or a reliability distribution function.
In one embodiment, the method further comprises the following steps: when the calculation of the unknown entropy is based on a probability distribution function, determining probability information by a Bayesian method on the basis of expert information;
and when the calculation of the uncertain entropy is based on a reliability distribution function, determining reliability information in a multi-expert integrated interactive dialogue mode.
In one embodiment, the method further comprises the following steps: when the unknown entropy is described by the probability distribution function, the formula for evaluating the unknown entropy of the index is:
wherein S represents unknown entropy; n is the discretized summation upper limit; i is a summation count parameter; p is a radical ofiProbability for each part of discretization, pi＝F(yi+1)-F(yi) Y represents an evaluation index, and F (y) represents a probability distribution function;
when the uncertain entropy takes the reliability distribution function as a description form, the formula for evaluating the uncertain entropy of the index is as follows:
wherein S (y) represents an unknown entropy; p is a radical ofiFor each part of the discretization, pi＝F(yi+1)-F(yi) Y represents an evaluation index, and F (y) represents a reliability distribution function.
In one embodiment, the method further comprises the following steps: screening out insensitive attributes in the attributes of the evaluation objects by a branch and bound screening method;
through 2KAn experimental design and analysis method is used for solving the main effect and the associated effect of the residual attributes;
and according to the main effect and the correlation effect, forming attribute vector space by the attributes with stronger main effect and correlation, and acquiring the sensitive area in the attribute vector space by an area screening method.
In one embodiment, the method further comprises the following steps: when the unknown entropy takes a probability distribution function as a description form, sensitive region analysis is carried out on the attribute of the evaluation object and the evaluation model to obtain a sensitive region, and a formula for calculating the unknown entropy of the sensitive region is as follows:
wherein the attribute vector space is divided into N subregions, denoted Ai(i＝1···N)，A1···ALAs a sensitive area, AL···ANIs a non-sensitive region, L is an upper limit of the count of a sub-region of the sensitive region, and
when the uncertain entropy takes the reliability distribution function as a description form, sensitive region analysis is carried out on the attribute of the evaluation object and the evaluation model to obtain a sensitive region, and a formula for calculating the uncertain entropy of the sensitive region is as follows:
in one embodiment, the method further comprises the following steps: and updating the attribute of the evaluation object with higher unknown entropy and the evaluation model of the sensitive area by increasing the experimental strength and increasing the experimental scale to supplement information data.
An evaluation and analysis device for complex problems, the device comprising:
the evaluation model establishing module is used for acquiring the problems to be evaluated of the weapon system, determining an evaluation target, determining the attribute of the evaluation object according to the evaluation target, and establishing an evaluation model with the attribute of the evaluation object as input and the evaluation index as output; evaluating the object attribute as a combat effectiveness attribute of the weapon system;
the evaluation model solving module is used for solving the evaluation model to obtain an evaluation index;
the sensitive area acquisition module is used for determining the unknown entropy of the evaluation index according to the evaluation index, performing sensitive area analysis on the attribute of the evaluation object and the evaluation model to obtain a sensitive area when the unknown entropy is larger than a preset threshold value, and calculating the unknown entropy of the sensitive area; the sensitive area is an area with large influence effect of change on the output of the evaluation model;
and the iteration module is used for updating the evaluation object attribute with higher unknown entropy in the sensitive area and the evaluation model through the supplementary information data so as to reduce the unknown entropy of the output evaluation index until the unknown entropy of the evaluation index output by the evaluation model is smaller than a threshold value, ending iteration, outputting the evaluation index and finishing evaluation on the evaluation object.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
the method comprises the steps of obtaining a problem to be evaluated of a weapon system, determining an evaluation target, determining an attribute of the evaluation target according to the evaluation target, and establishing an evaluation model with the attribute of the evaluation target as input and an evaluation index as output; evaluating the object attribute as a combat effectiveness attribute of the weapon system;
solving the evaluation model to obtain an evaluation index;
determining the unknown entropy of the evaluation index according to the evaluation index, when the unknown entropy is larger than a preset threshold value, performing sensitive area analysis on the attribute of the evaluation object and the evaluation model to obtain a sensitive area, and calculating the unknown entropy of the sensitive area; the sensitive area is an area with large influence effect of change on the output of the evaluation model;
and updating the evaluation object attribute with higher unknown entropy in the sensitive area and the evaluation model through the supplementary information data to reduce the unknown entropy of the output evaluation index until the unknown entropy of the evaluation index output by the evaluation model is smaller than a threshold value, ending iteration, outputting the evaluation index and finishing evaluation on the evaluation object.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
the method comprises the steps of obtaining a problem to be evaluated of a weapon system, determining an evaluation target, determining an attribute of the evaluation target according to the evaluation target, and establishing an evaluation model with the attribute of the evaluation target as input and an evaluation index as output; evaluating the object attribute as a combat effectiveness attribute of the weapon system;
solving the evaluation model to obtain an evaluation index;
determining the unknown entropy of the evaluation index according to the evaluation index, when the unknown entropy is larger than a preset threshold value, performing sensitive area analysis on the attribute of the evaluation object and the evaluation model to obtain a sensitive area, and calculating the unknown entropy of the sensitive area; the sensitive area is an area with large influence effect of change on the output of the evaluation model;
and updating the evaluation object attribute with higher unknown entropy in the sensitive area and the evaluation model through the supplementary information data to reduce the unknown entropy of the output evaluation index until the unknown entropy of the evaluation index output by the evaluation model is smaller than a threshold value, ending iteration, outputting the evaluation index and finishing evaluation on the evaluation object.
The complex problem-oriented evaluation analysis method, the complex problem-oriented evaluation analysis device, the computer equipment and the storage medium establish evaluation elements such as an evaluation target, an evaluation index, an evaluation object attribute and an evaluation model by acquiring the problem to be evaluated of the weapon system. And then, carrying out evaluation to obtain an evaluation result. And analyzing the result, if the unknown entropy is larger than a preset threshold value, analyzing the attribute of the evaluation object and the sensitive area of the evaluation model to obtain the sensitive area, calculating the unknown entropy of the sensitive area, reducing the attribute of the evaluation object and the uncertainty of the evaluation model by various means, iterating the evaluation analysis again until the unknown entropy of the evaluation index output by the evaluation model is smaller than the threshold value, ending iteration, outputting the evaluation index, and finishing the evaluation of the evaluation object. The method provided by the invention realizes the analysis of the uncertainty of the evaluation result, and updates the factors with strong sensitivity and high uncertainty by analyzing the sensitive area of the evaluation factors, so as to reduce the uncertainty of the evaluation factors in a targeted manner, and then iterates by using the updated evaluation factors, thereby gradually improving the precision and quality of the evaluation and gradually improving the precision and quality of the evaluation.
Drawings
FIG. 1 is a schematic flow chart diagram of a method for evaluation and analysis of complex problems in one embodiment;
FIG. 2 is a schematic flow chart of a method for evaluation and analysis of complex problems in another embodiment;
FIG. 3 is a schematic diagram of an embodiment of an evaluation object attribute and an evaluation model for a counter-aircraft carrier combat system;
FIG. 4 is a diagram illustrating a belief distribution function of attack times K in one embodiment;
FIG. 5 is a diagram illustrating a belief distribution function of the single attack damage rate Ps in one embodiment;
FIG. 6 shows the submarine damage ratio P in one embodiment1A reliability distribution function diagram of (1);
FIG. 7 is a schematic illustration of a simulation model of a ballistic missile attacking aircraft carrier in one embodiment;
FIG. 8 shows the hit rate P of a ballistic missile in one embodimentkA reliability distribution function diagram of (1);
FIG. 9 is a graphical illustration of a confidence distribution function of the ratio R of the radius of ballistic missile killing to the target location radius CEP in one embodiment;
FIG. 10 shows the ballistic missile damage rate P in one embodiment2Reliability distribution ofA functional diagram;
FIG. 11 shows the damage rate P of the aircraft missile in one embodiment3A reliability distribution function diagram of (1);
FIG. 12 is a diagram illustrating a reliability distribution function of the overall damage rate P of the anti-aircraft carrier combat system in one embodiment;
FIG. 13 is a diagram illustrating a sensitive area displaying a ratio R of a killing radius to a target location radius CEP through human-computer interaction in one embodiment;
FIG. 14 is a graphical illustration of the confidence distribution function of the adjusted CEP ratio R of the killer radius to the target localization radius in one embodiment;
FIG. 15 shows the adjusted ballistic missile damage rate P in one embodiment2A reliability distribution function diagram of (1);
FIG. 16 shows the adjusted ballistic missile damage rate P in one embodiment1A reliability distribution function diagram of (1);
FIG. 17 is a diagram illustrating a belief distribution function of the adjusted ballistic missile damage rate P in one embodiment;
FIG. 18 is a block diagram of an apparatus for evaluating and analyzing complex problems according to an embodiment;
FIG. 19 is a diagram showing 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.
The evaluation and analysis method for complex problems can be applied to the following application environments. The terminal executes a complex problem-oriented evaluation analysis method. The assessment target, assessment index, assessment object attribute, assessment model and other assessment elements are established by obtaining the to-be-assessed problems of the weapon system. And then, carrying out evaluation to obtain an evaluation result. And analyzing the result, if the unknown entropy is larger than a preset threshold value, analyzing the attribute of the evaluation object and the sensitive area of the evaluation model to obtain the sensitive area, calculating the unknown entropy of the sensitive area, reducing the attribute of the evaluation object and the uncertainty of the evaluation model by various means, iterating the evaluation analysis again until the unknown entropy of the evaluation index output by the evaluation model is smaller than the threshold value or the unknown entropy cannot be reduced continuously, ending iteration, outputting the evaluation index, and finishing the evaluation of the evaluation object. The terminal may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers.
In one embodiment, as shown in fig. 1, there is provided a complex problem oriented evaluation analysis method, comprising the steps of:
The weapon system is a comprehensive system formed by weapon ammunition and various auxiliary devices of military aircraft and is used for killing and destroying various targets in the air, on the ground, on the water surface and under the water. The auxiliary devices include weapon mounting or suspension devices and various software and hardware devices for ensuring the use of weapon ammunition and target hitting.
The goal of the evaluation is why the evaluation is to be performed, e.g., is the optimal solution selected? Is a satisfactory solution to meet demand selected? Rank the assessment objects? Is the evaluation subject examined for achievement of the respective objective? Monitoring the evaluation object, and the like.
The evaluation index is a series of quantitative scales defined according to the evaluation target, and the final result of the evaluation is the solution analysis of the evaluation index. The evaluation target can only be achieved after the result of the evaluation index is obtained. The evaluation index can be divided into the following types according to the numerical value: existence type, grade type, equidistant type and geometric type. The mathematical form of data can be divided into: scalar, vector and tensor. In addition, the direction from the target requirement can be divided into: benefit type, cost type and fixed type.
The evaluation object attribute is the combat effectiveness attribute of the weapon system and is closely related to the evaluation result. The evaluation object attribute is a precondition for evaluation in the evaluation process, and the evaluation object attribute may be described in a definite or unknown form, such as a probability and reliability distribution function.
The evaluation model refers to the association mapping relationship between the attributes of the evaluation objects (including the intermediate attributes), between the evaluation indexes (including the intermediate indexes), and between the attributes and the indexes. Such models are generally classified into the following categories: theoretical analysis calculation method, simulation experiment method, physical data test method and subjective judgment estimation method.
The evaluation target, the evaluation index, the evaluation object and the evaluation model all belong to evaluation elements, and integrally form an evaluation framework for evaluating the weapon system, and the evaluation model corresponding to the core elements of the evaluation framework can be represented by a functional form:
Y＝f(X)+ε
where Y represents an evaluation index vector (single or multiple indexes), and X represents an evaluation object attribute vector, the description form of which is usually described by a probability analysis and a reliability distribution function. f is an evaluation model cluster, which is a composite of multiple mapping models, typically
f is implemented as before. ε represents the unknown vector value.
And 104, solving the evaluation model to obtain an evaluation index.
Since both the attribute of the evaluation object and the evaluation model have uncertainty, the description form of the evaluation index also has uncertainty, and is described by the form of a probability or a reliability distribution function. In the field of uncertainty, probability distribution generally refers to subjective probability, which is a probability distribution of a subject in imagination of an unknown individual, and although the object has no randomness, the problem is evaluated and solved by introducing the subjective probability in imagination through the randomness in the imagination of the subject. Confidence distributions refer to the intensity of belief of a subject for some type of judgment of an object. Which is different from the randomness in imagination and can be understood as the degree of certainty for a certain judgment, like a kind of "strength". The probability and the reliability are modeling modes of different visual angles of the uncertainty of the subject to the object, and can be selected and used according to the characteristics of the subject and the object in practical application.
And 106, determining the unknown entropy of the evaluation index according to the evaluation index, when the unknown entropy is larger than a preset threshold value, performing sensitive area analysis on the attribute of the evaluation object and the evaluation model to obtain a sensitive area, and calculating the unknown entropy of the sensitive area.
The sensitive region is a region where the influence of the change on the output of the evaluation model is large.
If the uncertainty of the solved evaluation index is high, the evaluation result is not ideal, and the value of the evaluation result is not large. Such that subsequent need to reduce the uncertainty through a variety of means. For the uncertainty of the evaluation index, the method is characterized by using the uncertain entropy, and the uncertain entropy can be defined according to two modes of probability and reliability.
And 108, updating the evaluation object attribute with higher unknown entropy in the sensitive area and the evaluation model through the supplementary information data to reduce the unknown entropy of the output evaluation index until the unknown entropy of the evaluation index output by the evaluation model is smaller than a threshold value or the unknown entropy cannot be reduced continuously, ending iteration, outputting the evaluation index, and finishing the evaluation of the evaluation object.
For sensitive areas with high uncertainty, various means such as deep test investigation and valuable information introduction should be adopted to reduce the uncertainty. And then re-evaluating, and gradually improving the evaluation quality.
In the complex problem-oriented evaluation analysis method, evaluation elements such as an evaluation target, an evaluation index, an evaluation object attribute and an evaluation model are determined by acquiring the problem to be evaluated of the weapon system. And then, carrying out evaluation to obtain an evaluation result. And analyzing the result, if the unknown entropy is larger than a preset threshold value, analyzing the attribute of the evaluation object and the sensitive area of the evaluation model to obtain the sensitive area, calculating the unknown entropy of the sensitive area, reducing the attribute of the evaluation object and the uncertainty of the evaluation model by various means, iterating the evaluation analysis again until the unknown entropy of the evaluation index output by the evaluation model is smaller than the threshold value or the unknown entropy cannot be reduced continuously, ending iteration, outputting the evaluation index, and finishing the evaluation of the evaluation object. The method provided by the invention realizes the analysis of the uncertainty of the evaluation result, and the factors with strong sensitivity and high uncertainty are found out and updated through the analysis of the sensitive area of the evaluation factors, so that the uncertainty of the evaluation factors is reduced in a targeted manner, and the updated evaluation factors are used for iteration, thereby gradually improving the precision and quality of the evaluation.
In one embodiment, the method further comprises the following steps: when the unknown entropy is described by the probability distribution function, the formula for evaluating the unknown entropy of the index is:
wherein S represents unknown entropy; n is the discretized summation upper limit; i is a summation count parameter; p is a radical ofiProbability for each part of discretization, pi＝F(yi+1)-F(yi) Y represents an evaluation index, and F (y) represents a probability distribution function;
when the uncertain entropy takes the reliability distribution function as a description form, the formula for evaluating the uncertain entropy of the index is as follows:
wherein S (y) represents an unknown entropy; p is a radical ofiFor each part of the discretization, pi＝F(yi+1)-F(yi) Y represents an evaluation index, and F (y) represents a reliability distribution function.
In the field of uncertainty, probability distribution is usually referred to as subjective probability, and is a probability distribution in an imagination of an unknown individual by a subject, and although the object has no randomness, the problem is evaluated and solved by introducing the subjective probability in the imagination through the randomness in the imagination of the subject. Confidence distributions refer to the intensity of belief of a subject for some type of judgment of an object. Which is different from the randomness in imagination and can be understood as the degree of certainty for a certain judgment, like a kind of "strength". The probability and the reliability are modeling modes of different visual angles of the uncertainty of the subject to the object, and can be selected and used according to the characteristics of the subject and the object in practical application. The subjective probability is usually obtained by continuously correcting and applying a Bayes method on the basis of expert judgment. The credibility is obtained by adopting a multi-expert integrated interactive dialogue mode, and the specific obtaining mode can be shown in the contents disclosed in the Explorer evaluation and demonstration method.
In one embodiment, the method further comprises the following steps: screening out insensitive attributes in the attributes of the evaluation objects by a branch and bound screening method; through 2KAn experimental design and analysis method is used for solving the main effect and the associated effect of the residual attributes; and according to the main effect and the correlation effect, forming attribute vector space by the attributes with stronger main effect and correlation, and acquiring the sensitive area in the attribute vector space by an area screening method.
The threshold for determination of the sensitive zones is determined by the evaluating person, for example, by the usual 2-8 principle, i.e. an area which is only 20% in space but has an effect of 80%. That is, the ratio of relative effect to relative area space is greater than 4. Screening out insensitive attributes in the attributes of the evaluation objects by a branch and bound screening method; through 2KAn experimental design and analysis method is used for solving the main effect and the associated effect of the residual attributes; and according to the main effect and the correlation effect, forming attribute vector space by the attributes with stronger main effect and correlation, and acquiring the sensitive area in the attribute vector space by an area screening method. The specific algorithm can be seen in the contents disclosed in the book "combat simulation experiment design and analysis". In practical problems, a human-computer interaction approximate analysis method based on multi-dimensional visualization can be flexibly adopted to obtain the sensitive area.
In one embodiment, the method further comprises the following steps: when the unknown entropy takes a probability distribution function as a description form, sensitive region analysis is carried out on the attribute of the evaluation object and the evaluation model to obtain a sensitive region, and a formula for calculating the unknown entropy of the sensitive region is as follows:
wherein the attribute vector space is divided into N subregions, denoted Ai(i＝1···N)，A1···ALAs a sensitive area, AL···ANIs a non-sensitive region, L is an upper limit of the count of a sub-region of the sensitive region, and
when the uncertain entropy takes the reliability distribution function as a description form, sensitive region analysis is carried out on the attribute of the evaluation object and the evaluation model to obtain a sensitive region, and a formula for calculating the uncertain entropy of the sensitive region is as follows:
in one embodiment, the method further comprises the following steps: by increasing the experiment intensity and increasing the experiment scale to supplement information data, the attributes and the evaluation models of the evaluation objects with high unknown entropies in the sensitive areas are updated, and the uncertainty is reduced.
By increasing the experimental strength and the experimental scale to obtain more information data and updating the attribute of the evaluation object and the evaluation model with higher unknown entropy in the sensitive area, the method realizes the purpose of pertinently reducing the uncertainty of the attribute of the corresponding evaluation object and the evaluation model, and can improve the evaluation quality of the updated evaluation model.
It should be understood that, although the steps in the flowchart of fig. 1 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 fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 2, a method for evaluating and analyzing complex problems is provided, which includes:
s1, constructing an evaluation framework of a weapon system, and establishing elements such as an evaluation target, an evaluation criterion, an evaluation object and attribute, an evaluation model and the like;
s2, performing evaluation and solving an evaluation model;
s3, judging whether the quality of the evaluation result meets the requirement or not based on the uncertain entropy interaction;
s4, when the evaluation result does not meet the requirement, analyzing the elements in the evaluation frame to find out the key elements (data and models) with stronger sensitivity;
s5, correcting and calculating the unknown entropy of the attribute according to the acquired sensitive area;
s6, according to S5, finding out elements with larger unknown entropy, and reducing the uncertainty of the elements by various means;
s7, judging whether the used means can reduce the uncertainty, if so, returning to S2, re-evaluating, and if not, ending the iteration and finishing the evaluation of the weapon system.
In another embodiment, the counter-aircraft carrier combat system is used as an evaluation object for evaluation, and as shown in fig. 3, the counter-aircraft carrier has three main means: the submarine attack, the ballistic missile attack and the aircraft missile attack are carried out by launching an aircraft missile. The whole anti-aircraft carrier combat system mainly comprises the three main means. In the example, three major means are simultaneously applied, and finally the combat effectiveness of the anti-aircraft carrier system is comprehensively solved. The method comprises the following steps:
s1, constructing an assessment framework:
evaluation target: and solving the efficiency of the anti-aircraft carrier combat system, and taking the probability of the combat efficiency that the aircraft carrier loses the combat capability as an evaluation index.
Evaluation object attributes: attributes for determining submarine damage rate include: the attack times of the submarine and the success rate of single attack; attributes for determining ballistic missile damage rate include: the ratio of the killing radius to the target positioning radius CEP, the emission quantity and the hit rate; attributes for determining damage rate of aircraft-based missiles include: missile performance data (RCS, speed, range), number of missiles that can be launched, damage capability, etc.
And (3) evaluating the model: namely, solving the model of the final combat performance. The following were used:
P＝1-(1-P1)(1-P2)(1-P3)
P1＝1-(1-Ps)K
P2＝f2(R,BN,Pk)
P3＝P(KN≥RN)
KN＝f3(ML,MV,RCS,LN)
wherein, P is the overall damage rate; p1The submarine damage rate; p2Ballistic missile damage rate; p3The damage rate of the aircraft-warship missile; ps is the single attack damage rate; k is the attack frequency; r is the ratio of the killing radius to the target positioning radius CEP; BN is the number of shots; pk is the hit rate of the ballistic missile; KN is the effective hit number of the air-borne missile; RN is the number of missiles required for damage of the aircraft-borne missiles; ML is the flight range of the aircraft missile; MV is the speed of the air-borne missile; RCS is radar reflection area; LN is the number of missiles launched by the aircraft; f. of2Solving the model for the simulation of ballistic missile hitting aircraft carrier, f3And (3) a model is solved for the penetration quantity simulation of the aircraft-based missile. The overall operational efficiency of the anti-aircraft carrier operational system can be solved through the calculation of the evaluation model. In practical problems, it is often difficult to obtain accurate attribute data, and therefore, each attribute is represented by a reliability distribution in the present model. And obtaining the reliability distribution function by adopting a multi-expert integrated judgment mode.
S2, executing evaluation, solving evaluation indexes:
s2.1. solving for P1The belief distribution function of K is shown in FIG. 4, the belief distribution function of Ps is shown in FIG. 5, and the model P1The belief distribution function of (2) is shown in FIG. 6, according to the formula
Setting N to 5 to obtain P1Has an unknown entropy of 1.08.
S2.2. solving for P2For P2The agent model is constructed and analyzed by adopting a simulation experiment method, and the experimental results are shown in table 1 (the emission quantity is set as a constant, and BN is 20):
table 1:
the data are extracted from the military force comparison report of the Lande company and equivalent transformation is carried out. Generating a corresponding proxy model f using neural network regression2As in fig. 7. The reliability distribution function of PK, the reliability distribution function of R, and the reliability distribution function of P2 are shown in fig. 8, 9, and 10, respectively. Setting N to 5 to obtain P2Has an unknown entropy of 1.03.
S2.3. solving for P3，P3The reliability distribution function of (2) is shown in fig. 11. Setting N to 5 to obtain P3Has an unknown entropy of 0.56.
S2.4, solving P, wherein the credibility distribution function of P is shown in figure 12, and the uncertain entropy of P obtained through calculation is 1.1. The evaluation uncertainty is larger in intuition and in the uncertainty entropy, the uncertainty should be reduced by various means, and the evaluation analysis needs to be continued iteratively.
S3, sensitivity analysis is carried out, and key unknown attributes and value intervals thereof are found:
through the calculation of the previous step, the uncertainty of P is mainly derived from P1，P2. In this example, the sensitive region can be obtained more clearly through human-computer interaction. Through analysis, R is found to be [0,1 ]]The interval sensitivity was the greatest as shown in fig. 13. The uncertainty of R is reduced by various means (e.g., increasing the test intensity, increasing the test scale, etc.), as shown in fig. 14. Adjusted P2The confidence distribution function of (a) is shown in figure 15,visible adjusted P2The reliability distribution of (2) is greatly improved.
In the same way, P after adjustment1The reliability distribution function of (2) is shown in fig. 16. The final confidence distribution of P after adjustment is shown in fig. 17, and the calculated unknown entropy is 0.9, which is reduced compared to 1.1 before adjustment, and if it is greater than the preset threshold, iteration can be continued to reduce its uncertainty and improve the evaluation quality until the evaluation quality is satisfactory or its uncertainty cannot be reduced any more.
In one embodiment, as shown in fig. 18, there is provided a complex problem oriented evaluation analysis apparatus including: an evaluation model establishing module 1802, an evaluation model solving module 1804, a sensitive region obtaining module 1806 and an iteration module 1808, wherein:
the evaluation model establishing module 1802 is used for acquiring a problem to be evaluated of the weapon system, determining an evaluation target, determining an evaluation object attribute according to the evaluation target, and establishing an evaluation model with the evaluation object attribute as input and an evaluation index as output; evaluating the object attribute as a combat effectiveness attribute of the weapon system;
an evaluation model solving module 1804, configured to solve the evaluation model to obtain an evaluation index;
a sensitive region obtaining module 1806, configured to determine an unknown entropy of the evaluation index according to the evaluation index, and when the unknown entropy is greater than a preset threshold, perform sensitive region analysis on the evaluation object attribute and the evaluation model to obtain a sensitive region, and calculate an unknown entropy of the sensitive region; the sensitive area is an area with large influence effect of change on the output of the evaluation model;
an iteration module 1808, configured to update the attribute of the evaluation object and the evaluation model with higher unknown entropy in the sensitive region through the supplemental information data, so as to reduce the unknown entropy of the output evaluation index until the unknown entropy of the evaluation index output by the evaluation model is smaller than a threshold or the unknown entropy cannot be reduced continuously, end iteration, output the evaluation index, and complete evaluation on the evaluation object.
The sensitive region obtaining module 1806 is further configured to determine an unknown entropy of the evaluation index according to the evaluation index, where the calculation of the unknown entropy is based on a probability distribution function or a reliability distribution function.
The sensitive region obtaining module 1806 is further configured to, when the unknown entropy takes a probability distribution function as a description form, determine the unknown entropy of the evaluation index by using a bayesian method on the basis of the expert information; when the uncertain entropy takes the reliability distribution function as a description form, the uncertain entropy of the evaluation index is determined in a multi-expert integrated interactive dialogue mode.
The sensitive region obtaining module 1806 is further configured to, when the unknown entropy is described by the probability distribution function, evaluate the unknown entropy of the index by the following formula:
wherein S represents unknown entropy; n is the discretized summation upper limit; i is a summation count parameter; p is a radical ofiProbability for each part of discretization, pi＝F(yi+1)-F(yi) Y represents an evaluation index, and F (y) represents a probability distribution function;
when the uncertain entropy takes the reliability distribution function as a description form, the formula for evaluating the uncertain entropy of the index is as follows:
wherein S (y) represents an unknown entropy; p is a radical ofiFor each part of the discretization, pi＝F(yi+1)-F(yi) Y represents an evaluation index, and F (y) represents a reliability distribution function.
The sensitive region obtaining module 1806 is further configured to screen out an insensitive attribute in the attributes of the evaluation object by using a branch-and-bound screening method; through 2KAn experimental design and analysis method is used for solving the main effect and the associated effect of the residual attributes; and according to the main effect and the correlation effect, forming attribute vector space by the attributes with stronger main effect and correlation, and acquiring the sensitive area in the attribute vector space by an area screening method.
The sensitive region obtaining module 1806 is further configured to, when the unknown entropy takes the probability distribution function as a description form, perform sensitive region analysis on the attribute of the evaluation object and the evaluation model to obtain a sensitive region, and calculate a formula of the unknown entropy of the sensitive region as follows:
wherein the attribute vector space is divided into N subregions, denoted Ai(i＝1···N)，A1···ALAs a sensitive area, AL···ANIs a non-sensitive region, L is an upper limit of the count of a sub-region of the sensitive region, and
when the uncertain entropy takes the reliability distribution function as a description form, sensitive region analysis is carried out on the attribute of the evaluation object and the evaluation model to obtain a sensitive region, and a formula for calculating the uncertain entropy of the sensitive region is as follows:
the iteration module 1808 is further configured to update the attribute of the evaluation object and the evaluation model with unknown high entropy in the sensitive region by increasing the experimental strength and increasing the experimental scale to supplement the information data.
For specific limitations of the evaluation and analysis device for complex problems, reference may be made to the above limitations of the evaluation and analysis method for complex problems, which are not described herein again. The modules in the complex problem-oriented evaluation and analysis device can 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.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 19. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of evaluation analysis oriented to complex problems. 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. 19 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 an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
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. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
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 complex problem-oriented exploratory assessment analysis method, comprising:
the method comprises the steps of obtaining a problem to be evaluated of a weapon system, determining an evaluation target, determining an attribute of the evaluation target according to the evaluation target, and establishing an evaluation model with the attribute of the evaluation target as input and an evaluation index as output; the evaluation object attribute is a combat effectiveness attribute of the weapon system;
solving the evaluation model to obtain the evaluation index;
determining an unknown entropy of the evaluation index according to the evaluation index, when the unknown entropy is larger than a preset threshold value, performing sensitive area analysis on the evaluation object attribute and the evaluation model to obtain a sensitive area, and calculating the unknown entropy of the sensitive area; the sensitive area is an area with large influence effect of change on the output of the evaluation model;
and updating the evaluation object attribute with higher unknown entropy of the sensitive area and the evaluation model through supplementary information data to reduce the output unknown entropy of the evaluation index until the unknown entropy of the evaluation index output by the evaluation model is smaller than the threshold or the unknown entropy cannot be reduced continuously, ending iteration, outputting the evaluation index, and finishing the evaluation of the evaluation object.
2. The method of claim 1, wherein determining an unknown entropy of the evaluation metric based on the evaluation metric comprises:
and determining the unknown entropy of the evaluation index according to the evaluation index, wherein the calculation of the unknown entropy is based on a probability distribution function or a reliability distribution function.
3. The method of claim 2, wherein determining an unknown entropy of the evaluation metric based on the evaluation metric comprises:
when the calculation of the unknown entropy is based on a probability distribution function, determining probability information by a Bayesian method on the basis of expert information;
and when the calculation of the uncertain entropy is based on a reliability distribution function, determining reliability information in a multi-expert integrated interactive dialogue mode.
4. The method of claim 3, wherein determining an unknown entropy of the evaluation index based on the evaluation index comprises:
when the unknown entropy is described by a probability distribution function, the formula of the unknown entropy of the evaluation index is as follows:
wherein S represents unknown entropy; n is the discretized summation upper limit; i is a summation count parameter; p is a radical ofiProbability for each part of discretization, pi＝F(yi+1)-F(yi) Y represents the evaluation index, and F (y) represents a probability distribution function;
when the uncertain entropy takes a reliability distribution function as a description form, the formula of the uncertain entropy of the evaluation index is as follows:
wherein S (y) represents an unknown entropy; p is a radical ofiFor each part of the discretization, pi＝F(yi+1)-F(yi) Y represents the evaluation index, and F (y) represents a reliability distribution function.
5. The method of claim 4, wherein performing a sensitivity region analysis on the evaluation object attribute and the evaluation model to obtain a sensitivity region comprises:
screening out insensitive attributes in the attributes of the evaluation objects by a branch and bound screening method;
through 2KAn experimental design and analysis method is used for solving the main effect and the associated effect of the residual attributes;
and according to the main effect and the correlation effect, forming attributes with strong main effect and correlation into an attribute vector space, and obtaining a sensitive area in the attribute vector space by an area screening method.
6. The method of claim 5, wherein performing a sensitive region analysis on the evaluation object attribute and the evaluation model to obtain a sensitive region, and calculating an unknown entropy of the sensitive region comprises:
when the unknown entropy takes a probability distribution function as a description form, performing sensitive region analysis on the attribute of the evaluation object and the evaluation model to obtain a sensitive region, and calculating the unknown entropy of the sensitive region according to a formula:
wherein the attribute vector space is divided into N subregions, denoted Ai(i＝1···N)，A1···ALAs the sensitive region, AL···ANIs a non-sensitive region, L is the upper limit of the count of the sub-region of the sensitive region, and
when the unknown entropy takes a reliability distribution function as a description form, performing sensitive region analysis on the evaluation object attribute and the evaluation model to obtain a sensitive region, and calculating the unknown entropy of the sensitive region according to a formula:
7. the method according to claim 6, wherein the updating of the evaluation object attribute with higher unknown entropy of the sensitive area and the evaluation model by the supplemental information data comprises:
and updating the attributes of the evaluation objects with higher unknown entropies of the sensitive areas and the evaluation model by increasing the experimental strength and increasing the experimental scale to supplement information data.
8. A method and a device for exploratory evaluation and analysis of complex problems are characterized in that the device comprises:
the evaluation model establishing module is used for acquiring the problems to be evaluated of the weapon system, determining an evaluation target, determining the attribute of an evaluation object according to the evaluation target, and establishing an evaluation model with the attribute of the evaluation object as input and the evaluation index as output; the evaluation object attribute is a combat effectiveness attribute of the weapon system;
the evaluation model solving module is used for solving the evaluation model to obtain the evaluation index;
a sensitive region acquisition module, configured to determine an unknown entropy of the evaluation index according to the evaluation index, perform sensitive region analysis on the evaluation object attribute and the evaluation model to obtain a sensitive region when the unknown entropy is greater than a preset threshold, and calculate an unknown entropy of the sensitive region; the sensitive area is an area with large influence effect of change on the output of the evaluation model;
and the iteration module is used for updating the evaluation object attribute with higher unknown entropy of the sensitive area and the evaluation model through supplementary information data so as to reduce the output unknown entropy of the evaluation index until the unknown entropy of the evaluation index output by the evaluation model is smaller than the threshold or the unknown entropy cannot be reduced continuously, ending iteration, outputting the evaluation index and finishing the evaluation of the evaluation object.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
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 7.
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