CN117350163A - Underground shielding space communication sensing equipment efficiency evaluation method - Google Patents
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Abstract
The invention discloses an efficiency evaluation method of underground shielding space communication sensing equipment, and relates to the technical field of efficiency evaluation of communication sensing equipment. The method specifically comprises the following steps: s1: acquiring underground shielding space disaster rescue requirements and underground shielding space communication sensing equipment requirements; s2: establishing a four-layer underground shielding space communication perception equipment efficiency evaluation index system by using an analytic hierarchy process, and calculating the weight of a comprehensive evaluation index of the communication perception equipment; s3: evaluating the equipment efficiency evaluation index system by adopting a multi-stage fuzzy comprehensive evaluation method to obtain a fuzzy comprehensive judgment result of the equipment system; s4: and analyzing and judging whether the overall equipment efficiency meets the design requirement according to the fuzzy comprehensive judgment result of the equipment system. According to the invention, three methods, namely an analytic hierarchy process, a particle swarm optimization algorithm and a fuzzy comprehensive evaluation method, are selected to be combined to test whether the performance of the equipment meets the design requirement, and whether the actual use value meets the standard or not.
Description
Technical Field
The invention belongs to the technical field of efficiency evaluation of communication sensing equipment, and particularly relates to an underground shielding space communication sensing equipment efficiency evaluation method.
Background
(1) Current state of domestic research
The national equipment efficiency evaluation starts relatively late, and after 1975, the evaluation analysis of the equipment efficiency in China starts, and is mainly focused on the efficiency evaluation of the weapon equipment. In the evaluation direction of the operational efficiency of the equipment system, analysis and research are developed, and on the evaluation method, a plurality of domestic units introduce, discuss and apply some efficiency evaluation methods according to the actual situation of the domestic units, and the methods such as a hierarchical analysis method, a fuzzy comprehensive evaluation method, a gray fuzzy evaluation method, an ADC evaluation method and the like are frequently used. The application of the method plays an important role in the efficiency evaluation development of equipment systems in China.
1) Main application field
Li Hao combines the advantages of the AHP method and the fuzzy comprehensive evaluation method, determines the index weight by the AHP method, and evaluates the index weight step by using the fuzzy comprehensive evaluation method to obtain the comprehensive combat effectiveness of the aircraft carrier formation. Gu Hui and the like provide an index decomposition and determination method for the ground-to-air missile weapon system after the ADC efficiency evaluation model is established, and provide good reference for the subsequent research on the ground-to-air missile weapon system efficiency. Yang Dongbo A very classical performance evaluation method is provided by determining the comprehensive capability of missile weapons based on an ADC model by adopting an AHP-fuzzy comprehensive evaluation method. Wu Jun and the like combine the comprehensive utilization of utility functions and triangle fuzzy number theory to provide a new weapon system efficiency evaluation method. Zhang Jinping and the like, and an expert system is combined to provide an evaluation algorithm for the health state of the power equipment aiming at on-line monitoring data, preventive test data and historical operation condition data. Han Shilian and the like discuss and solve the problem of global logistics and supply optimization selectivity from different standard systems such as cost, quality, service, risk and the like by adopting a fuzzy analytic hierarchy process, and all obtain good application effects. Tang Xin et al construct scoring rules for uniformly distributing evaluation indexes and combine the gray system theory and the Bayesian network theory to give a damage model under specific probability of weapons. Chen Lei et al build a performance index system with a hierarchical structure by decomposing and polymerizing the course of a certain type of bomber operation. Fang Ying and the like use an entropy weight-gray correlation method to evaluate the construction safety risk of the real estate project, objectively and truly reflect the construction safety risk in the real estate project, and provide corresponding measures. Li Tedeng proposes an evaluation method based on the combination of entropy weight gray correlation and D-S evidence theory, and better processes uncertainty information of a target.
2) Main evaluation method
The fuzzy comprehensive evaluation is firstly proposed by a scholars Wang Peizhuang in China, and the basic principle is as follows: firstly, determining a factor (index) set and an evaluation (grade) set of an object to be evaluated, and weights of all factors and membership vectors of the factors to obtain a fuzzy evaluation matrix; and performing fuzzy operation and normalization on the fuzzy evaluation matrix and the weight vector of the factor to obtain a fuzzy evaluation comprehensive result. The well-known scholars Deng Julong in China teach the concept of a gray system for the first time in 1982, and mainly study the problems of 'small sample, poor information and uncertainty'. The gray comprehensive evaluation method is constructed based on the mathematical principle of gray clustering and comprises the following general steps: formulating an evaluation index, determining an index weight, scoring by an expert, solving a matrix of an evaluation sample, determining an evaluation gray class, calculating a gray correlation coefficient, calculating a weight vector, weighting the matrix, integrating an upper index, integrating an evaluation target and giving an evaluation value. The gray comprehensive evaluation is characterized in that: the analysis thought is clear, the required data is less, the calculation method is simple, and the comprehensive evaluation error is small.
An exponential method is a quantization method in which a characteristic value of an analysis object is represented by a relative numerical value. The exponential method is a precondition for evaluating the weapon combat efficacy, and must be used on the basis of the design theory, self attribute and usage attribute of the weapon system, such as the related index data for defining the single efficacy evaluation of the weapon system. The exponential method can avoid the influence of a large number of uncertain factors, and the algorithm is clear. The greatest difficulty is that the coefficients in the exponential method are not easy to determine, and the evaluation results have large differences. It is often necessary to assist in obtaining coefficients by expert evaluation methods such as analytic hierarchy process.
(2) Current state of research abroad
The research work of the foreign traditional military strong state on the efficiency of the weaponry is early, and the efficiency evaluation of the weaponry by some countries and organizations is not interrupted, such as the United states and the Soviet Union, as early as the beginning of the 20 th century to the beginning of the 21 st century, and the research work extends from the efficiency evaluation to the fight efficiency evaluation and the fight process evaluation. England scientists created the battle model of the Lanchester equation, and the United states and the Soviet Union have also studied and developed weaponry performance evaluations.
The analysis and evaluation method of the combat effectiveness of the two military countries of the United states and the Soviet Union form a relatively complete theoretical system, and two main categories are adopted according to the division standard of subjective and objective and experience theory: the method is a semi-subjective and objective research method, namely a method combining experience and theory, such as an empirical formula analysis method, an expert evaluation method and the like; the other category is strict objective and theoretical methods, and mainly depends on objective experimental data, such as combat simulation method, probability statistics method and the like.
At present, research on weapon efficiency evaluation in foreign countries is becoming finer and more important to application, and as time goes on and corresponding theoretical research advances, the efficiency evaluation theory of countries around the world is advancing towards more science and more intelligent along with the development of weapon equipment. The efficiency evaluation is more focused on the combination of a person and a computer, and various combat environments and combat tasks are simulated by the computer so as to obtain more scientific and accurate efficiency evaluation results.
1) Main application field
The Nazaninazizi-an utilizes the SWOT (situation analysis method) method to deeply comment and analyze various quantitative and qualitative evaluation models such as TRL and the like, considers that the evaluation of the maturity of the application technology is very critical, and can comprehensively examine the technical level and project risk. Kagazyo is based on technical indexes, and also based on indexes such as resources, society and the like, and the selection of an energy scheme is thoroughly evaluated by adopting a hierarchical analysis method. Bevilacqua et al used analytic hierarchy process for the selection of a very important refinery maintenance protocol in Italy and employed a sensitivity analysis to increase the efficiency of the AHP process. Ishibuchi sees the solution decision as a classification problem and uses a neural network as a classification system, while various techniques for soft decision making therewith are being explored. Molina considers an artificial neural network as an efficient and potential method in scheme evaluation, and applies the artificial neural network to performance evaluation of a bridge system. Tamura et al combine analytic hierarchy process with neural network, propose neural analytic hierarchy process (neurolAHP), and demonstrate superior performance to traditional analytic hierarchy process in multi-objective evaluation of flexible manufacturing systems. Grzesik et al analyzed the impact of fuzzy expert system membership function shape on the selected aircrew efficiency evaluation results, answering the question of how to select the best membership function in a fuzzy inference system for evaluating the efficiency of the air mission. Doming et al designed a Pareto-based archive particle swarm optimization algorithm that combined global optimal location selection with congestion measure-based archive maintenance. Mohandes trains an artificial neural network by using a particle swarm algorithm, and estimates the average global solar radiation amount per month per day by using a trained artificial neural network model so as to fully utilize solar radiation, which is a very important renewable resource.
2) Main evaluation method
The Analytic Hierarchy Process (AHP) is a systematic and hierarchical decision analysis method combining qualitative and quantitative analysis, which is proposed by a famous operation student T.L.Satty in the 20 th century in the 70 th year, and is a decision method for decomposing relevant factors of a decision problem into layers of targets, criteria, schemes and the like, objectively quantifying subjective judgment of a person by a certain scale, and carrying out qualitative analysis and quantitative analysis on the basis of the objective judgment. The method relies on the subjective thinking of the person to decompose, judge and combine in turn, which is the process of analyzing and integrating the objective things by the brain, and the subjective judgment of the person is processed and expressed in the form of quantity. The general application program is that a hierarchical structure is established, a judgment matrix is constructed, weight calculation and consistency check are carried out, and an evaluation standard is selected for evaluation.
The polish professor Pawlak proposed in 1982 a Rough Set (RS) theory, which is a theory that uses a definite mathematical formula to process imprecise, incomplete, inconsistent information and knowledge, thereby obtaining systematic underlying rules and implicit information. When the theory is adopted for evaluation index processing, the method has the advantages that the mining of the implicit information completely depends on the information hidden in the data, and no additional condition or priori knowledge is needed.
The fuzzy mathematics are created by the professor zhad in the United states, and the fuzzy comprehensive evaluation method is a method for applying fuzzy mathematics and fuzzy statistics to scientifically and comprehensively evaluate things by analyzing each factor influencing the quality of the things and applying a fuzzy transformation principle and a maximum membership principle. The method has clear structure and strong systematicness, can better solve the problems of blurring and difficult quantization, can avoid one-sided performance caused by judging only by one factor, and is suitable for solving various non-deterministic problems.
The ADC method is a method used by the us industry weapons system performance counseling committee in evaluating weapons systems. The system is based on the modeling idea of system state division and conditional transition probability thereof, and aims to evaluate equipment systems according to three factors of Availability (Availability), dependability (Dependability) and capability (capability), and define the efficiency of the systems as the product of the three factors. The ADC efficiency evaluation model can comprehensively reflect the change condition of the weapon system with the lapse of time.
The underground and shielding space communication sensing equipment system comprises a micro unmanned aerial vehicle, an autonomous unmanned aerial vehicle, a companion unmanned aerial vehicle, individual wearable sensing equipment, emergency communication network system equipment, a disaster information acquisition service platform and the like, and can evaluate the equivalent energy including working endurance time, specification, positioning accuracy, communication bandwidth, single and double communication capability, communication mode, communication distance, transmission rate, information distribution duration, throughput, packet loss rate and the like. According to the research of the evaluation method, an analytic hierarchy process is selected during weight calculation of the comprehensive evaluation index system of the performance of the underground and shielding space communication sensing equipment, and the analytic hierarchy process is applicable to: in multi-objective decision making, complex systems with various variables, complex structures, significant effects of uncertainty factors and the like are encountered, and decision problems in the complex systems all need to make a correct value for describing the relative importance of an objective. The importance of each factor is different, so that the relative importance of each factor needs to be estimated (i.e. weights) to reflect the importance of the factor, and the set of weights of each factor is a weight set. The weight is an objective reflection of the physical properties of the index itself, and is the result of subjective and objective comprehensive measurement. The analytic hierarchy process (AnalyticHierarchyProcess, AHP) in the system engineering theory is a better weight determination method. The method is a multi-objective and multi-criterion decision method for dividing each factor in the complex problem into associated ordered layers to lead the factors to be physicochemical, and is an effective method for combining quantitative analysis and qualitative analysis.
Meanwhile, as the expert scoring of the analytic hierarchy process is very subjective, inconsistent or missed filling situations often occur in the scoring matrix, correction is carried out on the expert scoring matrix, and a particle swarm optimization algorithm and an entropy weight method are compared in the selection of a correction method; finally, selecting the comprehensive evaluation method of the equipment efficiency, comparing and researching the fuzzy comprehensive evaluation method and the gray clustering method,
(1) Particle swarm optimization algorithm and entropy weighting method
Particle swarm optimization algorithms are methods for finding optimal solutions through collaboration and information sharing among individuals in a population, which have the advantage of being simple and easy to implement and without many parameter adjustments. The algorithm has the advantages of easy realization, high precision, quick convergence and the like, and is widely applied to the application fields of function optimization, neural network training, fuzzy system control and other genetic algorithms.
The entropy weight method is an objective weighting method, in the specific use process, the entropy weight of each index is calculated by utilizing information entropy according to the dispersion degree of the data of each index, and then the entropy weight is corrected to a certain extent according to each index, so that objective index weight is obtained. The entropy weight method is a method for objectively determining the weight, and has certain accuracy compared with subjective methods such as an analytic hierarchy process and the like; and secondly, the weight determined by the method can be corrected, so that the characteristic of higher adaptability is determined, but the application range of the method is limited, and the method is only suitable for calculating the weight.
In conclusion, compared with an entropy weight method, the particle swarm optimization algorithm has wider application range and more accurate algorithm, and is more suitable for computing the weight of the communication sensing equipment and evaluating the equipment of the underground shielding space.
(2) Fuzzy comprehensive evaluation method and gray clustering method
The fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics and expert scoring. The comprehensive evaluation method converts qualitative evaluation into quantitative evaluation according to membership theory of fuzzy mathematics, and makes an overall evaluation on things or objects limited by various factors. The evaluation method has the characteristics of clear results and strong systematicness, can better solve the problems of ambiguity and difficult quantification, and is suitable for solving various non-deterministic problems.
The gray clustering is also called as gray absolute correlation clustering, and is a clustering method based on gray correlation degree formed by every two indexes. Gray-correlated clustering is mainly used for merging of similar factors to simplify a complex system. Thus, it can be checked whether several of the many factors are very closely related. The gray clustering method is widely applied to water quality analysis and atmospheric pollution evaluation, and particularly has wide application in the field of geological disaster evaluation.
In conclusion, compared with the gray clustering method, the fuzzy comprehensive evaluation method is suitable for all evaluation objects, has a wide application range, gives attention to subjective weighting and objective weighting, quantifies factors with unclear boundaries and difficult quantification, and is more suitable for the multi-effect characteristic of the project equipment.
Therefore, an evaluation method for performance of communication sensing equipment in underground shielding space is provided to solve the difficulty in the prior art, which is a problem to be solved by those skilled in the art.
Disclosure of Invention
The invention provides an underground shielding space communication sensing equipment efficiency evaluation method, which mainly aims at evaluating equipment efficiency, checking whether the equipment performance meets design requirements or not, optimizing equipment working capacity and an integrated system, and improving contribution rate of individual equipment in the system so as to play a maximum role of the equipment in actual rescue.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an underground shielding space communication perception equipment efficiency evaluation method comprises the following steps:
s1: acquiring underground shielding space disaster rescue requirements and underground shielding space communication sensing equipment requirements;
s2: establishing a four-layer underground shielding space communication perception equipment efficiency evaluation index system by using an analytic hierarchy process, and calculating the weight of a comprehensive evaluation index of the communication perception equipment;
s3: evaluating the equipment efficiency evaluation index system by adopting a multi-stage fuzzy comprehensive evaluation method to obtain a fuzzy comprehensive judgment result of the equipment system;
s4: and analyzing and judging whether the overall equipment efficiency meets the design requirement according to the fuzzy comprehensive judgment result of the equipment system.
In the above method, optionally, the performance evaluation index system of the four-layer underground shielding space communication sensing device in S1 specifically includes: a target layer, a quasi-side layer, a capability index layer and a basic index layer;
target layer: evaluating the effectiveness of the communication sensing equipment in the low shielding space;
quasi-lateral layer: a five-step index of the equipment;
capability index layer: the membership of the functional characteristics of the underground shielding space communication sensing equipment system and the five-step index;
basic index layer: membership of basic parameters of the equipment to capability indicators.
In the above method, optionally, the five indexes include: light weight, standardization, systemization, intellectualization and practicability.
In the above method, optionally, the method of calculating the weight of the comprehensive evaluation index of the communication sensing equipment in S2 is a combination of a hierarchical analysis method with a particle swarm optimization algorithm and a swarm decision method.
The method, optionally, the specific step S2 is as follows:
s201: industry experts respectively score the importance of the four layers of indexes by adopting a 9-scale method to form a judgment matrix;
s202: correcting the matrix;
s203: then, importance ranking is carried out on the judgment matrix, namely, each index weight is calculated, and consistency inspection and hierarchical total ranking inspection are passed;
s204: and finally, fusing the index weight coefficients into final index weights by adopting a direct mean value method to obtain an index weight set.
In the above method, optionally, step S202 specifically includes:
and correcting the judgment matrix by using a particle swarm optimization algorithm.
The method, optionally, the specific step S3 is as follows:
s301: dividing the basic index into quantized and unquantized indexes, setting a comment set and corresponding calculated values, wherein the quantized indexes adopt a third-party test result, and the unquantized indexes are scored by industry experts or equipment users to obtain membership of the basic index;
s302: starting from the first stage, determining a fuzzy matrix according to an index weight set and a membership matrix, determining the fuzzy matrix of the previous stage step by step, and weighting the fuzzy matrix of each stage to obtain a fuzzy comprehensive matrix;
s303: and calculating a value matrix according to the fuzzy comprehensive matrix and the comment set to obtain a fuzzy comprehensive judgment result of the equipment system.
Compared with the prior art, the invention provides the efficiency evaluation method for the communication sensing equipment of the underground shielding space, which has the following beneficial effects:
(1) The invention establishes the membership of the five-degree and the equipment system corresponding to the different capability indexes, subdivides the indexes of different capabilities, parameters, standards and the like, so that the evaluation of the efficiency is more scientific;
(2) According to the definition of the efficiency of the equipment system, the invention constructs an underground shielding space communication perception equipment efficiency evaluation index system based on the basis of the functional actual combat requirement of the equipment system by combining the top-layer, comprehensive, light weight, standardization, systemization, intellectualization and practicability as the basis, and ensures the rationality of the construction of the index system; in the aspect of equipment evaluation, a judgment matrix is optimized by using a group particle optimization algorithm, so that the rationality of index weight distribution is ensured; the equipment efficiency is evaluated by using a multi-layer fuzzy comprehensive evaluation method, the problem that quantitative and non-quantitative indexes are combined can be well solved, and the evaluation result can effectively reflect the whole application efficiency of the equipment.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for evaluating the effectiveness of communication sensing equipment in an underground shielding space;
FIG. 2 is a schematic diagram of the index construction disclosed in the present invention;
FIG. 3 is a schematic diagram of an index system for evaluating performance of communication sensing equipment in an underground shelter.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In this application, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions, and the terms "comprise," "include," or any other variation thereof, are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Referring to fig. 1, a method for evaluating performance of communication sensing equipment in an underground shielding space comprises the following steps:
s1: acquiring underground shielding space disaster rescue requirements and underground shielding space communication sensing equipment requirements;
s2: establishing a four-layer underground shielding space communication perception equipment efficiency evaluation index system by using an analytic hierarchy process, and calculating the weight of a comprehensive evaluation index of the communication perception equipment;
s3: evaluating the equipment efficiency evaluation index system by adopting a multi-stage fuzzy comprehensive evaluation method to obtain a fuzzy comprehensive judgment result of the equipment system;
s4: and analyzing and judging whether the overall equipment efficiency meets the design requirement according to the fuzzy comprehensive judgment result of the equipment system.
Further, referring to fig. 2, the performance evaluation index system of the four-layer underground shielding space communication sensing equipment in S1 specifically includes: a target layer, a quasi-side layer, a capability index layer and a basic index layer;
target layer: evaluating the effectiveness of the communication sensing equipment in the low shielding space;
quasi-lateral layer: a five-step index of the equipment;
capability index layer: the membership of the functional characteristics of the underground shielding space communication sensing equipment system and the five-step index;
basic index layer: membership of basic parameters of the equipment to capability indicators.
Further, the five-step index includes: light weight, standardization, systemization, intellectualization and practicability.
Furthermore, the method for calculating the weight of the comprehensive evaluation index of the communication sensing equipment in S2 is a combination of a hierarchical analysis method, a particle swarm optimization algorithm and a swarm decision method.
Further, the specific step of S2 is as follows:
s201: industry experts respectively score the importance of the four layers of indexes by adopting a 9-scale method to form a judgment matrix;
s202: correcting the matrix;
s203: then, importance ranking is carried out on the judgment matrix, namely, each index weight is calculated, and consistency inspection and hierarchical total ranking inspection are passed;
s204: and finally, fusing the index weight coefficients into final index weights by adopting a direct mean value method to obtain an index weight set.
Further, the step S202 specifically includes: correction of judgment matrix by particle swarm optimization algorithm
In particularThe specific steps of S201 are as follows: target denoted by A, u i 、u j (i, j=1, 2, … …, n) represents an index, u ij Represents u i For u j And is composed of u ij And forming an A-U judgment matrix P.
S202 comprises the following specific steps: because the expert scoring of the analytic hierarchy process is very subjective, inconsistent or missed filling situations often occur to the scoring matrix, and then the particle swarm optimization algorithm can be adopted to correct the expert scoring matrix.
The PSO initializes to a group of random particles (random solution) and then finds the optimal solution by iteration, in each iteration the particles update themselves by tracking two "extrema" (pbest, gbest), after which the particles update their own velocity and position by the following formula.
V i+1 ＝V i +c 1 ×rand(0～1)×(pbest i -x i )+c 2 ×rand(0～1)×(gbest i -x i ) (2)
x i+1 ＝x i +V i (3)
i=1, 2, …, M being the total number of particles in the population; v (V) i Is the velocity of the particles; pbest is the individual optimum; gbest is the global optimum; rand (0-1) is a random number between (0, 1); x is x i Is the current position of the particle. c 1 And c 2 Is a learning factor, usually taking c 1 ＝c 2 In each dimension, the particle has a maximum limiting velocity Vmax, and if the velocity in a dimension exceeds the set Vmax, the velocity in that dimension is defined as Vmax.
S203, the specific steps are as follows: calculating importance ranking: according to the judgment matrix, find out the maximum characteristic root lambda max The corresponding feature vector w. The equation is as follows:
P w ＝λ max ·w (4)
the feature vector w is normalized, and the importance ranking of each evaluation index, namely the weight distribution is achieved.
Consistency test: whether the weight distribution obtained above is reasonable or not, and consistency test is needed to be carried out on the judgment matrix. The test uses the formula:
wherein CR is the random consistency ratio of the judgment matrix; CI is a consistency index of the judgment matrix. It is given by:
RI is the average random consistency index of the judgment matrix, and RI values of the judgment matrix of 1-9 orders are shown in the following table.
Table 1 judgment matrix
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 | 1.41 | 1.46 |
When judging CR of matrix P<0.1 time or lambda max When=n, ci=0, P is considered to have satisfactory consistency, otherwise the index in P is adjusted to have satisfactory consistency.
And (3) checking the hierarchical total ordering: the total rank of the hierarchical analysis method is to obtain the total target combination weight of a certain layer of indexes in the hierarchical structure and the mutual influence of the total target combination weight and the upper layer of indexes, and the combination weight of the layer of indexes is calculated by utilizing the result of all the single ranks of the layer, which is called the total rank of the hierarchy.
Wi in the formula m Global weight for the ith layer of mth index; CI (CI) m A combined consistency index which is an ith index of an ith layer; RI (RI) m Is the random consistency index of the ith layer.
If the total rank consistency CR <0.1, then it indicates that the total rank consistency test is passed.
Further, the specific step of S3 is as follows:
s301: dividing the basic index into quantized and unquantized indexes, setting a comment set and corresponding calculated values, wherein the quantized indexes adopt a third-party test result, and the unquantized indexes are scored by industry experts or equipment users to obtain membership of the basic index;
s302: starting from the first stage, determining a fuzzy matrix according to an index weight set and a membership matrix, determining the fuzzy matrix of the previous stage step by step, and weighting the fuzzy matrix of each stage to obtain a fuzzy comprehensive matrix;
s303: and calculating a value matrix according to the fuzzy comprehensive matrix and the comment set to obtain a fuzzy comprehensive judgment result of the equipment system.
Specifically, a comment set is constructed:
the comment set V takes various total evaluation results possibly made by an evaluator on the evaluation index as an index V i The set of components, i.e
V＝{v 1 ,v 2 ,…v n } (8)
Wherein n represents the number of indexes in the comment set V, namely the number of comment grades. The evaluation set contains 4 evaluation grades in total, and the evaluation grades are respectively: excellent, good, medium, poor, i.e. n=4, v= (excellent, good, medium, poor), in order to more intuitively and clearly present each evaluation grade, the limit range of each grade is determined by referring to the percent scoring method: preferably 90 to 100, 75 to 90 are good, 60 to 75 are medium, and less than 60 are bad.
Table 2 comment set
| Evaluation grade | Excellent (excellent) | Good grade (good) | In (a) | Difference of difference |
| Score range | 100～90 | 90～75 | 75～60 | 60～0 |
| Calculated value | 95 | 82.5 | 67.5 | 30 |
The setting of the assessment standard of each index of the comprehensive evaluation index system of the equipment efficiency is based on the actual use function of the equipment on a rescue site, and the assessment index and the system integration effect of project tasks are combined, the actual operability and the reliability of index scoring are considered, and the index capable of directly determining the scoring grade by means of the third party detection or the site test result is defined as a quantitative index in the project assessment index; the third party detection or field test results cannot directly determine the grading level, and the index which needs to be scored by an assessment personnel is defined as a qualitative index. On this basis, scoring criteria for each three-level index were developed, taking as examples the partial quantitative and qualitative indexes, as shown in tables 3 and 4:
TABLE 3 quantitative index scoring criteria
TABLE 4 qualitative rating scale
Determining index membership degree:
the membership degree determination method of the quantitative index comprises the following steps: taking the index 'temperature sensing' as an example, the temperature sensing range of the equipment system is known, and according to the comment grade classification standard, the comment grade of the index 'temperature sensing' is known to be 'excellent', so the membership grade is [1000]. Similarly, the membership of other quantitative indicators may be determined.
The method for determining the membership degree of the qualitative index comprises the following steps: assuming that the primary evaluation group consists of 10 evaluation persons, the membership degree of the corresponding qualitative index can be determined according to the evaluation results of the 10 evaluation persons on the qualitative index. Assuming that a certain index was considered "excellent" by 3 evaluation persons, considered "good" by 5 evaluation persons, considered "medium" by 2 evaluation persons, considered "bad" by no person, the evaluation result of this index was [0.30.50.20].
Constructing a fuzzy matrix:
firstly, evaluating from the bottom layer indexes, and determining the membership degree of each bottom layer index to each index in the comment set through a third party detection mode, a field test mode or a field scoring mode of an evaluation person. Setting index according to ith index u in index set i When evaluating, the j index v in the comment set j The membership degree of r ij Then according to the ith index ui The result of the evaluation can be expressed as a fuzzy set:
wherein R is i Representing a single index panel, can be simply expressed as:
R i ＝(r i1 ，r i2 ，…，r im ) (10)
where i=1, 2, …, m, j=1, 2, …, n; r is (r) ij Representation index u i Is subordinate to the evaluation grade v j And 0.ltoreq.r ij And is less than or equal to 1. Therefore, the m evaluation indexes are respectively subjected to single index comprehensiveThe combined evaluation constitutes the blur matrix R. Each row vector in the matrix corresponds to a single-index evaluation result of a certain evaluation index, and the matrix R can be recorded as:
establishing a multi-level fuzzy comprehensive evaluation model:
1) First-order index fuzzy evaluation
The first-order evaluation is a fuzzy evaluation of all indexes in a certain type of indexes. W (W) i The weight set j=1, 2, …, m and R of j evaluation indexes in the ith layer are fuzzy matrixes formed by respectively performing single-index comprehensive evaluation on the j evaluation indexes in the ith layer, and the fuzzy comprehensive evaluation set of the i indexes in the ith layer is marked as B i ，i＝1，2，…，n。
2) Fuzzy evaluation of secondary index
In the previous step, only the fuzzy evaluation is performed on the indexes in the index class, and in a multi-level fuzzy comprehensive evaluation model, fuzzy synthesis among all the index classes should be performed to obtain a final evaluation result.
Wherein B is the comprehensive evaluation result of each index in the evaluation index system, if the system contains a large number of more complex indexes, the multiple division can be carried out, so that a three-level fuzzy evaluation model is constructed.
(5) Comprehensive efficacy evaluation
The result of the fuzzy comprehensive evaluation is that the membership degree of the evaluated object to the fuzzy subset of each grade is generally a fuzzy vector instead of a point value, so that the information provided by the fuzzy comprehensive evaluation method is more abundant than that provided by other methods. Comparing and sorting the multiple evaluation objects, further processing is needed, namely, calculating the comprehensive score of each evaluation object, sorting according to the size, and sorting according to preference. The comprehensive evaluation result B is converted into a comprehensive score, and then the comprehensive scores can be ranked according to the size of the comprehensive scores, so that the optimal comprehensive score is selected.
Comprehensive evaluation vector b= (B) 1 ，b 2 ，…，b n ) Is a fuzzy vector, and the obtained vector is required to be integrated (or clarified) to determine the level of comprehensive evaluation in consideration of the fact that the actual evaluation result is always clear, and the maximum membership rule is generally adopted to make the comprehensive evaluation result.
According to the maximum membership principle, selecting a fuzzy comprehensive evaluation set:
B＝(b 1 ，b 2 ，…，b n ) (14)
in the formula b maximum j Corresponding level (comment) v j As a result of the comprehensive evaluation.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for a system or system embodiment, since it is substantially similar to a method embodiment, the description is relatively simple, with reference to the description of the method embodiment being made in part.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The method for evaluating the effectiveness of the communication sensing equipment in the underground shielding space is characterized by comprising the following steps of:
s1: acquiring underground shielding space disaster rescue requirements and underground shielding space communication sensing equipment requirements;
s2: establishing a four-layer underground shielding space communication perception equipment efficiency evaluation index system by using an analytic hierarchy process, and calculating the weight of a comprehensive evaluation index of the communication perception equipment;
s3: evaluating the equipment efficiency evaluation index system by adopting a multi-stage fuzzy comprehensive evaluation method to obtain a fuzzy comprehensive judgment result of the equipment system;
s4: and analyzing and judging whether the overall equipment efficiency meets the design requirement according to the fuzzy comprehensive judgment result of the equipment system.
2. The method for evaluating the performance of the underground shielding space communication sensing equipment according to claim 1, wherein the four-layer underground shielding space communication sensing equipment performance evaluation index system in S1 specifically comprises: a target layer, a quasi-side layer, a capability index layer and a basic index layer;
target layer: evaluating the effectiveness of the communication sensing equipment in the low shielding space;
quasi-lateral layer: a five-step index of the equipment;
capability index layer: the membership of the functional characteristics of the underground shielding space communication sensing equipment system and the five-step index;
basic index layer: membership of basic parameters of the equipment to capability indicators.
3. The method for evaluating the performance of underground shelter space communication sensory equipment as claimed in claim 2, wherein,
the five indexes comprise: light weight, standardization, systemization, intellectualization and practicability.
4. The method for evaluating the performance of underground shelter space communication sensory equipment as claimed in claim 1, wherein,
and S2, calculating the weight of the comprehensive evaluation index of the communication sensing equipment by combining a particle swarm optimization algorithm and a swarm decision method.
5. The method for evaluating the effectiveness of communication sensing equipment in an underground shielding space according to claim 1, wherein the specific steps of S2 are as follows:
s201: industry experts respectively score the importance of the four layers of indexes by adopting a 9-scale method to form a judgment matrix;
s202: correcting the matrix;
s203: then, importance ranking is carried out on the judgment matrix, namely, each index weight is calculated, and consistency inspection and hierarchical total ranking inspection are passed;
s204: and finally, fusing the index weight coefficients into final index weights by adopting a direct mean value method to obtain an index weight set.
6. The method for evaluating the performance of underground shelter space communication perception equipment of claim 4, wherein step S202 comprises the steps of:
and correcting the judgment matrix by using a particle swarm optimization algorithm.
7. The method for evaluating the effectiveness of communication sensing equipment in an underground shielding space according to claim 1, wherein the specific steps of S3 are as follows:
s301: dividing the basic index into quantized and unquantized indexes, setting a comment set and corresponding calculated values, wherein the quantized indexes adopt a third-party test result, and the unquantized indexes are scored by industry experts or equipment users to obtain membership of the basic index;
s302: starting from the first stage, determining a fuzzy matrix according to an index weight set and a membership matrix, determining the fuzzy matrix of the previous stage step by step, and weighting the fuzzy matrix of each stage to obtain a fuzzy comprehensive matrix;
s303: and calculating a value matrix according to the fuzzy comprehensive matrix and the comment set to obtain a fuzzy comprehensive judgment result of the equipment system.
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