CN111708986A

CN111708986A - Pipe gallery state parameter measuring method

Info

Publication number: CN111708986A
Application number: CN202010475016.3A
Authority: CN
Inventors: 唐朝国; 张毅; 朱聪; 胡波; 朱振华; 蒋俊杰; 张�杰
Original assignee: Sichuan Crungoo Information Engineering Co Ltd
Current assignee: Sichuan Crungoo Information Engineering Co Ltd
Priority date: 2020-05-29
Filing date: 2020-05-29
Publication date: 2020-09-25

Abstract

The invention discloses a pipe gallery state parameter measuring method, which relates to the detection technology and comprises the following steps: detecting environmental parameters; secondly, generating an environment parameter membership matrix through a preset parameter evaluation grading table; thirdly, establishing a judgment matrix fusing all environmental parameters according to a preset parameter importance level quantification assignment table based on an analytic hierarchy process; respectively calculating the geometric mean value corresponding to each environmental parameter based on each judgment matrix, and constructing a weight vector by using the geometric mean values of each parameter; fifthly, performing weighted calculation on the state grade distribution of each environment parameter to obtain a final state grade distribution vector; and (VI) based on DS evidence theory, calculating the support degree of mutual description of the vectors by the following formula, and calculating the final state grade distribution vector

Description

Pipe gallery state parameter measuring method

Technical Field

The present invention relates to detection technology.

Background

The utility tunnel is an underground city utility tunnel which integrates an electric pipeline, a gas pipeline, a water supply and drainage pipeline, a communication pipeline, a heat supply pipeline and the like. Because the inner space of the pipe gallery is large and the distance is long, a variety of sensor groups such as temperature, humidity, gas concentration, pipeline pressure and the like are built inside the pipe gallery. For carrying out environmental monitoring and reducing the disaster loss to the piping lane, divide the utility tunnel into a plurality of fire prevention subregion. Each fire zone has a different type of environmental parameter sensor for monitoring environmental changes in that fire zone. The types of sensors are mostly: oxygen concentration sensor, carbon monoxide sensor, combustible gas sensor, temperature sensor, humidity transducer, smog concentration sensor, water level sensor etc.. The existing method mainly adopts single index evaluation for evaluation of each fire protection subarea, for example, when the concentration of combustible gas is higher, the fire protection subarea is considered to have the risk of fire or leakage. The method is single in discrimination form, cannot form correlation with other sensor parameters, and lacks of integral evaluation on the safety state of the pipe gallery. Therefore, the utility tunnel needs to be fused with the environmental safety evaluation of multiple parameter indexes, and the multi-index fusion evaluation can accurately and effectively give the environmental safety evaluation under the current environmental parameter when the single index evaluation cannot accurately give the risk condition judgment, and can avoid the error and misjudgment problems that the single evaluation is easily influenced by the stability of the sensor equipment, and help the working personnel to carry out safety judgment on the whole fireproof subarea and make safety judgment and decision in time. Therefore, the development of a comprehensive pipe rack state evaluation method integrating a plurality of types of sensors is urgently needed.

The existing comprehensive evaluation method is a multi-hierarchy analysis method, the hierarchy analysis method is a common method for establishing an evaluation system, a judgment matrix is given through expert knowledge, weights of different factors are influenced by different judgment matrixes, and then all the factors are weighted and fused to obtain a final comprehensive evaluation result. The literature [1] develops a comprehensive evaluation of a liquefied natural gas ship based on an analytic hierarchy process with 10 evaluation indexes. The method only uses one expert judgment matrix to carry out importance rating on 10 different factors, does not set other modes for auxiliary evaluation, has low reliability and is not suitable for safety evaluation of the comprehensive pipe gallery. Document [2] proposes a D-S evidence theory evaluation method combining a layer analysis method and gray cloud clustering. Firstly, obtaining a plurality of expert opinions by using an analytic hierarchy process, then fusing the opinions by using an evidence theory, and finally finishing evaluation by calculating a gray clustering coefficient. When the expert opinions are fused, the fusion result is influenced to a large extent by the size of the conflict threshold, and the conflict threshold is difficult to determine. Document [3] proposes a fuzzy comprehensive evaluation method based on an analytic hierarchy process and information entropy. According to the method, the quality characteristic weight obtained by integrating the information entropy and the judgment matrix is utilized, the comprehensive evaluation weight index value is obtained by utilizing a fuzzy evaluation method, and finally, the index values are sequenced to determine the importance of the quality characteristic.

[1] Evaluation of liquefied natural gas vessels based on analytic hierarchy process [ J ] China navigation, 2019,42(01): 128-.

[2] A guidance simulation system credibility evaluation method based on DS/AHP and gray cloud clustering [ J ]. an electronic measurement technology, 2017,40(7):43-47.

[3] Ball valve quality multi-level fuzzy comprehensive evaluation method based on AHP and information entropy [ P ] Chinese patents of CN110135708A,2019-08-16.

In summary, the comprehensive evaluation of the fireproof subareas of the comprehensive pipe gallery at present has the following two problems:

1) overall evaluation was lacking. The existing method is to evaluate the environmental situation of the fire zone by means of a single sensor value range of different classes. The method has large error and the problem of false evaluation and missing report, and is not beneficial to the environmental monitoring of the comprehensive pipe rack. And the sensors lack relevance, so that when a certain sensor fails, the wrong report and the wrong report of an evaluation system are caused, and the judgment of staff on the environmental safety of the gallery is influenced to a greater extent.

2) The evaluation results were poor in stability. In order to reduce disaster loss and improve disaster early warning capability, the evaluation system should have certain stability. The conventional comprehensive evaluation method based on the analytic hierarchy process relies on a judgment matrix given by expert knowledge to obtain final parameter weights. However, since the expert judgment matrix is given by the subjective experience of the expert, and the emphasis of each expert is not all the same, the obtained evaluation results may not be uniform, that is, stable evaluation results cannot be obtained.

Disclosure of Invention

The invention aims to solve the technical problems that the existing evaluation mode is lack of overall evaluation and poor in stability of evaluation results, and provides a method for obtaining comprehensive pipe gallery state evaluation by firstly obtaining an expert judgment matrix by using an analytic hierarchy process and introducing D-S evidence theory and KL divergence to calculate the consistency of the evaluation results under different expert knowledge.

The technical scheme adopted by the invention for solving the technical problems is that the pipe gallery state parameter measuring method is characterized by comprising the following steps:

the following 6 environmental parameters are detected: temperature, humidity, oxygen concentration, water level, hydrogen sulfide concentration, methane concentration;

secondly, evaluating a grading table through preset parameters to generate an environment parameter membership matrix for representing the probability that each environment parameter belongs to different grading states in the grading table;

thirdly, establishing a judgment matrix fusing all environmental parameters according to a preset parameter importance level quantification assignment table based on an analytic hierarchy process, wherein the judgment matrix comprises a first judgment matrix taking temperature as the most important item, a second judgment matrix taking humidity as the most important item and a third judgment matrix taking methane concentration as the most important item;

respectively calculating the geometric mean value corresponding to each environmental parameter based on the first judgment matrix, and constructing a first weight vector by the geometric mean value of each parameter; generating a second weight vector based on the second judgment matrix and a third weight vector based on the third judgment matrix in the same way;

(V) performing weighted calculation on the state grade distribution of each environmental parameter according to the following formula to obtain a final state grade distribution vector for each judgment matrix;

wherein P is_dRepresenting the probability of a state level D, D being the total number of state levels, D being the state level ordinal number, h being the item ordinal number of the environmental parameter, W_hIs a weight vector, R_hdA membership function value of the h-th environmental parameter at the level d;

and (VI) based on DS evidence theory, calculating the support degree of mutual description of the vectors by the following formula, and calculating the final state grade distribution vector

Wherein the content of the first and second substances,

is by distribution

Instead of another distribution

The amount of information lost;

(seventhly) calculating a final evaluation score:

wherein, U_dIs a predefined state level weight.

The invention maintains the evaluation capability of single evaluation on the safety state when a disaster occurs, and simultaneously has the evaluation capability when the disaster does not occur, the disaster is about to occur and the disaster occurs, thereby being beneficial to long-term monitoring of the environmental safety of the comprehensive pipe rack and being beneficial to long-term operation and maintenance of the comprehensive pipe rack.

Detailed Description

The invention relates to a construction of a comprehensive evaluation method for fire prevention subareas of a comprehensive pipe gallery. In the evaluation method, firstly, an environment parameter set is defined, and each environment parameter is subjected to state classification by utilizing a membership function; then, a plurality of important factor judgment matrixes are established by using an analytic hierarchy process according to national standard criteria and expert opinions, different judgment matrixes focus on different aspects, and weight vectors of a plurality of environment parameters can be obtained through the judgment matrixes, so that state grade distribution is obtained through fusion, and the reliability of the final result is improved. Secondly, the multiple state grade distributions obtained by different judgment matrixes utilize the thought of a D-S evidence theory, evidences of the state grade distributions are divided by K-L divergence, the basic trust distribution of the state grade distributions is calculated, and the trust distribution is used as weight to fuse the state grade distributions, so that accurate and effective evaluation on fire partitions is realized, and meanwhile, when the score is low, the problem can be further diagnosed through the trust distribution of the state grade distributions.

The invention has the characteristics that:

1) and fusing various environment parameters by adopting different judgment matrixes. The method establishes different judgment matrixes by taking temperature, humidity and methane concentration as important factors respectively, and each judgment matrix emphasizes different aspects so as to obtain a plurality of state grade distributions

2) The DS method is introduced to fuse different state grade distributions. The method utilizes the idea of DS evidence theory and KL divergence to divide evidence by calculating KL distances among different state grade distributions, and distributes basic trust of the evidence to realize DS fusion.

The invention is further illustrated by the following examples:

according to the comprehensive pipe gallery state evaluation method, an environment parameter set and a pipe gallery state grade are defined, the parameter set comprises temperature, humidity, oxygen concentration, water level, hydrogen sulfide concentration and methane concentration, and the state grade is 1-4. Then, an expert judgment matrix is given to describe the relative importance degree among different environment parameters, and the AHP is utilized to obtain the state grade distribution from the environment parameter set. And finally, respectively obtaining different state grade distributions by defining a plurality of expert judgment matrixes, dividing evidences of the state grade distributions by using a DS (direct sequence) method, distributing basic trust to realize fusion, and obtaining the final comprehensive pipe gallery state evaluation.

Pipe gallery environmental parameters and status ratings

1) A set of environmental parameters. According to national standard GB50838-2015 city heald

Close piping lane engineering specification, the utility tunnel should set up environment and equipment monitored control system, and the environment detects the content and should satisfy that table 1 below shows, contains the cabin of two kinds and above pipelines, should press the pipeline setting of higher requirement. The gas alarm set value is in accordance with the relevant regulation of the national current standard 'occupational hazard protection code for closed space operation' GBZ/T205.

TABLE 1 environmental parameter monitoring

The effects and effects of the environmental parameters in table 1 on the equipment and staff in the corridor are as follows.

And (3) temperature. The temperature is one of the main foundation that the conflagration was judged, and temperature monitoring can effectively judge each the inside working equipment operation of piping lane, cable heat dissipation, the ventilation condition etc. can guarantee piping lane inside staff's health status simultaneously. For the operation of the pipe gallery, the problems of abnormal work of partial working equipment in the pipe gallery, large error of a sensor and the like are easily caused by too low or too high temperature. Meanwhile, the water pipeline and accumulated water in the foundation pit can be frozen, agglomerated and other adverse conditions due to too low temperature; and if the temperature is too high, dangerous situations such as fire and the like are easy to occur, and the abnormal ventilation situation is also shown. For the staff in the corridor, too low or too high temperature can cause the physical discomfort of the staff and influence the working state of the staff.

Humidity. The health state of working equipment and staff is influenced to a large extent by the humidity. For working equipment, the electrical equipment runs in an environment with humidity more than 70% for a long time, which may cause the surface of solid insulator to continuously absorb moisture and form a water film, and ions contained in the water move along the surface of the insulator in an electric field and form accumulation near an electrode, so that the local electric field intensity is increased, discharge occurs, and the flashover voltage along the surface is reduced. The more severe the moisture absorption, the more the flashover voltage decreases along the surface, and this discharge along the surface of the insulation can cause local overheating of the insulation surface to carbonization with a strong discharge sound and arc odor. Which eventually causes a short circuit fault with respect to ground and the phases, resulting in a significant electrical accident. Meanwhile, when the humidity is low, the probability of fire occurrence can be improved, and fire accidents are caused. For workers, in summer, the humidity is increased, the steam water tends to be saturated, the exertion of the heat dissipation function of the human body can be inhibited, and people feel very sultry and dysphoria; in winter, the humidity is increased, which can accelerate the heat conduction by 20 times, make people feel cool and depressed, and cause dry mouth and tongue, sore throat, hoarseness and epistaxis, and cold. And when the indoor air humidity is lower than 40%, dust, bacteria and the like are easily attached to mucous membranes to stimulate the throats and cause cough, and respiratory diseases such as bronchitis, asthma and the like are easily induced. Influenza viruses are rapidly propagated in dry environments, and also cause symptoms such as allergic dermatitis and skin itch.

The oxygen concentration. Due to the inconsistency of the oxygen index of different materials, the purpose of oxygen monitoring should be mainly around the health status of the staff in the corridor. According to the toxic reaction, severity, occurrence frequency and toxic action mechanism of oxygen concentration to organisms in toxicology, the working capacity is reduced, and the coordination capacity and perception judgment capacity are reduced when the oxygen concentration is lower than a normal level; inadequate oxygen concentration will lead to shortness of breath, loss of judgment, purple lips, and if less than 8% will lead to death.

Water level. In recent years, local large-scale water accumulation often occurs due to the fact that urban inland inundation phenomenon is serious. When the sewage pipe cannot discharge water immediately due to difficulty in water drainage in a city, the flood discharge can be realized by means of a large space of the underground comprehensive pipe gallery immediately. Because underground utility tunnel can realize getting rid of ponding, so need acquire the water level height condition of ponding in the piping lane. Meanwhile, the over-high water level may cause water inflow of working equipment, electric leakage of cables and other dangerous conditions.

The concentration of hydrogen sulfide. The hydrogen sulfide has great harm to working equipment and physical conditions of workers. For working equipment, the hydrogen sulfide is combined with water vapor in a pipe gallery to easily corrode pipelines and equipment, and the hydrogen sulfide is flammable and explosive, and the equipment and personnel safety can be harmed by the explosion with too high concentration. For workers, hydrogen sulfide stimulates eyeballs according to toxicology, causes respiratory tract stimulation, and the high concentration can cause olfactory sensation loss, consciousness loss and respiratory arrest to death. In conclusion, the concentration of hydrogen sulfide should be strictly controlled in the corridor.

The methane concentration. Methane concentration is one of the important constitution of piping lane safety evaluation, and methane is as easily combustible explosive gas, and the too high explosion that can take place of concentration can make the staff appear the condition such as dizziness, breathing acceleration, dyskinesia simultaneously, also explains the inside condition that probably ventilation fault or gas pipeline leaked of piping lane simultaneously.

In summary, the present invention defines an environmental parameter set { H | H ═ 1 to H } { temperature, humidity, water level, oxygen concentration, hydrogen sulfide concentration, methane concentration }, where H is the total number of environmental parameters, and H ═ 6.

2) Pipe gallery state grade. And establishing a grading standard and a state grade for each environmental parameter by combining the influence of each parameter on working equipment and working personnel. The invention defines the state grade as the pipe gallery state grade D, D is 1-D, D is 4, the pipe gallery environment safety state is the best when the grade is 1, and the pipe gallery environment safety state is the worst when the grade is 4. The ranking table for the environmental parameters at different state levels is shown in table 2 below.

Table 2 environmental parameter evaluation grading table

Wherein x is₁＝|x′₁-20|，x₂＝|x′₂-40, in the temperature regime classification, 20 is the optimum temperature; humidity evaluation scale 40 was the optimum humidity.

3) The environment parameter relates to the degree of membership of the different states. Based on the relevant characteristics of each environmental parameter, the oxygen concentration membership function selects a half-increasing order, and other selected half-decreasing step-shaped membership functions are as follows:

stage 1, i.e. when d is 1:

raising half step shape membership function:

decreasing the half-step membership function:

stage 2 and 3, i.e. when d is 2 and d is 3:

raising half step shape membership function:

decreasing the half-step membership function:

stage 4, i.e. when d is 4:

raising half step shape membership function:

decreasing the half-step membership function:

wherein x is₁′、x₂′、x₃、x₄、x₅、x₆Actual measurement values of the environmental parameters are represented; s_hdLevel d criteria representing an h environmental parameter, e.g. S₁₁＝10。

From this, a membership matrix R can be obtained_H×DThe relationship between the environmental parameters and the status levels can then be quantified by means of a membership matrix, in which each row is followed by a row

Representing the probability that the environment parameter h belongs to different state classes.

4.2 AHP assessment based on Multi-environmental parameters

1) And judging the matrix. Since the membership function can only provide the distribution of each environmental parameter in different state levels, the distribution of each environmental parameter needs to be fused by using an analytic hierarchy process proposed by Santy. Analytic hierarchy process, AHP for short, refers to a decision-making method that decomposes elements related to decision-making into levels such as targets, criteria, schemes, etc., and performs qualitative and quantitative analysis based on the levels. The core of the AHP is to calculate the fusion weight of each parameter according to the relative importance of each parameter.

For different parameters of the problem, AHP compares them pairwise and according to importanceThe degree rating, importance rating and their assignment are shown in table 3. A. the_h,h'The degree of importance of one environmental parameter h to another parameter h' is represented, a matrix formed by two-by-two comparison results is called a judgment matrix, and the judgment matrix has the following properties:

TABLE 3 importance level and its assigned value table

Condition h is less than condition h'	Quantized value
		All the same important	1
Of slight importance	3
		Of greater importance	5
Of strong importance	7
		Of extreme importance	9
Intermediate values of two adjacent judgments	2,4,6,8

The environmental parameters embodied by different accident environments of the comprehensive pipe gallery are not all the same in terms of emphasis factors, so thatCombining GB50838-2015 and expert opinions to establish a judgment matrix A_H×H. According to the analysis of each environmental parameter in 4.1, the temperature is considered to have strong representation capability in the problems of fire, leakage, ventilation and the like, so that the importance degree of the temperature is considered to be higher under the conditions, and a judgment matrix of the temperature with emphasis is established as follows:

after the judgment matrix is established, in order to judge whether the importance of the parameters is reasonable or not and whether the common sense violation exists or not, the consistency of the judgment matrix needs to be checked. The test was performed in the following manner.

For the maximum feature root λ of the decision matrix_maxNormalized and then recorded as w. The element of w is the sorting weight value of the relative importance of the condition parameter of the same layer to the condition parameter of the previous layer, and the process is called the hierarchical list sorting. If the hierarchical list sorting can not be confirmed, consistency check is needed, namely, an inconsistent allowable range is determined for the judgment matrix A. Wherein the only nonzero characteristic root of the n-order coherent array is n; maximum characteristic root lambda of n-order positive reciprocal array A_maxAnd n, A is a uniform matrix if and only if λ is n. The consistency index is calculated by CI, and the smaller the CI, the greater the consistency. Defining the consistency index as:

wherein CI ═ 0 indicates complete consistency; CI is close to 0, and then the consistency is satisfied, and the larger CI is, the larger the inconsistency is.

To measure the magnitude of CI, a random consistency index RI was introduced, with RI normalized values as shown in table 4.

TABLE 4 RI standard values

Considering that deviations from consistency may be due to random causes, the coefficient CR is checked as shown in the following equation:

and if CR is less than 0.1, the judgment matrix is considered to meet the consistency test, otherwise, the consistency is not met.

The determination matrix A is examined herein using the method described above¹The results show that it meets the consistency requirements.

2) The environmental parameter weight. After obtaining the judgment matrix, converting the judgment matrix into the combined weight of each environmental parameter, calculating the weight coefficient of the environmental parameter by using a characteristic root method, and firstly calculating a matrix A_H×HThe geometric mean of each row of elements is shown below:

then, the normalization processing is carried out to finally obtain a parameter weight vector

As follows:

calculated by the above equation, the matrix A₁Is a vector of parametric weights

From the results, it can be seen that the weight of the temperature is the largest among the weights of the parameters obtained by the matrix with the temperature as the dominance factor.

3) A state level distribution. And weighting the state grade distribution of each environmental parameter by using the parameter weight to obtain the final state grade distribution. As follows:

wherein P is_dRepresenting the probability of the state level d.

4.3DS method fusing different evaluation results

The method comprises the steps of firstly introducing important environmental parameters, establishing a plurality of judgment matrixes according to the important environmental parameters, obtaining state grade distribution, and finally realizing fusion by using a D-S evidence theory and K-L divergence.

1) Importance of environmental parameters

Since the AHP evaluation result is influenced by the ranking of the importance degrees, the security evaluation with only a single important factor cannot achieve a high reliability, and thus, a plurality of important factors need to be determined and fused to obtain the final evaluation. The temperature has been set as an important factor in 4.2. When the important factor is humidity, the judgment of the emphasis on the humidity can effectively monitor the electric leakage, the health state of working equipment and the working state of personnel. When the important factor is methane, the judgment of the methane with emphasis can effectively monitor the fire probability, the gas leakage, the ventilation condition and the health state of workers.

Defining selected important environmental parameter as z, and establishing corresponding judgment matrix

Calculating the weight of the parameter

z ═ {1,2,3} represents temperature, humidity, and methane concentration, respectively. The case of z ═ 1 has been described. When the temperature is taken as an important parameter, the weight of the environmental parameter is recorded as

The state level distribution is recorded as

When z is 2, the humidity is taken as an important environmental parameter, and a judgment matrix is established

As follows

Obtaining the weight of the parameter

Substituting the environmental parameter values to obtain state grade distribution

When z is 3, the methane concentration is used as an important environmental parameter, and a judgment matrix is established

As follows:

obtaining the weight of the parameter

Substituting the environmental parameter values to obtain a state level distribution

2) Brief description of DS evidence theory and KL divergence

D-S evidence theory. The D-S evidence theory, in which the recognition framework theta represents a finite number of perfect and mutually exclusive elements theta, is proposed by Dempster and Shafer₁,θ₂,...,θ_n}，2^θTo identify a set of powers of a framework, evidence theory describes the dissimilarity between elements by defining basic trust assignments. Any focus proposition corresponds to a subset of the recognition framework Θ. If the following holds:

then m is 2^θ→[0,1]Is the mas function on Θ. Wherein m (A) is the basic belief assignment function (BPA) of the primitive, if m (A)>0, then the element A is called focal element. All focal elements are referred to collectively as the nucleus of BPA. The belief function (Bel) and the plausibility function (Pl) are defined as:

for proposition (or event) a in the recognition framework Θ, a confidence interval [ bel (a), pl (a) ], which is used for describing a value range of occurrence probability of proposition a, can be formed. Namely, the evidence theory is to use the confidence interval to describe the uncertainty of proposition. The assignment m (Θ) to the Mass function e [0,1] in evidence theory is used to describe the unknowns. It should be noted that according to the bayesian total probability theory, the total probability P (Θ) of the corpus is 1.

Based on Dempster rule, independent evidence m can be obtained₁And m₂The combination or fusion result of (a) is shown as follows:

wherein:

to normalize the factors, indicate the degree of conflict between the evidences. When a plurality of evidences are combined, the Dempster rule meets the combination law and the exchange law, which is beneficial to the distributed realization of the information fusion system.

Relative entropy. Relative entropy, also known as KL divergence, is a measure of the asymmetry of the difference between two probability distributions. Defining P (X), Q (X) are two probability distributions on the random variable X, and in the case of discrete and continuous random variables, the definitions of the relative entropy are:

in information theory, KL (P | | | Q) represents the information loss that results when Q is fitted with a probability distribution P, where P represents the true distribution and Q represents the fitted distribution of P. KL (P | | Q) ≧ 0, and 0 is assumed when P ═ Q. The larger the relative entropy, the larger the difference between the two distributions; the smaller the relative entropy, the smaller the difference between the two distributions. According to the method, evidence of each state grade distribution is divided by using K-L divergence, basic confidence is further distributed, and finally, each distribution is subjected to weighted fusion to obtain final state grade distribution.

3) Evidence partitioning based on KL distance. Since the state grade distribution is discrete random distribution, the calculation formula of KL in the text is as follows

The expression is that

Instead of another distribution

The amount of information lost, from a probabilistic perspective, can be understood as the amount of information lost

Description distribution

The ability of the cell to perform.

Small, represents

Description of the invention

Is strong in ability, therefore

Can be supported powerfully

Definition of

Support for

Is as follows

Thus completing

The evidence of (2) is generated.

4) DS fusion of state level distributions. The fusion of the state level distributions requires a prior calculation

Basic trust distribution. Setting the important environmental parameter as z, state grade

Is allocated as

All state grade distributions are weighted and fused by adopting basic trust distribution, and the final state grade distribution is obtained after normalization

5) Calculating the final evaluation score of the fire protection zone

The obtained final state grade distribution cannot intuitively obtain the comprehensive evaluation of the fire-proof subarea, and the situation that the probabilities of the four state grades are close or slightly different can occur, so that the state grade distribution needs to be converted into state scores, and the four state grades are assigned as U in a weighting mode_dThe value of {90,80,70,60}, d ═ 1 ~ 4, the final score is:

the larger the value of S is, the better the comprehensive state of the fire protection zone is, and the lower the value of S is, the health state of the fire protection zone may be in a problem. Thereby completing the state evaluation of the utility tunnel fire zone. The specific steps of the algorithm are as follows:

STEP1 inputs environment parameter detection value;

STEP2 obtains a membership matrix through the defined state grade;

STEP3 obtains each parameter weight through the judgment matrix to be fused, and state grade distribution is obtained;

STEP4 obtains a plurality of different state level distributions by defining a plurality of decision matrices;

STEP5 calculates KL distances of three distributions, and divides evidences of the level distribution of each state;

STEP6 carries out DS evidence fusion according to basic trust distribution to obtain the final fire-proof subarea evaluation.

4.4 example analysis

In order to verify the effectiveness and reliability of the text algorithm, the text algorithm is analyzed in an embodiment, and experimental data is selected from collected measured data.

Example 1

The experimental data are shown in the following table:

table 5 example 1 experimental data

The membership degree matrix can be obtained by the formulas (1) to (6)

When the temperature is an important factor, z is 1, and the matrix A is judged according to the judgment¹

The parameter weight vector obtained by the equations (11-12)

Computing

According to formula (14)

To obtain

When humidity is an important factor, z is 2, and the matrix A is judged according to the judgment²

The parameter weight vector obtained by the equations (11-12)

Computing

According to formula (14)

To obtain

When the concentration of methane is an important factor, z is 3, and the matrix A is judged according to the judgment³

The parameter weight vector obtained by the equations (11-12)

Computing

According to formula (14)

To obtain

Can be selected from

It is seen that the temperature belongs to level 1, the membership degree is high, and the weight of the temperature is great, so that the probability that the state output is level 1 is high under the influence of the temperature. From

It is seen that the probability of the state output being 2-level to 3-level is large due to the humidity. From

It can be seen that since methane has a high probability of being class 1, the probability of the state output being class 1 is high.

The evidences of the state level distributions can be classified by equations (22) to (23) as follows:

according to formula (24)

M (1) ═ 0.330, m (2) ═ 0.335, and m (3) ═ 0.335 were obtained.

According to formula (25)

Can obtain the product

According to formula (26)

The final score S is obtained 84.056. It can be seen that the safety state of the fire protection zone obtained from this experiment is class 2. Can be seen from the state level distribution and the final score, although

And

the probability of level 1 is considered to be the greatest, but because

The effect of (c) results in a final result of level 2. Can explain

The intervention of (2) changes the final output result, and improves the effectiveness and reliability of the final output.

Example 2

In example 2, in order to check the evaluation capability of the algorithm in this document on the disaster situation, the measured data of the methane leakage situation is selected to perform an experiment, the methane concentration should be in the state level 1 or the state level 2 under the normal situation, and the methane concentration should be in the state level 3 or the state level 4 under the leakage situation. The experimental data are shown in table 6 below:

table 6 example 2 experimental data

The same operation as in example 1 can be performed by the formulas (1) to (26)

The final score S is 69.81 according to equation (27). As can be seen, the safety state of the fireproof subarea obtained by the experiment is 4-level, and obvious potential safety hazards exist. When other environmental parameters are in a good state, the overall state evaluation can still effectively and accurately express the safety state of the fire-proof subarea due to the large concentration of methane.