CN112016742A - Optimization method of fire rescue path selection algorithm based on AHP - Google Patents
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Abstract
The invention discloses an optimization method of a fire rescue path selection algorithm based on AHP, which comprises the following steps of establishing a hierarchical model optimization target, constructing a judgment matrix, and determining each element value by adopting a consistent matrix method; carrying out consistency check on the judgment matrix to obtain whether the judgment matrix conforms to consistency; calculating weight vectors and total hierarchical ordering to obtain the influence degree of each scheme on the final target; performing dimensionless quantitative processing on parameters influencing the road driving time, wherein the dimensionless quantitative processing comprises the step of performing dimensionless unification on the parameters by adopting a fuzzy decision model and a multi-attribute decision model so as to determine an optimal path selection algorithm optimization model; and optimizing the optimal route selection of the fire rescue through a path planning algorithm to finally obtain the optimal route of the fire rescue. The invention can fully consider the influence of various factors in the actual road condition information on the driving time of the fire-fighting vehicle, thereby obtaining the optimal fire-fighting route and having an auxiliary function on the actual fire-fighting alarm-dispatching decision.
Description
Technical Field
The invention relates to the technical field of optimal path planning, in particular to an optimization method of a fire rescue path selection algorithm based on AHP.
Background
At present, urban areas are moving from small market centers to large cities, and the rapid development increases the possibility of fires occurring due to the interaction between different activities. In addition, with the development of economy, the selectable modes of people for traffic and travel also become diversified, vehicles are continuously popularized, so that the road is always blocked, and the fire fighting team can timely give out an alarm to the fire fighting team in case of fire. The optimal path selection in fire fighting is realized, the purpose is to quickly and effectively select the path to realize fire fighting rescue with minimum loss, and the shortest path is obtained as far as possible, so that the rescue action is quicker and the loss is reduced.
In order to judge the quality of the route, the route can be determined by calculating the comprehensive weight of the selected route, the traditional route selection algorithm takes the most intuitive route length as the unique weight, and the algorithm has no practicability at all and does not meet the requirement of a real road network, so that the influence factor comprehensive judgment algorithm route weight needs to be selected again to optimize the algorithm. The time for fire fighting and fire extinguishing rescue force to reach is the key for success or failure of rescue, the faster the rescue team reaches the scene, the less the fire property and life loss will be, so the rescue arrival time is the first decision attribute for path selection. Factors influencing the time of arrival of the fire rescue vehicle at the scene are many, such as whether traffic control is performed or not, whether traffic jam is performed or not, road surface parameter conditions, weather conditions and the like. At present, an algorithm capable of accurately selecting a path is lacked in this respect.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides an optimization method of a fire rescue path selection algorithm based on AHP, which can comprehensively consider various factors influencing fire-fighting vehicles and optimize a road network weight matrix of the path selection algorithm so as to obtain a path reaching a fire scene in the shortest time.
The technical scheme is as follows: in order to achieve the above object, the present invention provides an optimization method of AHP-based fire rescue path selection algorithm, comprising the following steps:
step 1, establishing a hierarchical model optimization target, wherein the hierarchical model structurally comprises a target layer, a criterion layer and a scheme layer, the hierarchical model optimization target is a fire-fighting alarm-giving shortest time path, and the evaluation standard of a road weight O is time;
step 2, constructing a judgment matrix, wherein the judgment matrix is an importance comparison matrix between any two factors influencing the time weight of the fire-fighting police road, and determining each element value in the matrix according to the importance comparison result of the two factors in the same layer;
step 3, carrying out consistency check on the judgment matrix, wherein the check parameters comprise consistency indexes, random consistency indexes and check coefficients, and obtaining whether the judgment matrix conforms to consistency;
step 4, calculating a weight vector and a total hierarchical ranking, wherein the total hierarchical ranking is to weight the weight vector of the criterion layer and the weight vector of each scheme layer factor to obtain the influence degree of each scheme on the final target;
step 5, performing dimensionless quantitative processing on parameters influencing the road driving time, wherein the dimensionless quantitative processing comprises the step of performing dimensionless unification on the parameters by adopting a fuzzy decision model and a multi-attribute decision model so as to determine an optimal path selection algorithm optimization model;
and 6, optimizing the optimal fire rescue route selection through a path planning algorithm to finally obtain the optimal fire rescue route.
Further, in the present invention: the step 2 of constructing the judgment matrix further comprises the following steps:
comparing the importance of two factors in the same layer by using 1-5 and reciprocal scaling method thereof to obtain a judgment matrix A ═ aij)n×nWherein a isijRepresenting the relative weights of the factor i and factor j quantizations.
Further, in the present invention: in step 3, the calculation formula of the check parameter is respectively as follows:
CI＝(λmax-n)/(n-1)
RI＝(CI1+CI2+CI3+...+CIn)/n
CR＝CI/RI
wherein CI is consistency index, RI is random consistency index, CR is check coefficient, and lambdamaxThe maximum eigenvalue of the judgment matrix is obtained; and when CR is less than 0.1, the judgment matrix is considered to be consistent, otherwise, the judgment matrix needs to be changed.
Further, in the present invention: in step 4, the weight vector is obtained by a characteristic value method, and X ═ xi (xi) is recorded1,ξ2,...,ξn)TIf the maximum eigenvalue corresponds to the eigenvector, the weight vector is calculated as:
the criterion layer weight vector is ω ═ ω (ω ═ ω)1,ω2,...,ωn)TThe weight vector of each scheme layer factor is beta respectively1,β2,...,βlThe degree of influence of each solution on the final goal (C)1,C2,...,Cl)TThe calculation formula of (2) is as follows:
(C1,C2,...,Cl)T＝(β1,β2,...,βl)l×n·(ω1,ω2,...,ωn)T
wherein, CkThe k-th solution accounts for the total target.
Further, in the present invention: step 5, performing dimensionless quantization processing on factors such as delay caused by the road section parameters by adopting the fuzzy decision model, and performing dimensionless quantization processing on the other factors by adopting a multi-attribute decision model;
and the comment set V in the fuzzy decision model is { excellent, good, qualified and poor }, and the factor set U is { U ═ U }1,u2,...,umIs given by rijAnd if the evaluation result is the membership degree of the ith factor to the jth evaluation, the comprehensive evaluation result is as follows:
wherein B is the comprehensive evaluation result, BiFor the quantized values of the factors in the criteria layer,
selecting a membership function to design each factor and form a membership matrix, quantizing by adopting a percentile system method, and selecting a quantization standard as follows: c ═ 100,80,60,40, the quantification of all comments is:
wherein, biIs B2The quantized values of all factors under the criterion layer are obtained to obtain B2The weights of the factors are:
R2＝1-S/100
the method for dimensionless quantization by adopting the multi-attribute decision model comprises the following steps:
wherein x isiIs a factor attribute value, miIs the lower or minimum value of the value range, MiIs the upper limit or the maximum value of the value interval.
Further, in the present invention: and 6, the optimization process is to apply the optimization result of each road section factor to a weight matrix formed by an actual road network and participate in the operation of the fire alarm optimal path planning algorithm, so that the optimal path with the shortest alarm time is obtained.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that:
(1) the method selects the optimal route under the condition of fully considering various factors influencing the fire-fighting alarming time, including basic road section driving time, road section parameter delay, road section condition delay, driver subjective factors and the like, and also relates to weather-related conditions, so that the influencing factors have real-time performance, the considered factors have comprehensiveness, and the obtained result is more reasonable;
(2) according to the method, the shortest time is determined as an evaluation index of the optimal path for fire rescue, the mathematical model based on the AHP is established to evaluate the weight of the road, and the optimal path algorithm is optimized.
Drawings
FIG. 1 is an overall flow chart of the optimization method of the AHP-based fire rescue path selection algorithm to realize path selection;
FIG. 2 is a schematic diagram of an AHP hierarchical model of the optimal route in the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
As shown in fig. 1, the optimization method of the AHP-based fire rescue path selection algorithm provided by the present invention is characterized in that: the method comprises the following steps:
step 1, establishing a hierarchical model optimization target, wherein the hierarchical model structurally comprises a target layer, a criterion layer and a scheme layer, the hierarchical model optimization target is a fire-fighting alarm-giving shortest time path, and the evaluation standard of a road weight O is time;
specifically, the target layer of the hierarchical model includes a road weight O; the criterion layer comprises a section basic travel time B1Delay caused by road section parameters B2Road surfaceDelay of stage condition B3And driver subjective factor B4(ii) a The plan layer includes a road section length C1Average vehicle speed C2Road surface quality C3Road grade C4Road width C5Turning radius C6Grade of road section C7Road section Congestion C8Traffic control C9Weather conditions C10Road familiarity degree C11。
Step 2, constructing a judgment matrix, wherein the judgment matrix is an importance comparison matrix between any two factors influencing the time weight of the fire-fighting police road, and determining each element value in the matrix according to the importance comparison result of the two factors in the same layer;
wherein, the step of constructing the judgment matrix further comprises the following steps:
the judgment matrix is used for comparing the importance of any two factors influencing the time weight of the fire-fighting police road, 1-5 and the reciprocal scale method thereof are used for comparing the importance of any two factors in the same layer and determining each element value in the matrix, and the judgment matrix A is obtained as (a)ij)n×nWherein a isijRepresenting the relative weights of the factor i and factor j quantizations. Taking the judgment matrix of the criterion layer in step 1 as an example, the judgment matrix shown in the following table 1 can be obtained according to the 1-5 scaling method:
TABLE 1 decision matrix for criterion layer
Table 1 shows a general judgment matrix obtained by referring to the existing data for the four factors in the criterion layer under the requirement of the shortest time for fire rescue, which represents the specific gravity of each factor in the criterion layer in the target.
Step 3, carrying out consistency check on the judgment matrix, wherein the check parameters comprise consistency indexes, random consistency indexes and check coefficients, and obtaining whether the judgment matrix conforms to consistency;
wherein, the calculation formula of the inspection parameter is respectively as follows:
CI＝(λmax-n)/(n-1)
RI＝(CI1+CI2+CI3+...+CIn)/n
CR＝CI/RI
wherein CI is consistency index, RI is random consistency index, CR is check coefficient, and lambdamaxThe maximum eigenvalue of the judgment matrix can be obtained by programming and solving matlab; and when CR is less than 0.1, the judgment matrix is considered to be consistent, otherwise, the judgment matrix needs to be changed.
Further, the consistency of the judgment matrix formed in table 1 above is judged, and the λ of the judgment matrix can be obtained by the programmed calculationmax＝4.0104，X＝(0.2505,0.2505,0.8099,0.4674)TThe index of consistency is CI ═ lambdamax-n)/(n-1) 0.0035, and RI (CI) as a random consistency index1+CI2+CI3+...+CIn) The result of the table lookup is 0.90 when n is 4, and therefore, the result of the calculation is 0.0039 when the formula CR is CI/RI, the formula CR is examined<0.1, the decision matrix of Table 1 satisfies the consistency index.
Step 4, calculating a weight vector and a total hierarchical ranking, wherein the total hierarchical ranking is to weight the weight vector of the criterion layer and the weight vector of each scheme layer factor to obtain the influence degree of each scheme on the final target;
wherein, according to the maximum eigenvalue and the corresponding eigenvector obtained in step 3, the weight value occupied by each factor can be obtained by normalization, the weight vector is obtained by eigenvalue method, and X ═ ξ (ξ) is recorded1,ξ2,...,ξn)TIf the maximum eigenvalue corresponds to the eigenvector, the weight vector is calculated as:
both the criterion layer and the scheme layer can obtain the weight value of each element by the above method, and the weight vector of the criterion layer is ω (ω ═ ω)1,ω2,...,ωn)TThe weight vector of each scheme layer factor is beta respectively1,β2,...,βlThe degree of influence of each solution on the final goal (C)1,C2,...,Cl)TThe calculation formula of (2) is as follows:
(C1,C2,...,Cl)T＝(β1,β2,...,βl)l×n·(ω1,ω2,...,ωn)T
wherein, CkAnd finally obtaining a total hierarchical sorting result for the proportion of the kth scheme in the total target.
Step 5, performing dimensionless quantitative processing on parameters influencing the road driving time, wherein the dimensionless quantitative processing comprises the step of performing dimensionless unification on the parameters by adopting a fuzzy decision model and a multi-attribute decision model so as to determine an optimal path selection algorithm optimization model;
the method comprises the following steps that factors such as delay caused by road section parameters are subjected to dimensionless quantization processing by adopting a fuzzy decision model, and the rest factors are subjected to dimensionless quantization processing by adopting a multi-attribute decision model;
and the comment set V in the fuzzy decision model is { excellent, good, qualified and poor }, and the factor set U is { U ═ U }1,u2,...,umIs given by rijAnd if the evaluation result is the membership degree of the ith factor to the jth evaluation, the comprehensive evaluation result is as follows:
wherein B is the comprehensive evaluation result, BiFor the quantized values of the factors in the criteria layer,
"o" stands for the fuzzy operator.
Selecting a membership function to design each factor and form a membership matrix, combining a factor level total sequencing weight obtained by AHP modeling analysis, obtaining a comprehensive judgment result by a fuzzy operator, and adopting a percentile system method for quantization in order to perform more detailed quantitative evaluation on an evaluated object, wherein the selection of a quantization standard is as follows: c ═ 100,80,60,40, the quantification of all comments is:
wherein, biIs B2The quantized values of all factors under the criterion layer are obtained to obtain B2The weights of the factors are:
R2＝1-S/100
for the dimensionless quantization method of the multi-attribute decision model to the factors, because many factors are basically cost-type factors, the method for performing dimensionless quantization by adopting the multi-attribute decision model is as follows:
wherein x isiIs a factor attribute value, miIs the lower or minimum value of the value range, MiIs the upper limit or the maximum value of the value interval.
Further, taking dimensionless dimension of road parameter factors as an example, by referring to relevant data and expert opinions, the parameter information of the part of the preliminarily specified urban road is shown in table 2:
TABLE 2 City road partial parameter information table
| Grade of urban highway | A | II | III | Fourthly |
| Road width (Rice) | 40 | 30 | 16 | 10 |
| Turning radius (Rice) | 30 | 20 | 15 | 10 |
| Corresponding variable | m1 | m2 | m3 | m4 |
| Corresponding comment | Superior food | Good wine | Qualified | Difference (D) |
According to the results of Table 2, it is necessary to determine the membership function, i.e. the membership of each factor to each evaluation criterion, for example, for turning radius, the score "excellent" is greater than or equal to 30 meters, and the score "good" is in the interval of 20 meters to 30 meters, and the membership function is:
and calculating the membership degree of other factors in the same way, finally obtaining a membership degree matrix, quantizing by adopting a percentile system method, and selecting the quantization standard as follows: c ═ 100,80,60,40, the quantification of all comments is:
wherein b isiIs B2The quantized values of the factors under the criterion layer, combined with Table 2 above, can be used to obtain B2The weights of the factors are: r2＝1-S/100。
And 6, optimizing the optimal fire rescue route selection through a path planning algorithm to finally obtain the optimal fire rescue route.
And the optimization process is to apply the optimization result of each road section factor to a weight matrix formed by an actual road network and participate in the operation of the optimal path planning algorithm of the fire alarm, so that the optimal path with the shortest alarm time is obtained.
Specifically, the attribute set in the fire-fighting rescue system is C1,C2,...,CnThe total weight ranking result calculated by the AHP method is omega1,ω2,...,ωnFinally, the dimensionless quantization of the attributes results in r1,r2,...,rnIf T is the time for the firefighting rescue vehicle to travel in a section of route, T can be obtained as:
T＝ω1×r1+ω2×r2+…+ωn×rn
and taking the T as the road network weight of the optimal path selection algorithm, thereby optimizing the algorithm and obtaining the minimum T and the driving route of the vehicle.
It should be noted that the above-mentioned examples only represent some embodiments of the present invention, and the description thereof should not be construed as limiting the scope of the present invention. It should be noted that, for those skilled in the art, various modifications can be made without departing from the spirit of the present invention, and these modifications should fall within the scope of the present invention.
Claims (6)
1. A fire rescue path selection algorithm optimization method based on AHP is characterized in that: the method comprises the following steps:
step 1, establishing a hierarchical model optimization target, wherein the hierarchical model structurally comprises a target layer, a criterion layer and a scheme layer, the hierarchical model optimization target is a fire-fighting alarm-giving shortest time path, and the evaluation standard of a road weight O in the target layer is time;
step 2, constructing a judgment matrix, wherein the judgment matrix is an importance comparison matrix between any two factors influencing the time weight of the fire-fighting police road, and determining each element value in the matrix according to the importance comparison result of the two factors in the same layer;
step 3, carrying out consistency check on the judgment matrix, wherein the check parameters comprise consistency indexes, random consistency indexes and check coefficients, and obtaining whether the judgment matrix conforms to consistency;
step 4, calculating a weight vector and a total hierarchical ranking, wherein the total hierarchical ranking is to weight the weight vector of the criterion layer and the weight vector of each scheme layer factor to obtain the influence degree of each scheme on the final target;
step 5, performing dimensionless quantitative processing on parameters influencing the road driving time, wherein the dimensionless quantitative processing comprises the step of performing dimensionless unification on the parameters by adopting a fuzzy decision model and a multi-attribute decision model so as to determine an optimal path selection algorithm optimization model;
and 6, optimizing the optimal fire rescue route selection through a path planning algorithm to finally obtain the optimal fire rescue route.
2. The optimization method of an AHP-based fire rescue path selection algorithm as defined in claim 1, wherein: the step 2 of constructing the judgment matrix further comprises the following steps:
comparing the importance of two factors in the same layer by using 1-5 and reciprocal scaling method thereof to obtain a judgment matrix A ═ aij)n×nWherein a isijRepresenting the relative weights of the factor i and factor j quantizations.
3. A fire rescue path selection algorithm based on AHP as claimed in claim 1 or 2, wherein: in step 3, the calculation formula of the check parameter is respectively as follows:
CI＝(λmax-n)/(n-1)
RI＝(CI1+CI2+CI3+...+CIn)/n
CR＝CI/RI
wherein CI is consistency index, RI is random consistency index, CR is check coefficient, and lambdamaxThe maximum eigenvalue of the judgment matrix is obtained; and when CR is less than 0.1, the judgment matrix is considered to be consistent, otherwise, the judgment matrix needs to be changed.
4. A fire rescue path selection algorithm optimization method based on AHP as defined in claim 3, wherein: in step 4, the weight vector is obtained by a characteristic value method, and X ═ xi (xi) is recorded1,ξ2,...,ξn)TIf the maximum eigenvalue corresponds to the eigenvector, the weight vector is calculated as:
criterion layer weight directionThe quantity is ω ═ (ω)1,ω2,...,ωn)TThe weight vector of each scheme layer factor is beta respectively1,β2,...,βlThe degree of influence of each solution on the final goal (C)1,C2,...,Cl)TThe calculation formula of (2) is as follows:
(C1,C2,...,Cl)T＝(β1,β2,...,βl)l×n·(ω1,ω2,...,ωn)T
wherein, CkThe k-th solution accounts for the total target.
5. The optimization method of an AHP based fire rescue path selection algorithm as recited in claim 4, wherein: step 5, performing dimensionless quantization processing on factors such as delay caused by the road section parameters by adopting the fuzzy decision model, and performing dimensionless quantization processing on the other factors by adopting a multi-attribute decision model;
and the comment set V in the fuzzy decision model is { excellent, good, qualified and poor }, and the factor set U is { U ═ U }1,u2,...,umIs given by rijAnd if the evaluation result is the membership degree of the ith factor to the jth evaluation, the comprehensive evaluation result is as follows:
wherein B is the comprehensive evaluation result, BiFor the quantized values of the factors in the criteria layer,
selecting a membership function to design each factor and form a membership matrix, quantizing by adopting a percentile system method, and selecting a quantization standard as follows: c ═ 100,80,60,40, the quantification of all comments is:
wherein, biIs B2The quantized values of all factors under the criterion layer are obtained to obtain B2The weights of the factors are:
R2＝1-S/100
the method for dimensionless quantization by adopting the multi-attribute decision model comprises the following steps:
wherein x isiIs a factor attribute value, miIs the lower or minimum value of the value range, MiIs the upper limit or the maximum value of the value interval.
6. A fire rescue path selection algorithm based on AHP as defined in claim 4 or 5, further comprising: and 6, the optimization process is to apply the optimization result of each road section factor to a weight matrix formed by an actual road network and participate in the operation of the fire alarm optimal path planning algorithm, so that the optimal path with the shortest alarm time is obtained.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110298518A (en) * | 2019-07-09 | 2019-10-01 | 四川三秦电气有限责任公司 | A kind of fire rescue route planning method |
-
2020
- 2020-08-21 CN CN202010848364.0A patent/CN112016742A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110298518A (en) * | 2019-07-09 | 2019-10-01 | 四川三秦电气有限责任公司 | A kind of fire rescue route planning method |
Non-Patent Citations (2)
| Title |
|---|
| 张慧;: "基于层次分析法的应急救援最优路径选择分析", 交通标准化, no. 03, pages 2 * |
| 李超鹏;: "一种基于分层分析算法的实时最优消防应急救援路径算法", 武警学院学报, no. 08, pages 1 - 2 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112488401A (en) * | 2020-12-08 | 2021-03-12 | 武汉理工光科股份有限公司 | Fire escape route guiding method and system |
| CN112556714A (en) * | 2020-12-08 | 2021-03-26 | 武汉理工光科股份有限公司 | Fire-fighting rescue intelligent path planning method and system |
| CN112488401B (en) * | 2020-12-08 | 2022-12-02 | 武汉理工光科股份有限公司 | Fire escape route guiding method and system |
| CN112556714B (en) * | 2020-12-08 | 2023-07-07 | 武汉理工光科股份有限公司 | Intelligent path planning method and system for firefighting and rescue |
| CN112763978A (en) * | 2020-12-29 | 2021-05-07 | 中国矿业大学 | Target positioning method for mine post-disaster rescue scene |
| CN113420920A (en) * | 2021-06-22 | 2021-09-21 | 哈尔滨工业大学 | Synchronous decision-making method and system for emergency resource delivery path and traffic control measure |
| CN114936661A (en) * | 2021-11-02 | 2022-08-23 | 哈尔滨工程大学 | Improved A-algorithm based on analytic hierarchy process optimization and used for planning paths of ship cabin personnel in fire environment |
| CN115061815A (en) * | 2022-06-20 | 2022-09-16 | 北京计算机技术及应用研究所 | Optimal scheduling decision method and system based on AHP |
| CN115061815B (en) * | 2022-06-20 | 2024-03-26 | 北京计算机技术及应用研究所 | AHP-based optimal scheduling decision method and system |
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