Evaluation method and device for tunnel fire safety level, medium and program product
By constructing a hierarchical structure model for tunnel fire safety evaluation and combining multiple analytical methods, the tunnel fire safety level is quantified, and the complexity of tunnel fire assessment is solved and the tunnel fire prevention and control and emergency response capabilities are improved.
Patent Information
- Application Number
- CN202510334566.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-20
- Publication Date
- 2025-07-08
AI Technical Summary
The prior art is difficult to accurately quantify the multiple risk factors and their interactions of tunnel fires, resulting in complex tunnel fire safety assessment and difficult to meet the refined needs of modern tunnel safety management.
AHP is used to construct a tunnel fire safety evaluation hierarchical structure model, combining the advantages and disadvantages distance method TOPSIS and grayscale correlation analysis method, and quantify the tunnel fire safety level through the integrated proximity objective function of distance proximity and similar proximity.
Quantitative assessment of tunnel fire safety has been realized, key risk points are identified, resource allocation is optimized, prevention and control capabilities and emergency response efficiency have been improved, and tunnel operation safety has been ensured.
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Figure CN120278511A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of traffic safety technology, and in particular to a method, device, medium and program product for evaluating the fire safety level of a tunnel. Background Art
[0002] With the continuous expansion of underground space utilization worldwide, the construction of urban and mountainous tunnels is increasing, and tunnel safety issues have become a key factor restricting the sustainable development of infrastructure. Fire is one of the most serious potential disasters during tunnel operation. It is sudden and difficult to control. It not only threatens the lives of drivers and passengers, but also may cause huge economic losses and social impacts. According to statistics, in major tunnel accidents in the past, the casualties and property losses caused by fires were extremely heavy. Therefore, it is particularly important to conduct a systematic and scientific evaluation of tunnel fire safety.
[0003] At present, although tunnel fire safety assessment has attracted widespread attention, it still faces challenges such as diverse and inconsistent assessment methods, complex assessment process, strong influence of subjective judgment, and difficulty in fully covering risk factors. Traditional tunnel fire safety assessment methods, such as empirical judgment and single indicator analysis, often fail to accurately quantify the multiple risk factors of tunnel fires and their interactions, and fail to meet the refined needs of modern tunnel safety management. Summary of the invention
[0004] The present application proposes a method, device, medium and program product for evaluating the fire safety level of a tunnel, which can solve the problem of being unable to quantitatively evaluate the fire safety of a tunnel.
[0005] In order to achieve the above objectives, this application adopts the following technical solutions:
[0006] In a first aspect, a method for evaluating a tunnel fire safety level is provided, the method comprising:
[0007] Based on the analytic hierarchy process (AHP), a hierarchical model of tunnel fire safety evaluation is constructed, and a standardized matrix consisting of safety evaluation index factors is obtained.
[0008] The superior and inferior solution distance method TOPSIS is used to obtain the distance closeness determined by the safety evaluation index factor and the positive ideal value and the negative ideal value, and the grayscale correlation analysis method is used to obtain the similarity closeness determined by the safety evaluation index factor and the positive ideal value and the negative ideal value;
[0009] Determining the current tunnel fire safety level quantitative score according to the established nonlinear programming integrated closeness objective function defined by the distance closeness, the similarity closeness and the tunnel fire safety level quantitative score;
[0010] By searching for the corresponding relationship between the quantified score of the tunnel fire safety level and the tunnel fire safety level, the current tunnel fire safety level corresponding to the current quantified score of the tunnel fire safety level is obtained.
[0011] Based on the above technical solution, the analytic hierarchy process (AHP) is used to construct a hierarchical structure model for tunnel fire safety evaluation, the technique for order preference by similarity to ideal solution (TOPSIS) is used to obtain the distance closeness degree, and the grey relational analysis method is used to obtain the similarity closeness degree. A non-linear programming integrated closeness degree objective function defined by the distance closeness degree, similarity closeness degree, and the quantified score of the tunnel fire safety level is used to determine the current quantified score of the tunnel fire safety level. By searching for the corresponding relationship, the corresponding current tunnel fire safety level is obtained. In this way, the quantitative evaluation of tunnel fire safety is realized, which is beneficial to identifying key risk points, optimizing resource allocation, formulating preventive measures, greatly improving the tunnel fire prevention and control ability and emergency response efficiency, ensuring the safety of tunnel operation, and protecting people's lives and property.
[0012] In a possible design manner of the first aspect, the hierarchical structure model for tunnel fire safety evaluation includes an objective layer, a criterion layer, and an index layer. The objective layer is used to represent the tunnel fire safety evaluation, the criterion layer is used to represent the first-level indicators of the tunnel fire safety evaluation, and the scheme layer is used to represent the second-level indicators under the first-level indicators.
[0013] Based on the analytic hierarchy process (AHP), a hierarchical structure model for tunnel fire safety evaluation is constructed, and a standardized matrix composed of safety evaluation index factors is obtained, specifically including:
[0014] The expert scoring method is used to construct a criterion layer index judgment matrix for characterizing the relative importance of the first-level indicators to the objective layer, and an index layer index judgment matrix for characterizing the relative importance of the second-level indicators to the criterion layer. From the criterion layer index judgment matrix and the index layer index judgment matrix, an index weight matrix is obtained.
[0015] According to the tunnel fire safety evaluation index system, the second-level indicators are scored to obtain a safety index standard matrix.
[0016] From the index weight matrix and the safety index standard matrix, a weighted positive matrix is obtained, and the weighted positive matrix is standardized to obtain a standardized matrix composed of safety evaluation index factors, and the safety evaluation index factors correspond to the second-level indicators.
[0017] In a possible design manner of the first aspect, the evaluation method further includes: performing a consistency test on the criterion layer index judgment matrix and the index layer index judgment matrix.
[0018] In a possible design of the first aspect, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is adopted to obtain the distance closeness determined by the safety evaluation index factors, the positive ideal value, and the negative ideal value. The grey relational analysis method is adopted to obtain the similarity closeness determined by the safety evaluation index factors, the positive ideal value, and the negative ideal value, which specifically includes:
[0019] Adopt TOPSIS to obtain the positive ideal value and the negative ideal value of the safety evaluation index factors from the standardized matrix;
[0020] Determine the distance closeness from the first distance between the safety evaluation index factors and the positive ideal value and the second distance between the safety evaluation index factors and the negative ideal value;
[0021] Adopt the grey relational analysis method to obtain the first grey approximate correlation degree between the safety evaluation index factors and the positive ideal value and the second grey approximate correlation degree between the safety evaluation index factors and the negative ideal value;
[0022] Determine the similarity closeness from the first grey approximate correlation degree and the second grey approximate correlation degree.
[0023] In the second aspect, an evaluation device for the safety level of tunnel fires is provided. The evaluation device includes:
[0024] A preprocessing unit for constructing a hierarchical structure model for tunnel fire safety evaluation based on the Analytic Hierarchy Process (AHP) and obtaining a standardized matrix composed of safety evaluation index factors;
[0025] A closeness processing unit for adopting TOPSIS to obtain the distance closeness determined by the safety evaluation index factors, the positive ideal value, and the negative ideal value, and adopting the grey relational analysis method to obtain the similarity closeness determined by the safety evaluation index factors, the positive ideal value, and the negative ideal value;
[0026] An optimization and solution unit for determining the current quantitative score of the tunnel fire safety level according to the established non-linear programming integrated closeness objective function defined by the distance closeness, the similarity closeness, and the quantitative score of the tunnel fire safety level;
[0027] A search unit for obtaining the current tunnel fire safety level corresponding to the current quantitative score of the tunnel fire safety level by searching for the corresponding relationship between the quantitative score of the tunnel fire safety level and the tunnel fire safety level.
[0028] In a third aspect, an electronic device is provided, which includes: a processor, and a memory coupled to the processor, where the memory is configured to store a computer program; and the processor is configured to execute the computer program stored in the memory, so that the electronic device executes the evaluation method according to any possible implementation manner in the first aspect.
[0029] In a fourth aspect, a computer-readable storage medium is provided, including a computer program or instruction, when the computer program or instruction runs on a computer, it causes the computer to execute the evaluation method according to any possible implementation manner in the first aspect.
[0030] In a fifth aspect, a computer program product is provided, including: a computer program or instruction, when the computer program or instruction runs on a computer, it causes the computer to execute the evaluation method according to any possible implementation manner in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or the related art descriptions. Obviously, the drawings in the following descriptions are only some embodiments of the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0032] Figure 1 It is a schematic flowchart of the evaluation method for the tunnel fire safety level provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0033] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further details the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.
[0034] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the flowchart. Terms such as "first" and "second" in the description and claims of the specification and the above drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence.
[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application, and are not intended to limit the present application.
[0036] An embodiment of the present application, as Figure 1 shown, provides a method for evaluating the safety level of tunnel fires, specifically including the following processing procedures:
[0037] Step 1. Based on the AHP method, construct a hierarchical structure model for tunnel fire safety evaluation and classify safety indicators:
[0038] Select evaluation indicators, define the index layer, criterion layer, and target layer, and construct a hierarchical structure model for tunnel fire safety evaluation as shown in Table 1 below; among them, the target layer node represents the evaluation of tunnel fire safety; the criterion layer represents the first-level indicators of tunnel fire safety evaluation, and the first-level indicators include personnel factors, equipment factors, management factors, and environmental factors; the index layer represents the second-level indicators of tunnel fire safety evaluation. Among them, the personnel factors include three second-level indicators: human error of motor vehicle drivers, fire awareness of tunnel management personnel, and fire extinguishing ability of fire fighters; the fire extinguishing ability of fire fighters is an indicator that has not been fully considered in other general evaluation methods. The professional ability and reaction speed of fire fighters are the keys to extinguishing fires. Therefore, by evaluating the actual operation ability of fire fighters, it can be ensured that fires can be quickly and effectively extinguished when they occur. The equipment factors include six second-level indicators: tunnel fire resistance performance, reliability of fire extinguishing facilities, reliability of fire alarm systems, reliability of smoke exhaust facilities, reliability of evacuation facilities, and vehicle failure rate; the vehicle failure rate is an indicator that has not been fully considered in other general evaluation methods and directly affects the traffic conditions in the tunnel, which in turn affects the evacuation and rescue during a fire. Therefore, by evaluating the vehicle failure rate, potential traffic problems can be discovered in advance and chaos during a fire can be reduced. The management factors include four second-level indicators: tunnel fire prevention management system, rationality of emergency plans, fire fighting ability training situation, and implementation situation of emergency drills; the environmental factors include seven second-level indicators: tunnel length, tunnel width, tunnel slope, tunnel height, tunnel wind speed, tunnel traffic conditions, and distance to tunnel rescue stations; the tunnel wind speed is an indicator that has not been fully considered in other general evaluation methods. It directly affects the spread speed and direction of fires. Changes in wind speed may cause the fire to spread rapidly, increasing the difficulty of evacuation and fire fighting. Therefore, introducing the tunnel wind speed indicator can more accurately evaluate the fire risk and formulate more effective prevention and response measures; the distance to the tunnel rescue station is an indicator that has not been fully considered in other general evaluation methods. It directly affects the rescue response time. In an emergency, the length of the distance to the rescue station may determine life and death. Therefore, introducing the distance to the tunnel rescue station indicator can optimize the allocation of rescue resources, improve the speed and efficiency of emergency response, and reduce casualties and property losses.
[0039] Table 1
[0040]
[0041]
[0042] According to the fire safety status of the tunnel, the tunnel fire safety level is divided into five levels: Level I (safe), Level II (relatively safe), Level III (generally safe), Level IV (relatively unsafe), and Level V (unsafe). The safety standards for each index of the tunnel fire are shown in Table 2 below:
[0043] Table 2
[0044]
[0045]
[0046]
[0047] The tunnel fire safety level is divided into five levels: Level I (safe), Level II (relatively safe), Level III (generally safe), Level IV (relatively unsafe), and Level V (unsafe). The corresponding quantitative scores for each safety level are shown in Table 3 below.
[0048] Table 3
[0049] Safety level Score Level I [0.9,1] Level II [0.8,0.9) Level III [0.7,0.8) Level IV [0.6,0.7) Level V [0,0.6)
[0050] Step 2. According to the tunnel fire safety evaluation hierarchical structure model, calculating the index weights includes:
[0051] Using an expert scoring method, each element is compared pairwise, and the score for comparing the i-th element and the j-th element is a ij , which is divided into 9 levels in turn according to the importance degree of the i-th attribute to the j-th attribute: {9, 7, 5, 3, 1, 1 / 3, 1 / 5, 1 / 7, 1 / 9}, where 9 is the most important and 1 / 9 is the least important;
[0052] Through the scoring table of the relative importance of each first-level index to the target layer by experts, the judgment matrix of the criterion layer indicators is obtained;
[0053] Using the MATLAB program, calculate the maximum eigenvalue λ max of the judgment matrix of the criterion layer, and conduct a consistency test. The consistency test usually uses the consistency ratio CR as the test standard:
[0054]
[0055]
[0056] In the formula, RI is the average random consistency index, which is related to the order n of the judgment matrix and can be found in Table 4 below:
[0057] Table 4
[0058]
[0059] After normalization of the eigenvector corresponding to the maximum eigenvalue that meets the inspection requirements, it is denoted as the weight value of each index. When multiple experts score, the weights should be calculated separately and the arithmetic mean should be taken as the final weight value.
[0060] Through the scoring table of the relative importance of each secondary index to the criterion layer by experts, the index judgment matrix of the index layer is obtained;
[0061] Using the MATLAB program, calculate the maximum eigenvalue λ of the criterion layer judgment matrix max , and conduct a consistency test. The consistency test usually uses the consistency ratio CR as the test standard:
[0062]
[0063] Generally, when CR < 0.1, it is considered that the consistency of the calculation result is acceptable; when CR ≥ 0.1, the consistency test fails, and the judgment matrix should be reconstructed for calculation until the calculation result can be accepted when CR < 0.1;
[0064] Normalize the eigenvector corresponding to the maximum eigenvalue that meets the inspection requirements, and denote it as the weight value of each index. When multiple experts score, the weights should be calculated separately and the arithmetic mean should be taken as the final weight value;
[0065] Multiply the weight value of the first-level index to the target layer by the weight value of the second-level index to the criterion layer to obtain the weight value of each second-level index in the index layer to the target layer, and finally obtain the index weight matrix ω = [ω 11 ω 12 … ω 47 , ω 11 is the first index matrix element, ω 12 is the second index matrix element, ω 47 is the 20th index matrix element.
[0066] Step 3. According to a tunnel fire safety evaluation index system, score each safety index of the tunnel to obtain the safety index standard matrix:
[0067]
[0068] b 11 is the scoring result of the first index, b 12 is the scoring result of the second index, b 47 is the scoring result of the 20th index. According to the safety index standard matrix C and the index weight matrix ω = [ω calculated according to the analytic hierarchy process and the entropy weight method11 ω 12 … ω 47 ], ω 11 is the first index matrix element, ω 12 is the second index matrix element, ω 47 is the 20th index matrix element. Obtain the weight positive matrix X = B×ω = [x 11 x 12 … x 47 ], x 11 is the first weight positive matrix element, x 12 is the second weight positive matrix element, x 47 is the 20th weight positive matrix element. The matrix obtained by normalizing the weight positive matrix B is denoted as Z. Each safety factor evaluation index in the normalized matrix Z is z i is:
[0069]
[0070] The distances between the positive ideal value and the negative ideal value of all safety evaluation indexes are respectively denoted as Z + and Z - , where:
[0071] Z + = max[z1 z2 … z 20 ]
[0072] Z - = min[z1 z2 … z 20 ]
[0073] Define the grey similarity correlation degree between the tunnel fire safety evaluation index and the positive ideal value:
[0074]
[0075] In the formula, S is the area size enclosed between the Z i sequence curve and the Z + sequence curve;
[0076]
[0077] Define the grey similarity correlation degree between the tunnel fire safety evaluation index and the negative ideal value:
[0078]
[0079] In the formula, S is the area size enclosed between the Z i sequence curve and the Z-sequence curve;
[0080]
[0081] Define the similarity proximity γ i :
[0082]
[0083] Define the distance D between the tunnel fire safety evaluation index and the positive ideal value + :
[0084]
[0085] Wherein, ω j is the combined weight, let ω j = 1;
[0086] Define the distance D between the tunnel fire safety evaluation index and the negative ideal value - :
[0087]
[0088] Wherein, ω j is the combined weight, let ω j = 1;
[0089] Define the distance proximity D i :
[0090]
[0091] Establish a nonlinear programming model and define the integrated proximity:
[0092]
[0093] s.t. min(D i , γ i ) << I i << max(D i , γ i )
[0094] Wherein, I i is the quantitative score of the tunnel fire safety level.
[0095] Conduct a safety level evaluation based on the quantitative score of the tunnel fire safety level.
[0096] Through the above technical solution, the integrated proximity is proposed to overcome the limitations of a single evaluation method, and a more comprehensive evaluation result is obtained by considering both the similarity proximity and the distance proximity dimensions. Among them, the distance proximity reflects the degree of proximity between the two subsets, while the similarity proximity reflects the degree of similarity in the distribution of each element in the two subsets.
[0097] If only the similarity closeness degree is used, although it can reflect the shape similarity degree and the consistency of the change trend between the evaluation object and the ideal solution, it does not consider the distance between the actual attribute value and the ideal solution; while when only the distance closeness degree is used, only the absolute distance between the evaluation object and the ideal solution is considered, but the coordination and overall change trend among the evaluation indicators are ignored.
[0098] Therefore, to solve this problem, the integrated closeness degree realizes the organic combination of the two dimensions of similarity and distance by establishing a nonlinear programming model. The goal is to make the integrated closeness degree close to both the similarity closeness degree and the distance closeness degree. The integrated closeness degree is set specifically by minimizing the objective function and setting constraint conditions. Compared with only using a single closeness degree calculation method, the sorting result obtained by the integrated closeness degree calculation method is more reasonable. This is because the similarity closeness degree ignores the distance size, the distance closeness degree ignores the distribution similarity, and the integrated closeness degree fully utilizes the attribute value information by considering these factors simultaneously. Therefore, in practical applications such as tunnel fire safety assessment, the integrated closeness degree can more accurately reflect the actual level and coordination of various safety indicators.
[0099] The embodiment of the present invention also provides an evaluation device for the tunnel fire safety level, and the evaluation device includes:
[0100] A preprocessing unit, configured to construct a tunnel fire safety evaluation hierarchical structure model based on the Analytic Hierarchy Process (AHP) and obtain a standardized matrix composed of safety evaluation index factors;
[0101] A closeness degree processing unit, configured to obtain the distance closeness degree determined by the safety evaluation index factors and the positive ideal value and the negative ideal value by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and obtain the similarity closeness degree determined by the safety evaluation index factors and the positive ideal value and the negative ideal value by using the grey relational analysis method;
[0102] An optimization solving unit, configured to determine the current tunnel fire safety level quantization score according to the established nonlinear programming integrated closeness degree objective function defined by the distance closeness degree, the similarity closeness degree, and the tunnel fire safety level quantization score;
[0103] A searching unit, configured to obtain the current tunnel fire safety level corresponding to the current tunnel fire safety level quantization score by searching for the corresponding relationship between the tunnel fire safety level quantization score and the tunnel fire safety level.
[0104] The embodiment of the present invention also provides an electronic device, including: a processor, and a memory coupled to the processor, where the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory so that the electronic device executes the evaluation method as described above.
[0105] The electronic device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The electronic device may include, but is not limited to, a processor and a memory.
[0106] The so-called processor can be a Central Processing Unit (CPU), or can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor is the control center of the electronic device, and connects various parts of the entire device through various interfaces and circuits.
[0107] The memory can be used to store the computer program. The processor realizes various functions of the electronic device by running or executing the computer program stored in the memory and calling the data stored in the memory.
[0108] The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.
[0109] An embodiment of the present invention also provides a storage medium, which is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.
[0110] An embodiment of the present invention also provides a computer program product, including: a computer program or instruction. When the computer program or instruction runs on a computer, the computer is enabled to execute the evaluation method in any possible implementation manner in the first aspect.
[0111] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art of the present technology, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.
Claims
1. A method for evaluating the safety level of tunnel fires, characterized in that, The method includes: Based on the Analytic Hierarchy Process (AHP), construct a hierarchical structure model for tunnel fire safety evaluation, and obtain a standardized matrix composed of safety evaluation index factors; Using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), obtain the distance closeness determined by the safety evaluation index factors, positive ideal value, and negative ideal value, and using the grey relational analysis method, obtain the similarity closeness determined by the safety evaluation index factors, positive ideal value, and negative ideal value; According to the established non-linear programming integrated closeness objective function defined by the distance closeness, the similarity closeness, and the quantified score of the tunnel fire safety level, determine the quantified score of the current tunnel fire safety level; By looking up the correspondence between the quantified score of the tunnel fire safety level and the tunnel fire safety level, obtain the current tunnel fire safety level corresponding to the quantified score of the current tunnel fire safety level.
2. The evaluation method according to claim 1, characterized in that, The hierarchical structure model for tunnel fire safety evaluation includes an objective layer, a criterion layer, and an index layer. The objective layer is used to represent the tunnel fire safety evaluation, the criterion layer is used to represent the first-level indicators of the tunnel fire safety evaluation, and the scheme layer is used to represent the second-level indicators under the first-level indicators. Based on the Analytic Hierarchy Process (AHP), construct a hierarchical structure model for tunnel fire safety evaluation, and obtain a standardized matrix composed of safety evaluation index factors, specifically including: Using the expert scoring method, construct a criterion layer index judgment matrix for characterizing the relative importance of the first-level indicators to the objective layer, and an index layer index judgment matrix for characterizing the relative importance of the second-level indicators to the criterion layer. From the criterion layer index judgment matrix and the index layer index judgment matrix, obtain an index weight matrix; According to the tunnel fire safety evaluation index system, score the second-level indicators to obtain a safety index standard matrix; From the index weight matrix and the safety index standard matrix, obtain a weight positive matrix, and standardize the weight positive matrix to obtain a standardized matrix composed of safety evaluation index factors, where the safety evaluation index factors correspond to the second-level indicators.
3. The evaluation method according to claim 2, characterized in that The evaluation method further includes: performing a consistency test on the criterion layer index judgment matrix and the index layer index judgment matrix.
4. The evaluation method according to claim 1, characterized in that, Using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), obtain the distance closeness determined by the safety evaluation index factors, positive ideal value, and negative ideal value, and using the grey relational analysis method, obtain the similarity closeness determined by the safety evaluation index factors, positive ideal value, and negative ideal value, specifically including: Using TOPSIS, obtain the positive ideal value and negative ideal value of the safety evaluation index factors from the standardized matrix; Determine the distance closeness from the first distance between the safety evaluation index factor and the positive ideal value and the second distance between the safety evaluation index factor and the negative ideal value; Using the grey relational analysis method, obtain the first grey approximate correlation degree between the safety evaluation index factor and the positive ideal value and the second grey approximate correlation degree between the safety evaluation index factor and the negative ideal value; Determine the similarity closeness degree based on the first gray approximate correlation degree and the second gray approximate correlation degree.
5. An evaluation device for the safety level of tunnel fires, characterized in that, The evaluation device includes: A preprocessing unit, configured to construct a hierarchical structure model for tunnel fire safety evaluation based on the Analytic Hierarchy Process (AHP), and obtain a standardized matrix composed of safety evaluation index factors; A closeness degree processing unit, configured to use the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to obtain the distance closeness degree determined by the safety evaluation index factors, the positive ideal value, and the negative ideal value, and use the gray correlation analysis method to obtain the similarity closeness degree determined by the safety evaluation index factors, the positive ideal value, and the negative ideal value; An optimization solving unit, configured to determine the current quantified score of the tunnel fire safety level according to the established non-linear programming integrated closeness degree objective function defined by the distance closeness degree, the similarity closeness degree, and the quantified score of the tunnel fire safety level; A searching unit, configured to obtain the current tunnel fire safety level corresponding to the current quantified score of the tunnel fire safety level by searching for the corresponding relationship between the quantified score of the tunnel fire safety level and the tunnel fire safety level; 6. An electronic device, characterized in that, The electronic device includes: a processor and a memory coupled to the processor, The memory is configured to store a computer program; The processor is configured to execute the computer program stored in the memory, so that the electronic device executes the evaluation method according to any one of claims 1 to 4.
7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program or instruction, and when the computer program or instruction runs on a computer, the computer is caused to execute the evaluation method according to any one of claims 1 to 4.
8. A computer program product, characterized in that, The computer program product includes: a computer program or instruction, and when the computer program or instruction runs on a computer, the computer is caused to execute the evaluation method according to any one of claims 1 - 4.