Early warning and evaluating method for flood disaster of subway station

Abstract

The invention relates to a subway station flood disaster early warning evaluation method, which comprises the following steps of S1, establishing a subway flood disaster early warning data real-time dynamic evaluation system, and establishing an expert group; s2, proposing an early warning index grading quantitative characterization method, and constructing a flood disaster potential severity index system, a flood disaster vulnerability index system and a flood disaster prevention and disaster resistance index system; s3, researching the relationship between the average hourly rainfall capacity and the ponding depth, and realizing conversion between rainfall meteorological information and a ponding depth predicted value; s4, respectively evaluating the vulnerability and the disaster prevention and resistance capability by adopting AHP and TOPSIS methods, and calculating the early warning index weight and the relative closeness; s5, calculating a comprehensive early warning value based on the coupling influence relationship of the three types of indexes; and S6, determining an early warning grade based on the comprehensive early warning value, and realizing dynamic graded early warning. According to the method, a grading early warning index system is constructed, a grading quantitative characterization method of each index is provided, and theoretical support and actual guidance can be provided for advanced prediction early warning and accurate response disposal of subway flood disasters.

Description

Technical Field

[0001] The present invention belongs to the field of flood disaster warning, and particularly relates to a method for warning and evaluating flood disasters in subway stations. Background Art

[0002] The development of flood disasters in subway stations is a dynamic process, affected by various factors such as rainfall intensity, duration, and surrounding environmental conditions, which easily leads to water seepage into the station interior, causing equipment damage and difficulties in personnel evacuation. Due to the closed structure of subway stations and high passenger density, once a flood disaster occurs, it may cause serious economic losses and casualties. Currently, the research on flood disasters mainly focuses on the pre-disaster assessment stage, relying on historical data and the inherent attributes of stations for static analysis, and judging flood risks by analyzing basic conditions such as terrain height, drainage capacity, and flood control facilities. However, most of these methods rely on expert experience and subjective judgment criteria, lacking real-time monitoring and dynamic assessment of the disaster development process, and it is difficult to meet the high-precision requirements for disaster prediction in the complex environment of modern cities. In addition, the existing technology fails to effectively combine real-time meteorological warning information with the disaster assessment system, resulting in a lag in warning response and insufficient accuracy.

[0003] Existing assessment methods generally lack scientific quantification criteria, unable to ensure the objectivity and consistency of warning indicators, resulting in unreliable risk assessment results. At the same time, the existing technology lacks a mechanism for combining real-time meteorological warnings with the water accumulation depth prediction model, making it difficult to timely reflect the development trend of disasters and quickly respond to emergencies. In addition, the assessment system lacks systematicness and comprehensiveness, only focusing on single factors or attributes, and not fully considering the coupling relationship between the potential severity, vulnerability, and disaster prevention and resistance capabilities of flood disasters. Therefore, there is an urgent need to establish a dynamic and scientific flood disaster warning and assessment method, construct a complete warning index system and hierarchical quantification representation method, combine real-time monitoring data and meteorological information, and realize the dynamic assessment and hierarchical warning of flood disaster risks, so as to provide scientific and reliable decision-making support and emergency guidance. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a method for warning and evaluating flood disasters in subway stations, aiming to solve the technical problems that the flood disaster risk assessment is not objective, mainly based on the subjective judgment criteria of experts, and the evaluation indicators are not scientifically quantified.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] A method for warning and evaluating flood disasters in subway stations includes the following steps:

[0007] S1, establish a real-time dynamic assessment system for subway flood disaster warning data, and form an expert group of more than three people;

[0008] S2. Propose a method for grading and quantifying the representation of early warning indicators based on a real-time dynamic evaluation system. The evaluation system includes primary indicators and secondary indicators. The primary indicators include the potential severity of flood disasters, vulnerability factors of flood disasters, and disaster prevention and resistance ability factors of flood disasters.

[0009] S4. Based on the relationship research between the average hourly rainfall, meteorological warning level and water accumulation depth, realize the conversion of rainfall meteorological information and the predicted value of flood disaster water accumulation depth.

[0010] S7. Respectively propose evaluation methods for vulnerability and disaster prevention and resistance ability based on AHP and TOPSIS, determine the weight values of early warning indicators and calculate the relative closeness degree.

[0011] S5. Based on the coupling influence relationship of early warning indicators in three aspects: the potential severity of flood disasters, vulnerability, and disaster prevention and resistance ability, calculate the comprehensive early warning value of subway station flood disasters.

[0012] S6. Determine the early warning level based on the comprehensive early warning value of flood disasters.

[0013] Preferably, the calculation formula for the comprehensive early warning value of subway station flood disasters is:

[0014]

[0015] Among them, PV represents the early warning value of subway flood disasters; D water represents the measured or predicted value of the flood water accumulation depth around the subway station; H step(min) represents the minimum value of the step height of each entrance and exit of the subway station; V T represents the graded assignment of water accumulation time; E vulnerability represents the relative closeness degree of the vulnerability of subway station flood disasters; E resistance represents the relative closeness degree of the disaster prevention and resistance ability of subway station flood disasters.

[0016] Preferably, the indicators of the potential severity of flood disasters include water accumulation depth, water accumulation time, and meteorological warning level.

[0017] Preferably, the water accumulation depth and water accumulation time can be judged by measured values and predicted values. The measured values include the actual values of the water accumulation depth and water accumulation time around the subway station, and the predicted values are the water accumulation depths converted based on meteorological warning information. The predicted values and measured values are divided into early warning levels using the same standard.

[0018] Preferably, the quantification of flood disaster vulnerability includes the quantitative analysis of the terrain and elevation where the subway station is located, the step height of the entrance and exit, the type of the entrance and exit, the passenger flow, and the density of the municipal drainage pipe network.

[0019] Preferably, the quantification of the flood disaster prevention and resistance ability includes the quantitative analysis of the station drainage capacity, flood control facility equipment, water level monitoring coverage rate, flood control emergency drills and training, and the completeness of the flood control emergency plan.

[0020] Preferably, step S3 includes:

[0021] S31. Study the relationship between the average hourly rainfall and the water accumulation depth. By collecting the original data of the relationship between different rainfall amounts and water accumulation depths, analyze the data using the method of polynomial fitting to obtain the calculation formula for the relationship between rainfall and water accumulation depth;

[0022] S32. Grade and assign values to the severity of the water accumulation time;

[0023] S33. Numerical conversion of rainfall meteorological information and flood disaster water accumulation depth information. Convert the rainfall warning threshold in the meteorological warning level into the average hourly rainfall, and substitute it into the calculation formula for the relationship between rainfall and water accumulation depth in step S31 to obtain the predicted water accumulation depth under different meteorological warning levels.

[0024] Preferably, the calculation formula for the relationship between rainfall and water accumulation depth:

[0025] y = -0.03 + 0.01514x - 0.00007x 2

[0026] where x is the average hourly rainfall in mm, and y is the water accumulation depth in m.

[0027] Preferably, step S4 includes step S41, obtaining the weight values of the subway station vulnerability index and the flood prevention and resistance ability index, and constructing a complete expert judgment matrix. The matrix form is:

[0028]

[0029] where n represents the number of indicators at this level; a ij represents the relative importance scale of indicator i compared to indicator j;

[0030] The calculation formulas for the maximum eigenvalue and the consistency index CR of the matrix are:

[0031]

[0032] where W represents the eigenvector of the judgment matrix, and W i is the weight value obtained by normalizing the judgment matrix. RI can be obtained by looking up the table according to the matrix order. The CR values of each expert's data are all less than 0.1, and the judgment matrix passes the consistency test.

[0033] Preferably, step S4 includes step S42, which determines the warning level by using the TOPSIS method, and the process is as follows:

[0034] First, construct a weighted decision matrix based on the warning index weights:

[0035] C ij = ω j * a ij (i = 1, 2,..., m; j = 1, 2,..., n) where ω j represents the index weight; a ij represents the j-th evaluation index value of the i-th evaluation object;

[0036] Secondly, determine the positive ideal solution and negative ideal solution of each warning evaluation index:

[0037]

[0038] Thirdly, calculate the distances between the evaluation object and the positive and negative ideal solutions respectively by using the Euclidean distance:

[0039]

[0040] Finally, calculate the relative closeness of the evaluation object:

[0041]

[0042] Compared with the prior art, the advantages of the present invention are as follows: The present invention aims at the early warning of flood disasters in subway stations, combines real-time disaster dynamic information and the inherent attribute parameters of stations, and proposes a scientific calculation method and discrimination criteria for the warning level of flood disasters in subway stations, effectively solving the problems of insufficient dynamic evaluation, strong subjectivity, and lack of quantitative criteria in the prior art. The present invention constructs a complete warning index system and a hierarchical quantitative characterization method, providing a scientific basis and technical support for the risk assessment of flood disasters in subway stations, and further improving the accuracy and response speed of disaster warnings. From three aspects of the potential severity of flood disasters, the vulnerability of stations, and the disaster prevention and resistance capabilities, the present invention establishes a hierarchical warning index system for flood disasters in subway stations covering 13 warning indicators, and proposes a hierarchical quantitative characterization method for each indicator, overcoming the deficiencies of strong subjectivity and lack of quantitative basis in the evaluation criteria of the prior art. In addition, through literature research and numerical fitting analysis, the relationship between hourly rainfall and water accumulation depth is studied, a prediction method for water accumulation depth based on measured data and meteorological forecast information is proposed, and it supports dynamically correcting the calculation formula according to the characteristics of urban waterlogging and waterlogging point data to ensure the accuracy and adaptability of the prediction results. Based on the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), the present invention further proposes an evaluation method for the vulnerability and disaster prevention and resistance capabilities of flood disasters in subway stations, and comprehensively considers the coupling effect between the dynamic information of the disaster situation and the inherent attributes of the stations, establishing a dynamic hierarchical warning calculation method and discrimination criteria, which can realize the real-time dynamic assessment and accurate hierarchical warning of the risk of flood disasters in subway stations. In addition, the present invention analyzes the key influence of the step height of the entrance and exit on the vulnerability of flood disasters in the station, clearly proposes that low-step height stations should take sufficient protection compensation measures, and emphasizes the importance of the reasonable layout and dynamic monitoring of water level monitoring sensors in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic flow chart of the warning and evaluation method for flood disasters in the subway station of the present invention;

[0044] Figure 2 It is a schematic flow chart of the conversion of rainfall meteorological information and predicted values of water accumulation depth in flood disasters of the present invention;

[0045] Figure 3 It is a schematic flow chart of determining the weight value of warning indicators and calculating the relative closeness degree of the present invention;

[0046] Figure 4 It is a graph of the relationship between rainfall and water accumulation depth and the polynomial fitting result of the present invention;

[0047] Figure 5 It is the hierarchical warning response strategy for flood disasters in the subway station of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the present invention.

[0049] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to give a full understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the present application.

[0050] As Figures 1 - 5 shown, this embodiment discloses a method for early warning and assessment of flood disasters in subway stations, including the following steps:

[0051] S1. Establish a real-time dynamic assessment system for subway flood disaster warning data and form an expert group of more than three people;

[0052] S2. Propose a method for grading and quantifying the warning indicators based on the real-time dynamic assessment system. The assessment system includes primary indicators and secondary indicators. The primary indicators include the potential severity of flood disasters, vulnerability factors of flood disasters, and disaster prevention and resistance ability factors of flood disasters;

[0053] S3. Based on the research on the relationship between the average hourly rainfall, meteorological warning level, and water accumulation depth, realize the conversion of rainfall meteorological information and the predicted value of flood disaster water accumulation depth;

[0054] S4. Propose evaluation methods for vulnerability and disaster prevention and resistance ability based on AHP and TOPSIS respectively, determine the weight values of the warning indicators, and calculate the relative closeness;

[0055] S5. Calculate the comprehensive warning value of subway station flood disasters based on the coupling influence relationship of the three warning indicators of the potential severity of flood disasters, vulnerability, and disaster prevention and resistance ability;

[0056] S6. Determine the warning level based on the comprehensive warning value of the flood disaster.

[0057] Further, in step S5, the calculation formula for the comprehensive warning value of subway station flood disasters is:

[0058]

[0059] Among them, PV represents the early warning value of subway flood disasters; D water represents the measured or predicted value of the floodwater accumulation depth around the subway station; H step(min) represents the minimum value of the step height at each entrance and exit of the subway station; V T represents the graded assignment of the waterlogging time; E vulnerability represents the relative closeness of the vulnerability of the subway station to flood disasters; E resistance represents the relative closeness of the flood prevention and disaster resistance ability of the subway station to flood disasters.

[0060] In this embodiment, the indicators of the potential severity of flood disasters include the water accumulation depth, the water accumulation time, and the meteorological early warning level. The water accumulation depth is a key indicator for judging the severity of urban waterlogging. When the water accumulation depth around the subway station is relatively high, it may cause rainwater to pour into the station, thus affecting the passage of people in the subway station and the risk of water entering the station. Therefore, it is of great significance to reasonably judge the water accumulation depth and its impact on the safety of the station.

[0061] According to the "Code for Design of Subways" (GB 50157), the ground elevation of subway entrances and exits should be 30 - 45 cm higher than the outdoor ground at that place. Considering that the sidewalk height is usually 15 - 20 cm higher than the road, it can be inferred that when the water accumulation depth is higher than 60 cm, there is a relatively high risk of water entering the subway. This judgment provides an important benchmark condition for the risk assessment of subway flood disasters. According to the risk level classification standards of the "Technical Code for Urban Waterlogging Prevention and Control" (GB 51222 - 2017) and the "Beijing Urban Waterlogging Risk Map", the judgment threshold of the water accumulation depth is further refined into 15 cm, 27 cm, 40 cm, and 60 cm, so as to correspond different risk levels to specific water accumulation depth ranges, thereby clarifying the criteria for warning level classification. At the same time, referring to the water accumulation time evaluation standard in the "Guide for Urban Waterlogging Treatment in Anhui Province" (Jiancheng Han

[2023] No. 14), taking the central urban area as an example, the water accumulation time threshold is classified into 0.5 hours, 1 hour, and 1.5 hours to reflect the impact of the water accumulation duration on the severity of disasters. These standardized threshold classifications can improve the applicability and consistency of the water accumulation depth and time in risk assessment.

[0062] The rainstorm warning signal is a warning message issued by the meteorological department based on meteorological monitoring data before the arrival of a rainstorm, and it is an important factor for predicting and judging the severity of meteorological disasters. The rainstorm warning signal is divided into four levels, represented by blue, yellow, orange, and red respectively. Among them, the blue warning standard is that the rainfall will reach 50 mm or more within 12 hours, or it has reached 50 mm and the rainfall may continue; the yellow warning standard is that the rainfall will reach 50 mm or more within 6 hours, or it has reached 50 mm and the rainfall may continue; the orange warning standard is that the rainfall will reach 50 mm or more within 3 hours, or it has reached 50 mm and the rainfall may continue; the red warning standard is that the rainfall will reach 100 mm or more within 3 hours, or it has reached 100 mm and the rainfall may continue. Based on these warning signals, the development trend of rainfall can be quickly obtained, providing input parameters for the prediction of waterlogging depth and realizing the dynamic adjustment of warning responses.

[0063] The potential severity of flood disasters at subway stations can be judged from two aspects: the measured value and the predicted value of waterlogging. The measured values mainly include the actual values of the waterlogging depth and waterlogging time around the subway station, which can be obtained in real time through on-site measurement or sensor monitoring, providing direct support for disaster assessment. The predicted value is calculated based on meteorological warning information through the conversion formula between rainfall and waterlogging depth, which can make up for the lack of on-site monitoring data. To further improve the prediction accuracy, by statistically analyzing the correlation data between rainfall and waterlogging depth, a numerical fitting method is used to establish a mathematical model between rainfall and waterlogging depth, and according to the formula, the rainfall warning threshold in the meteorological warning level is converted into the average hourly rainfall, and then substituted into the fitting formula to calculate the predicted waterlogging depth under different meteorological warning levels, so as to realize the numerical conversion of rainfall meteorological information and flood disaster waterlogging depth information, providing a basis for real-time warning.

[0064] In practical applications, the waterlogging depth (C1) and waterlogging time (C2) determined based on sensor monitoring or actual measurement can directly reflect the on-site flood situation and help quickly judge the waterlogging risk. At the same time, based on the predicted rainfall information in the meteorological forecast warning, the predicted waterlogging depth (C3) is calculated through the conversion calculation formula between rainfall and waterlogging depth. The prediction value and the measured value use the same standard to divide the warning level to ensure the consistency and comparability of the evaluation results of the two. For example, when the predicted rainfall reaches 50 mm, the predicted waterlogging depth can be calculated according to the formula and included in the same warning level standard as the measured depth for evaluation, so as to realize a risk assessment and warning mechanism that combines dynamic monitoring and prediction.

[0065] In this embodiment, the quantification of flood disaster vulnerability includes the quantitative analysis of the terrain and elevation of the subway station, the height of the entrance steps, the type of entrance, the passenger flow, and the density of the municipal drainage pipe network.

[0066] The terrain where the subway station is located has an important impact on waterlogging. According to the inherent design parameters, the terrain and elevation are classified and quantitatively analyzed, and the elevation of the subway station is compared with the first threshold, the second threshold, and the third threshold. When the terrain and elevation (C4) of the subway station are significantly lower than the surrounding terrain, it is easy to form a water-collecting area, causing rainwater to gather in the low-lying areas. The index is given a dimensionless value of 10, indicating a relatively high flood risk; when C4 is similar to the surrounding terrain, although the flood spreads relatively evenly, there is still a certain risk of waterlogging, and the index is given a dimensionless value of 6; when C4 is slightly higher than the surrounding terrain, the drainage conditions are better, which is conducive to the evacuation of waterlogging, and the index is given a dimensionless value of 3; when C4 is significantly higher than the surrounding terrain, it has an obvious flood prevention advantage, and waterlogging is not easy to backflow, and the index is given a dimensionless value of 1, indicating a relatively low flood risk. As the elevation of the entrance and exit increases, the risk of rainwater backflow into the subway station gradually decreases, fully reflecting the key role of elevation in the vulnerability analysis of flood disasters.

[0067] The height of the entrance and exit steps (C5) determines the ease of rainwater and floodwater pouring into the underground station during heavy rain. According to the inherent design parameters, the height of the entrance and exit steps is classified and quantitatively analyzed. When the height of C5 is higher than the surrounding ground by less than 30 cm, it indicates that waterlogging may easily cross the steps and enter the station, and the index is given a dimensionless value of 10, and key prevention measures for rainwater backflow are required; when the height of C5 is higher than the surrounding ground by 30 cm to 45 cm, the steps have a certain blocking ability for waterlogging, but there is still a medium risk, and the index is given a dimensionless value of 6; when the height of C5 is higher than the surrounding ground by more than 45 cm, the step height is relatively large, effectively blocking rainwater, and the index is given a dimensionless value of 3, with strong flood prevention ability. As the height of the entrance and exit steps increases, the difficulty of rainwater backflow increases, thus effectively reducing the flood disaster risk of the subway station. Therefore, the height of the entrance and exit steps, as an important index for evaluating the vulnerability of flood disasters, can provide a scientific basis for risk assessment and emergency protection design.

[0068] The type of the station entrance and exit (C6) further affects the flood prevention performance and the risk level of flood disasters. The types of entrance and exits are generally divided into four types: open type, semi-closed type, closed type, and hidden type. Among them, the open-type entrance and exit has no roof cover and no surrounding enclosure, and is completely exposed, making it most vulnerable to heavy rain, and the index is given a dimensionless value of 10; the semi-closed type entrance and exit is provided with a roof cover, which has a certain blocking effect on rainwater and relatively strong ability to resist heavy rain disasters, and the index is given a dimensionless value of 6; the closed type entrance and exit is provided with a roof cover and three-sided enclosures, and the protection performance is further enhanced, and the index is given a dimensionless value of 3; the hidden type entrance and exit is directly set inside large buildings, such as shopping malls or high-speed railway stations, with the best protection conditions, and the index is given a dimensionless value of 1. Through the reasonable classification and quantitative analysis of the entrance and exit types, the performance in flood prevention can be effectively evaluated, providing a scientific reference for the vulnerability assessment of subway station flood disasters.

[0069] The in-station real-time passenger flow status (C7) has an important impact on the flood warning and emergency response process of the subway station. According to the sensor monitoring data, the passenger flow can be judged in real time and a hierarchical quantitative analysis can be carried out. When the passenger flow in the subway station is extremely crowded, it is difficult to evacuate people, and the safety risk is relatively high in case of emergency, and the index is given a dimensionless value of 10; when the passenger flow is large, although the evacuation is relatively controllable, there is still a certain pressure, and the index is given a dimensionless value of 6; when the passenger flow is moderate, the emergency evacuation conditions are better, and the index is given a dimensionless value of 3; when the passenger flow is small, the emergency response is relatively flexible, and the index is given a dimensionless value of 1. As the passenger flow decreases, the vulnerability of the subway station gradually decreases, further highlighting the important role of passenger flow monitoring in early warning assessment. Therefore, real-time monitoring and analysis of passenger flow changes are of great significance for formulating flood disaster emergency plans.

[0070] The density of the municipal drainage pipe network (C8) directly affects the severity of waterlogging and the drainage efficiency. The higher the density of the drainage pipe network, the faster the accumulated water formed by short-term heavy rainfall can be drained, thereby reducing the flood disaster risk of the subway station. According to the density level, the density of the municipal drainage pipe network is divided into four levels: low, general, high, and very high, and C8 is given dimensionless values of 10, 6, 3, and 1 respectively. Among them, a low density indicates insufficient drainage capacity, slow drainage speed of accumulated water, and the highest risk; a general density can meet the daily drainage needs, but there are still certain hidden dangers in the face of heavy rainfall; a high density has a strong drainage capacity and can quickly respond to sudden water accumulation conditions; a very high density drainage system can achieve rapid drainage and effectively reduce the flood risk. Therefore, the quantitative analysis of the density of the municipal drainage pipe network provides an important basis for the assessment of the external drainage environment of the subway station, which is convenient for further optimizing the configuration of flood control facilities and emergency management strategies.

[0071] In this embodiment, the quantification of flood disaster prevention and resistance capabilities includes the quantitative analysis of the station drainage capacity, flood control facility equipment, water level monitoring coverage rate, flood control emergency drills and training, and the completeness of flood control emergency plans.

[0072] The drainage capacity of the station (C9) is a key indicator for measuring the subway station's response to heavy rain and waterlogging. According to the regulations on the drainage capacity of rainwater pump stations, drainage ditches, and drainage pipelines in the "Code for Design of Metro" (GB50157 - 2013), the drainage system is classified and quantified based on the design standards and surplus capacity. When the rainwater pump house is designed according to the 100-year rainstorm recurrence period and the surplus capacity is greater than 30% when all equipment is fully started, it indicates that the station's drainage capacity reaches the optimal level and can maintain a high drainage efficiency under extreme weather conditions. The indicator is given a dimensionless value of 10; when the rainwater pump house is designed according to the 100-year rainstorm recurrence period but the surplus capacity is low, the drainage capacity decreases slightly, and the indicator is given a dimensionless value of 6; when the rainwater pump house is designed according to the 50-year rainstorm recurrence period and the surplus capacity is greater than 30% when all equipment is fully started, the drainage system still has a certain emergency handling capacity, and the indicator is given a dimensionless value of 3; when the design of the rainwater pump house is less than the 50-year rainstorm recurrence period, the drainage capacity is weak and it is difficult to cope with extreme weather conditions, and the indicator is given a dimensionless value of 1. Through the above classification and quantification, the drainage capacity of the subway station can be effectively evaluated, and an improvement direction can be provided for enhancing the drainage capacity.

[0073] The availability of flood prevention facilities (C10) directly affects the protection ability and emergency response efficiency of the subway station during flood disasters. Based on the quantity of equipment such as internal wireless intercoms, flood control shovels, water barriers, flood control sandbags, anti-slip mats, diesel generators, and emergency repair lights in the subway station, the flood prevention facility availability indicator is classified and characterized and assigned values. The availability of facilities is divided into three levels. When the facilities are imperfect or there are many damages, the flood prevention ability is weak, and the indicator is given a dimensionless value of 10; when the facilities are basically perfect but some equipment is aging or in need of maintenance, the protection ability is average, and the indicator is given a dimensionless value of 6; when the facilities are complete and in good condition, the protection ability is strong, and the indicator is given a dimensionless value of 3. This classification and quantification method can quickly evaluate the equipment availability level and guide the rational configuration, renewal, and maintenance of flood prevention facilities.

[0074] The water level monitoring coverage rate (C11) is an important indicator for measuring the real-time monitoring and response ability of the subway station. By classifying and characterizing and assigning values to the layout of water level monitoring sensors at key locations such as entrances and exits, ventilation shafts, and elevator shafts, the coverage level of the in-station monitoring system can be accurately reflected. When water level monitoring sensors are installed at all entrances and exits, vertical elevators, and ventilation shafts, the monitoring coverage rate is the highest, and the indicator is given a dimensionless value of 10; when water level monitoring sensors are installed in some of the above areas, the monitoring coverage rate is good, and the indicator is given a dimensionless value of 6; when water level monitoring sensors are installed in only a few areas, the monitoring coverage rate is low, and the indicator is given a dimensionless value of 3; when no monitoring sensors are installed in the above areas, the monitoring ability is the weakest, and the indicator is given a dimensionless value of 1. Based on this quantitative analysis method, the perfection degree of the water level monitoring system can be evaluated, and the layout plan can be further optimized for weak links to improve the monitoring efficiency.

[0075] The flood control emergency drill and training situation (C12) and the completeness of the flood control emergency plan (C13) are the key indicators to measure the emergency management level of the subway station. These two indicators are analyzed by grading and quantification based on daily management data. When the flood control emergency drill and training at the station are in a normal state and effectively carried out, and the emergency plan is well-prepared, it indicates that the station has strong emergency response capabilities and management levels, and both indicators are given dimensionless values of 1; when the drill and training are basically effectively carried out, and the plan is basically well-prepared, both indicators are given dimensionless values of 3; when the drill and training are not carried out strictly as required, and there are some deficiencies in the plan preparation, both indicators are given dimensionless values of 6; when the drill and training are hardly ever carried out, and there are serious deficiencies in the plan preparation, both indicators are given dimensionless values of 10. Through the above grading and quantification analysis, the strength of the station's emergency management capabilities can be intuitively judged, and it can guide subsequent management improvement and plan optimization.

[0076] Based on the above analysis of the grading and quantification representation of the subway station flood disaster warning indicators, each warning indicator is graded and assigned values according to "10, 6, 3, 1". The disaster potential severity and disaster vulnerability warning indicators are divided into levels I to IV from high to low according to the severity, and the disaster prevention and resistance capabilities are divided into levels I to IV from high to low. Finally, the subway station flood disaster warning indicator system, quantification representation, and data acquisition method are shown in Table 1.

[0077] Table 1 Subway Station Flood Disaster Warning Indicator System

[0078]

[0079]

[0080] Furthermore, step S3 includes:

[0081] S31, study the relationship between the average hourly rainfall and the water accumulation depth. By collecting the original data on the relationship between different rainfall amounts and water accumulation depths, and using the method of polynomial fitting to analyze the data, the calculation formula for the relationship between rainfall and water accumulation depth is obtained.

[0082] The potential severity of flood disasters is determined by three indicators: waterlogging depth, waterlogging duration, and meteorological warning level. Regarding the relationship between rainfall and waterlogging depth, this invention takes the 1-hour rainfall as the research object and widely collects the original data of relevant research on the relationship between different rainfall amounts and waterlogging depths (see the literature "Distribution Characteristics of Urban Waterlogging in Xi'an and Its Relationship with Rainfall", "Analysis and Calculation of the Benefit of Unit LID Measures in Alleviating Urban Waterlogging - Taking Guyuan City as an Example", "Error Analysis of Fitting Urban Waterlogging Depth in Fuzhou Using the Maximum Precipitation", "Research on the Threshold of Rainfall for Waterlogging Early Warning Based on the InfoWorks ICM Model", "Analysis of the Influence of Design Rainfall Characteristics on Urban Waterlogging Simulation Results", "Underground Space Rainstorm Waterlogging Risk Model and Application Based on the Volume Method", "Research on the Resilience Assessment of Urban Rail Transit Lines under Rainstorm Disasters"). The data is sorted as shown in Table 2:

[0083] Table 2 Summary of Original Data on the Relationship between Rainfall and Waterlogging Depth

[0084]

[0085]

[0086] The above original research data is summarized into a group, and the waterlogging depth values under different rainfall data are analyzed by polynomial fitting to obtain the calculation formula for the relationship between rainfall and waterlogging depth:

[0087] y = -0.03 + 0.01514x - 0.00007x 2 (2)

[0088] Among them, x represents the average hourly rainfall (mm); y represents the waterlogging depth (m).

[0089] S32, grade and assign values to the severity of waterlogging duration.

[0090] In practice, generally, when the waterlogging depth reaches more than 0.15 m and the duration exceeds 0.5 h, it is regarded as the standard for a waterlogging event. Referring to the classification standards for the severity of waterlogging duration in specifications such as the "Guide for Urban Waterlogging Control in Anhui Province", the severity of waterlogging duration is divided into four levels, and the method of grading and assigning values is shown in Table 3.

[0091] Table 3 Classification Representation of the Severity of Waterlogging Duration

[0092] Severity level Ponding time Grading value Level 1 t > 1.5h 1.2 Level 2 t > 1.0h 1.0 Level 3 t > 0.5h 0.8 Level 4 t < 0.5h 0.6

[0093] S33, numerical conversion of rainfall meteorological information and waterlogging depth information of flood disasters.

[0094] Based on the proposed conversion formula between rainfall and ponding depth, the rainfall warning thresholds in the meteorological warning levels are converted into the average hourly rainfall, and then substituted into Equation (2) to obtain the predicted ponding depths under different meteorological warning levels as shown in Table 4.

[0095] Table 4 Predicted Ponding Depths under Different Meteorological Warning Levels

[0096] Warning level Discrimination criteria Average hourly rainfall Expected ponding depth Blue Rainfall will reach more than 50 mm within 12 hours 5 mm 0.04m Yellow Rainfall will reach more than 50 mm within 6 hours 9 mm 0.10m Orange Rainfall will reach more than 50 mm within 3 hours 17 mm 0.21m Red Rainfall will reach more than 100 mm within 3 hours 34 mm 0.40m

[0097] In addition to rainfall, the ponding time is also a key factor affecting the severity of waterlogging disasters. The ponding time around the subway station is affected by various factors such as the surrounding environmental terrain, soil, and municipal drainage capacity. Long-term ponding may cause water to backflow into the subway station, increasing the possibility and severity of potential losses. In practice, generally, when the ponding depth reaches more than 0.15 m and the duration exceeds 0.5 h, it is regarded as the standard of a waterlogging event. The research refers to the classification criteria for the severity of ponding time in specifications such as the "Guidelines for Urban Waterlogging Control in Anhui Province" and divides the severity of ponding time into four levels:

[0098] If the ponding time of the subway station exceeds 1.5 hours, the severity level of subway ponding is the first level, and the severity level is assigned a value of 1.2;

[0099] If the ponding time of the subway station is greater than 1 hour but does not exceed 1.5 hours, the severity level of subway ponding is the second level, and the severity level is assigned a value of 1.0;

[0100] If the ponding time of the subway station is greater than 0.5 hour but does not exceed 1 hour, the severity level of subway ponding is the third level, and the severity level is assigned a value of 0.8;

[0101] If the ponding time of the subway station is within 0.5 hour, the severity level of subway ponding is the fourth level, and the severity level is assigned a value of 0.6;

[0102] Based on the above analysis, the severity levels and classification assignment methods of subway station ponding are shown in Table 5.

[0103] Table 5 Classification Representation of the Severity of Ponding Time

[0104] Severity level Ponding time Grading value Level Ⅰ t > 1.5h 1.2 Level Ⅱ t > 1.0h 1.0 Level Ⅲ t > 0.5h 0.8 Level Ⅳ t < 0.5h 0.6

[0105] The vulnerability of subway stations to flood disasters is mainly composed of five indicators: terrain and elevation, height of entrance steps, type of entrances and exits, real-time passenger flow, and density of municipal drainage pipelines. The flood prevention and disaster resistance capabilities of subway stations are mainly composed of five indicators: station drainage capacity, water level monitoring coverage rate, flood prevention facility equipment, flood control emergency drills and training, and completeness of flood control emergency plans.

[0106] Specifically, Step 4 includes:

[0107] S41. To obtain the weight values of various indicators for vulnerability and disaster prevention and resistance capabilities, based on AHP, the relative importance of each indicator is determined through expert scoring. The 1-9 scale method is used to compare the importance of early warning indicators pairwise, and a complete expert judgment matrix is constructed. Starting from the second layer, for a certain element in the upper layer, the elements in the lower layer related to it, that is, the elements with connections between layers, are compared pairwise, and the levels are evaluated according to their importance, denoted as a ij is the importance level of element i compared to element j, and a ji is the importance level of element j compared to element i. The form of the expert judgment matrix (see the literature "Research on the Evaluation Index System for the Emergency Response Capability of Metro Incidents in Full Automatic Operation") is shown in Equation (3):

[0108]

[0109] In the formula, n represents the number of indicators in this layer; a ij represents the relative importance scale of indicator i compared to indicator j.

[0110] Based on the expert judgment matrix, the maximum eigenvalue and the consistency index CR of the matrix are calculated respectively as shown in Equations (4) and (5). CR < 0.1 indicates that the consistency test is passed. On this basis, the maximum eigenvalue and the corresponding eigenvector W are normalized to obtain the weight values of each indicator.

[0111]

[0112] In the formula, W represents the eigenvector of the judgment matrix, and W i is the weight value obtained by normalizing the judgment matrix. RI can be obtained by looking up the table according to the order of the matrix. The weight and grading assignment methods of the vulnerability and disaster prevention and resistance capabilities indicators of subway station flood disasters are shown in Table 6. The CR values of each expert's data are all less than 0.1, and the judgment matrix passes the consistency test.

[0113] Table 6 Weight and Grading Assignment of Vulnerability and Disaster Prevention and Resistance Capability Indicators of Subway Station Flood Disasters

[0114]

[0115] S42. TOPSIS, short for Technique for Order Preference by Similarity to Ideal Solution, constructs "positive and negative ideal solutions" to obtain the degree of closeness between the evaluation object and the positive ideal solution as the basis for judging the level of the evaluation result, and has been widely used in the fields of risk assessment and early warning (see the literature "Safety Evaluation of Subway Station Construction Based on CRITIC Method and TOPSIS" and "Application of AHP-TOPSIS Method in Subway Station Scheme Selection"). Based on the calculation results of the TOPSIS method, the vulnerability and disaster prevention and resistance ability levels are divided into four levels according to the relative closeness by using the equal interval method.

[0116] The process of determining the early warning level by using the TOPSIS method is as follows:

[0117] First, construct a weighted decision matrix based on the early warning index weights:

[0118] C ij = ω j *a ij (i = 1, 2,..., m; j = 1, 2,..., n) (6)

[0119] In the formula, ω j represents the index weight; a ij represents the value of the j-th evaluation index of the i-th evaluation object.

[0120] Secondly, determine the positive ideal solution and negative ideal solution of each early warning evaluation index:

[0121]

[0122]

[0123] Thirdly, use the Euclidean distance to calculate the distances between the evaluation object and the positive and negative ideal solutions respectively:

[0124]

[0125] ④ Calculate the relative closeness of the evaluation object:

[0126]

[0127] Based on the calculation results of the TOPSIS method, the vulnerability and disaster prevention and resistance ability levels are divided into four levels according to the relative closeness by using the equal interval method as shown in Table 7.

[0128] Table 7 Relative Closeness Evaluation Criteria for Subway Station Flood Disaster Vulnerability and Disaster Prevention and Resistance Ability

[0129] Vulnerability level Meaning Proximity interval Level Ⅰ High vulnerability, difficult to resist flood disasters [0.75，1.00] Level Ⅱ Relatively high vulnerability, barely able to resist flood disasters [0.50，0.75) Level Ⅲ Lower vulnerability, able to resist flood disasters [0.25，0.50) Level Ⅳ Low vulnerability, able to effectively resist flood disasters (0.0，0.25) Disaster prevention ability level Meaning Proximity interval Level Ⅰ High disaster prevention ability, able to effectively resist flood disasters [0.75，1.00] Level Ⅱ Relatively high disaster prevention ability, able to resist flood disasters [0.50，0.75) Level Ⅲ Poor disaster prevention ability, barely able to resist flood disasters [0.25，0.50) Level Ⅳ Weak disaster prevention ability, difficult to resist flood disasters (0.0，0.25)

[0130] Specifically, in step S5, the calculation method for the flood disaster warning value of the subway station is as follows:

[0131] Subway flood disaster warning level = rainfall and water accumulation situation × vulnerability / disaster prevention and resistance ability

[0132] Based on the constructed flood disaster warning index system of the subway station, flood disaster warning should comprehensively consider three aspects: the potential severity of the disaster, disaster vulnerability, and disaster prevention and resistance ability. The relationship among the three is: Subway flood disaster warning level = rainfall and water accumulation situation × vulnerability / disaster prevention and resistance ability. Among them, the potential severity of the disaster mainly considers the ratio of the actual or predicted water accumulation depth to the height of the entrance steps. Vulnerability and disaster prevention and resistance ability are calculated through the relative closeness of the evaluation results of corresponding indicators. Finally, the formula for calculating the flood disaster warning value of the subway station is as follows:

[0133]

[0134] In the formula, PV represents the flood disaster warning value of the subway; D water represents the measured or predicted value of the flood and water accumulation depth around the subway station. The predicted value can be calculated by formula (2) according to rainfall information; H step(min) represents the minimum value of the step height of each entrance of the subway station; V T represents the grading assignment of the water accumulation time, and the assignment method is shown in Table 8; E vulnerability represents the flood disaster vulnerability (relative closeness) of the subway station; E resistance represents the flood disaster prevention and resistance ability (relative closeness) of the subway station.

[0135] Table 8 Grading assignment of the severity of water accumulation time

[0136] Severity level Ponding time Grading value Level Ⅰ t > 1.5h 1.2 Level Ⅱ t > 1.0h 1.0 Level Ⅲ t > 0.5h 0.8 Level Ⅳ t < 0.5h 0.6

[0137] Based on the calculation result of the subway flood disaster warning value (PV), determine the subway flood disaster grading standard;

[0138] In the most unfavorable scenario of the subway station flood disaster, considering that the water accumulation depth will reach 0.6 m at level 1, the entrance steps are the lowest standard of the standard specification at 0.3 m, the water accumulation time has reached more than 1.5 h, and the vulnerability and disaster resistance ability are taken as E vulnerability / E resistance = 2 for value taking, that is, the disaster resistance ability cannot effectively resist the vulnerability of the disaster-bearing body. From this, the threshold value of the subway flood disaster warning value in the unfavorable scenario is calculated as [(0.6 / 0.3) * 1.2] * 2 = 4.8.

[0139] In the ideal scenario of flood disasters at subway stations, considering that the water accumulation depth will reach level four, which is 0.15 m, the entrance and exit steps are set at the upper limit of the standard specification, which is 0.45 m, the water accumulation time is less than 0.5 h, and the vulnerability and disaster resistance ability are valued according to the relatively ideal scenario E vulnerability / E resistance = 0.5. That is, the disaster resistance ability can effectively resist the vulnerability of the disaster-bearing body. From this, the early warning value threshold of subway flood disasters in the relatively ideal scenario is calculated as [(0.15 / 0.45) * 0.6] * 0.5 = 0.1.

[0140] Based on the early warning value thresholds in the most unfavorable scenario and the ideal scenario of subway flood disasters, the early warning value range is divided according to the four-level early warning. The interval difference is (4.8 - 0.1) / 4 ≈ 1.2. Conservatively, the interval difference is taken as 1. On this basis, the subway flood disaster early warning classification standard based on the early warning value is proposed as shown in Table 9 below. The higher the early warning value, the greater the potential accident threat.

[0141] Table 9 Explanation of the Subway Flood Disaster Classification Early Warning Standard

[0142]

[0143] Based on the above calculation of the flood early warning level under different water accumulation depths and meteorological information, as well as the early warning level adjustment judgment criteria, the complete subway flood early warning level standard is proposed as shown in Table 10 below.

[0144] Table 10 Subway Flood Classification Early Warning Evaluation Standard

[0145]

[0146]

[0147]

[0148]

[0149] It should be noted that with the evolution of the water accumulation situation at the flood disaster site and the dynamic changes in rescue and disposal, the early warning level will also change dynamically. In practical applications, subway operation units can use the early warning level results output by this classification early warning evaluation model as an important decision-making reference, and adjust the emergency response strategy in a timely manner to ensure that the consequences of flood accidents are minimized.

[0150] In summary, the present invention aims at the early warning of flood disasters in subway stations. Based on the dynamic disaster information and the inherent property parameters of the stations, a warning and assessment method for flood disasters in subway stations based on dynamic monitoring and quantitative analysis is proposed. Starting from three aspects: the potential severity of flood disasters, the vulnerability of stations, and the disaster prevention and resistance capabilities, the present invention constructs a hierarchical warning index system covering 13 warning indicators, and proposes a hierarchical quantitative characterization method for each indicator, providing a scientific basis for the risk assessment of flood disasters in subway stations. The present invention uses the methods of literature research and numerical fitting to analyze the correlation between hourly rainfall and water accumulation depth, and establishes a water accumulation depth prediction model based on the measured information of water accumulation and meteorological forecast information. Through formula calculation and dynamic data update, this model can correct the formula according to the characteristics of urban waterlogging and the data of waterlogging points in different cities to ensure the accuracy and applicability of the prediction results. In addition, based on the AHP and TOPSIS methods, evaluation methods for flood disaster vulnerability and disaster prevention and resistance capabilities are respectively proposed. Starting from the coupled effects of disaster situation information, vulnerability, and disaster prevention and resistance capabilities, a comprehensive warning level calculation method and discrimination criteria are further proposed. This method can integrate real-time rainfall and dynamic information of water accumulation with the inherent property parameters of the stations to achieve dynamic hierarchical warning of flood disasters in subway stations, providing a timely and accurate decision-making basis for disaster prevention and control.

[0151] The present invention is not only applicable to the risk assessment of flood disasters in subway station projects, but also can be widely applied to the flood disaster prevention and control analysis of other public buildings or urban infrastructure, with strong applicability and promotion value. Compared with the existing technologies, the present invention has significant technological advancements in aspects such as dynamic monitoring, quantitative assessment, and algorithm optimization, effectively overcoming the defects of strong subjectivity of assessment criteria and insufficient dynamic response in traditional methods, and improving the scientificity and practicality of disaster assessment. In addition, the present invention has significant economic and social benefits, can provide technical support and guidance for urban flood control management, and will surely have a broad market application prospect.

[0152] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; under the idea of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and the steps can be implemented in any order, and there are many other variations in different aspects of the present invention as described above. For the sake of brevity, they are not provided in detail; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for early warning and assessment of flood disasters in subway stations, characterized in that: The following steps are involved: S1. Establish a real-time dynamic assessment system for subway flood disaster warning data and set up an expert group of more than three people; S2. A hierarchical quantitative characterization method for early warning indicators is proposed based on a real-time dynamic evaluation system. The evaluation system includes primary indicators and secondary indicators. The primary indicators include the potential severity of flood disasters, flood disaster vulnerability factors, and flood disaster prevention and resistance capacity factors; S3, based on the relationship between average hourly rainfall, meteorological warning level and waterlogging depth, realize the transformation of rainfall meteorological information and flood disaster waterlogging depth prediction value; S4, based on AHP and TOPSIS, vulnerability and disaster prevention and mitigation capacity evaluation methods are proposed respectively, the weight values ​​of early warning indicators are determined and the relative closeness is calculated; S5, based on the coupling influence relationship between the three early warning indicators of potential severity, vulnerability, and disaster prevention and mitigation capabilities of flood disasters, the comprehensive early warning value of subway stations’ flood disasters is calculated; S6, determining a warning level based on the comprehensive flood disaster warning value.

2. The subway station flood disaster early warning and assessment method according to claim 1 is characterized in that: The calculation formula for the comprehensive warning value of flood disasters at the subway station is: Among them, PV represents the subway flood disaster warning value; D water Indicates the measured or predicted value of flood water depth around the subway station; H step(min) Indicates the minimum height of the steps at each entrance and exit of the subway station; V T Indicates the graded value of waterlogging time; E vulnerability Indicates the relative proximity of the subway station to flood disaster vulnerability; E resistance It indicates the relative closeness of the subway station’s flood disaster prevention and mitigation capabilities.

3. The subway station flood disaster early warning and assessment method according to claim 2 is characterized in that: The indicators of the potential severity of flood disasters include water accumulation depth, water accumulation time and meteorological warning level.

4. The subway station flood disaster early warning and assessment method according to claim 3 is characterized in that: The water accumulation depth and water accumulation time can be determined by measured values ​​and predicted values. The measured values ​​include the actual values ​​of the water accumulation depth and water accumulation time around the subway station. The predicted values ​​are based on the water accumulation depth converted from meteorological warning information, and the predicted values ​​and measured values ​​are divided into warning levels using the same standards.

5. The subway station flood disaster early warning and assessment method according to claim 2 is characterized in that: The flood disaster vulnerability quantification includes a quantitative analysis of the subway station's terrain and elevation, entrance and exit step heights, entrance and exit types, passenger flow, and municipal drainage network density.

6. The subway station flood disaster early warning and assessment method according to claim 2 is characterized in that: The quantification of flood disaster prevention and mitigation capabilities includes quantitative analysis of station drainage capacity, flood control facilities, water level monitoring coverage, flood emergency drills and training, and the completeness of flood emergency plans.

7. The subway station flood disaster early warning and assessment method according to any one of claims 2 to 6, characterized in that: Step S3 includes: S31, study the relationship between average hourly rainfall and water depth, collect original data on the relationship between different rainfall and water depth, use polynomial fitting method to analyze the data, and obtain the calculation formula for the relationship between rainfall and water depth; S32, assigning grades to the severity of the waterlogging time; S33, numerical conversion of rainfall meteorological information and flood disaster water depth information, converting the rainfall warning threshold in the meteorological warning level into the hourly rainfall average, and substituting it into the rainfall and water depth calculation formula in step S31 to obtain the expected water depth under different meteorological warning levels.

8. The subway station flood disaster early warning and assessment method according to claim 7 is characterized in that: The calculation formula for the relationship between rainfall and water depth is: y=-0.03+0.01514x-0.00007x 2 Among them, x is the average hourly rainfall in mm, and y is the water depth in m.

9. The subway station flood disaster early warning and assessment method according to claim 8, characterized in that: Step S4 includes step S41, obtaining the weight values ​​of the vulnerability index and the disaster prevention and resistance capacity index of the subway station, and constructing a complete expert judgment matrix, the matrix form is: Among them, n represents the number of indicators in this level; a ij It represents the relative importance scale of indicator i compared to indicator j; The calculation formula of the maximum eigenvalue of the matrix and the consistency index CR is: Among them, W represents the eigenvector of the judgment matrix, W i To judge the weight value obtained by normalization of the matrix, RI can be obtained by looking up the table according to the matrix order. The CR value of each expert data is less than 0.1, and the judgment matrix passes the consistency test.

10. The subway station flood disaster early warning and assessment method according to claim 9, characterized in that: Step S4 includes step S42, using the TOPSIS method to determine the warning level, the process is as follows: First, a weighted decision matrix is ​​constructed based on the weights of the early warning indicators: c ij =ω j *a ij (i=1,2,...,m;j=1,2,...,n) Among them, ω j Indicates the indicator weight; a ij Represents the jth evaluation index value of the i-th evaluation object; Secondly, determine the positive ideal solution and negative ideal solution of each early warning evaluation indicator: Again, the Euclidean distance is used to calculate the distance between the evaluation object and the positive and negative ideal solutions respectively: Finally, the relative closeness of the evaluation object is calculated:

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