High-rise building construction engineering premium rate determination based on construction risk evaluation

Abstract

The invention provides determination of a premium rate of high-rise building construction engineering based on construction risk evaluation. The determination comprises the following steps: S1, constructing a high-rise building construction risk index system; s2, constructing a high-rise building construction risk evaluation model based on the high-rise building construction risk index system; s3, construction risk evaluation engineering premium rate determination is established based on the high-rise building construction risk evaluation model; and S4, performing trial calculation on the project instance through the determination of the project premium rate of the construction risk evaluation, and performing feasibility determination. According to the method, the problems that the calculation of the engineering premium rate in China is rough in consideration of construction project risks and is often obtained according to previous similar engineering rate determination and compensation experience, the obtained rate cannot well reflect the actual construction risk level, and the rate of similar engineering tends to be homogenized can be solved; and a new method can be provided for an insurance company to reasonably determine the premium rate of the high-rise building construction engineering for reference.

Description

Technical Field

[0001] The invention relates to the technical field of insurance premium rate determination, and in particular to insurance premium rate determination for high-rise building construction projects based on construction risk evaluation. Background Art

[0002] With the acceleration of urbanization, the number and scale of high-rise buildings that save urban land resources and improve space utilization are constantly increasing. However, engineering risk accidents occur frequently during the construction period, resulting in casualties and huge economic losses. The international engineering community mainly promotes the transfer of construction risks by purchasing engineering insurance to reduce the economic losses caused by accidents. In practice, the calculation of engineering insurance premiums in my country is relatively rough in considering the risks of construction projects. It is often based on the previous rate setting and compensation experience of similar projects. The obtained rates cannot well reflect the actual construction risk level, and the rates of similar projects tend to be homogenized. At present, there are few studies on the engineering insurance rate setting methods or setting models for high-rise buildings in the public literature. Based on this, the present invention proposes a high-rise building engineering insurance rate setting based on construction risk assessment to solve the above-mentioned problems. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a method for determining insurance premiums for high-rise building construction projects based on construction risk assessment, so as to at least solve the above problems.

[0004] The technical solution adopted by the present invention is as follows:

[0005] The determination of insurance premiums for high-rise housing construction projects based on construction risk assessment includes the following steps:

[0006] S1. Construct a risk indicator system for high-rise building construction;

[0007] S2. Construct a high-rise building construction risk assessment model based on the high-rise building construction risk index system;

[0008] S3. Constructing construction risk assessment based on the risk assessment model for high-rise building construction to determine the engineering insurance premium rate;

[0009] S4. The project insurance premium rate is determined through construction risk assessment, and the project examples are calculated and feasibility is determined.

[0010] Furthermore, step S1 also includes the following steps:

[0011] S11. Identify the initial risk factors in the construction process of high-rise buildings by adopting the grounded theory risk identification method;

[0012] S12. Filter and screen initial risk factors through questionnaire survey;

[0013] S13, based on the Cronbach reliability coefficient method, the questionnaire survey was used to test the relevance and consistency of the questionnaire survey data;

[0014] S14. Construct a high-rise building construction risk index system based on the relevance and consistency of the completed inspection questionnaire survey data.

[0015] Furthermore, the initial risk factors include personnel risk, equipment and material risk, management risk, solution and technology risk, and environmental risk.

[0016] Furthermore, in step S2, the following steps are also included:

[0017] S21, calculate the subjective weight in the indicator by using the ordinal relationship method G1;

[0018] S22. Calculate the objective weights in the indicators by entropy weight method;

[0019] S23. Calculate the combined weights of subjective and objective weights through game theory;

[0020] S24. A high-rise building construction risk assessment model is constructed based on the cloud model and combined weights.

[0021] Furthermore, in step S21, the subjective weight in the index is calculated by the order relationship method G1 as follows:

[0022] Determine the order relationship between the indicators at the indicator layer and the criterion layer, and set the risk indicator of a certain indicator layer as an initial indicator set X = {x 1 ,x 2 ,x 3 ,…,x n}, experts sort the importance of n indicators in the initial indicator set. If x i The importance of x is greater than j , then recorded as n indices can determine a unique order relationship, denoted as

[0023] Determine the importance of adjacent indicators, assign importance values ​​between adjacent indicators through experts, and use r k express,

[0024]

[0025] In the formula, are the independent weights of the kth and k-1th indicators respectively;

[0026] Calculate the weight of each indicator, assuming that the experts assign the importance of two adjacent indicators to r k, calculate the more important indicator among the two adjacent indicators, that is, the independent weight of the kth indicator

[0027]

[0028] Group weighted fusion uses the weighted average method to fuse the values ​​assigned by different experts for each indicator to obtain the subjective weighted value.

[0029]

[0030] In the formula, m is the number of experts;

[0031] In step S22, the objective weight in the index is calculated by the entropy weight method as follows:

[0032] Assuming that there are m evaluation experts invited and there are n evaluation indicators in a certain evaluation indicator system, the original judgment matrix X is constructed as:

[0033]

[0034] In the formula, x ij is the value of the jth evaluation index by the i-th evaluation expert,

[0035] The original judgment matrix is ​​standardized and the calculation formula is as follows:

[0036] Positive indicators:

[0037] Negative indicators:

[0038] Calculate the entropy e of the jth evaluation index j

[0039]

[0040] In the formula, m is the number of evaluation experts;

[0041] Calculate the weight of evaluation index

[0042]

[0043] In step S23, the combined weight calculation of the subjective and objective weights is performed through game theory as follows:

[0044] Construct a basic weight vector set. If L methods are used to calculate multiple weights of n indicators, the vector set is:

[0045] w j ={w j1 ,w j2 ,w j3 ,…,wjn},(j=1,2,…,L)

[0046] Then any linear combination of L weight vectors is:

[0047]

[0048] In the formula, α i is the combination coefficient, α i >0 and α 1 +α 2 +…+α L =1,

[0049] Optimizing the combination coefficient The optimization objective function is:

[0050]

[0051] Through the differential characteristics, the optimal linear equations equivalent to the objective function are obtained:

[0052]

[0053] Determine the optimal combination weight W and find the optimal combination coefficient (α 1 ,α 2 ,…,α L ), and normalized to obtain Finally, the optimal weight W is calculated

[0054]

[0055] In step S24, a high-rise building construction risk assessment model is constructed based on the cloud model and the combined weights. Specifically,

[0056] The three cloud digital features of expectation Ex, entropy En, and super entropy He are transformed from qualitative fuzzy concepts to quantitative data through the forward cloud generator, and converted into cloud drop drop (x i ,μ(x i )), (i=1,2,3,…,n), the cloud model is drawn by the generated n cloud droplets, and the steps of the forward cloud generator algorithm are as follows:

[0057] ① Calculate the characteristic values ​​Ex, En and the number of cloud droplets to be generated n, where n = 3000;

[0058] ② Generate a normal random number x with expected value and standard deviation Ex and He respectively i ;

[0059] ③Calculate the normal random number x j The corresponding membership μ(x i );

[0060]

[0061] Let (x i ,μ(x i )) is a cloud droplet, which represents the specific embodiment of the language value represented by the cloud droplet in quantity. Repeat steps ①-③ until n cloud droplets are generated;

[0062] The quantitative data is transformed into qualitative fuzzy concepts through the reverse cloud generator. The calculation steps of the reverse cloud generator are as follows:

[0063] ①Calculate the mean of sample data

[0064]

[0065] ② Calculate the absolute center distance A of the sample:

[0066]

[0067] ③Calculate the sample variance S 2 :

[0068]

[0069] Calculate cloud digital features (Ex, En, He) based on the basic features of sample data:

[0070] ① Calculate the expected Ex:

[0071]

[0072] ② Calculate entropy En:

[0073]

[0074] ③ Calculate the super entropy He:

[0075]

[0076] Determine the evaluation level, divide the construction risk level of high-rise building projects into 5 levels, the risk level set V = {low risk, lower risk, medium risk, higher risk, high risk}, and the evaluation standard cloud. According to the risk evaluation level and risk evaluation characteristics, set a domain space U with bilateral constraints. Define the domain space U = [0,10] and divide it into 5 sub-intervals according to the risk level, where the i-th sub-interval is Specifically, they are [0,2], [2,4], [4,6], [6,8], and [8,10]. Based on the divided sub-intervals, the following formula is used to calculate the cloud digital characteristics of each risk level:

[0077]

[0078] Where s is a constant, which is obtained based on the aggregation degree of the debugged cloud droplets, where s = 0.07;

[0079] Calculate the index cloud parameters, invite experts to assign values ​​according to the actual situation of the risk index, and use the reverse cloud generator to obtain the variance of the risk index and the cloud digital characteristics.

[0080] Let the jth cloud digital feature be C j (Ex j ,En j ,He j ), the formula is as follows:

[0081]

[0082] In the formula, p is the number of experts, x ij represents the value assigned by the ith expert to indicator j, is the variance;

[0083] Determine the evaluation comprehensive cloud and risk level, input the calculated combined weight value and cloud digital features into the reverse cloud generator, and calculate the comprehensive cloud digital features (Ex, En, He), as follows:

[0084]

[0085] The digital features of the comprehensive cloud are input into the forward cloud generator, and the evaluation comprehensive cloud map is generated using MATLAB software. The risk level of high-rise building construction is comprehensively determined by comparing and analyzing the relative positions of the standard cloud and the comprehensive cloud and combining the similarity between the comprehensive cloud and the standard cloud. The similarity between the comprehensive cloud and the standard cloud is calculated using the Euclidean distance, and the calculation formula is as follows:

[0086]

[0087] Further, in step S3, the engineering insurance premium rate for construction risk assessment based on the high-rise building construction risk assessment model is determined as follows:

[0088] Calculate a basic rate determination value within the basic rate range;

[0089] According to the subdomain interval corresponding to the risk level, the adjustment coefficient is calculated using the linear interpolation method;

[0090] Substitute the accurate values ​​of the basic rate and adjustment coefficient into the general formula for determining the increase and decrease method to calculate the determined pure rate of engineering insurance. The pure rate of high-rise building construction engineering insurance is determined by the increase and decrease method. The pure rate calculation formula of the increase and decrease method is as follows:

[0091] r=remin / max ×(1+α)

[0092] Where: r emin / max is the basic rate value range, (1+α) is the adjustment coefficient, and α is the risk impact coefficient.

[0093] Furthermore, in step S4, the project insurance premium rate is determined through construction risk assessment to calculate the project example, and the feasibility is determined as follows:

[0094] The engineering examples based on trial calculations are identified through the use of the order relationship method G1 to determine the subjective weights, the entropy weight method to determine the objective weights, the game theory combination weights, the cloud model comprehensive evaluation and the engineering insurance premium rate determination.

[0095] Compared with the prior art, the present invention has the following beneficial effects:

[0096] 1. A new high-rise housing construction project insurance premium rate determination method has been constructed, which provides a solution to the current situation where there are few high-rise housing construction project insurance premium rate determination methods. The determined rate not only takes into account the previous determination and compensation experience of similar projects, but also allows the actual risk situation of the project to be reflected in the engineering insurance premium rate, providing a reference for insurance companies to reasonably determine the engineering insurance premium rate of high-rise housing construction projects.

[0097] 2. By introducing the game theory combined weighting method in the indicator weight assignment, the deviation between the combined weighting value and the subjective and objective weights is minimized, while taking into account both subjectivity and objectivity and effectively improving the accuracy of the weight value. In order to improve the accuracy of construction risk assessment, the cloud model comprehensive evaluation method is introduced to fully consider the randomness and uncertainty of risks, realize the two-way conversion between qualitative concepts and quantitative data, and directly quantify risks into specific values, effectively connecting the rate determination work. In addition, the accurate value of the pure rate of engineering insurance can be directly determined based on the comprehensive evaluation results and cloud digital characteristics, with a clear determination process and reasonable determination results.

[0098] 3. The research results were applied to a high-rise children's health building. Taking this project as an example, the project engineering insurance pure rate determination process was run and trial calculations were performed. The risk assessment and determination results achieved the same frequency fluctuation of the engineering insurance pure rate and the actual construction risk level, verifying the feasibility of the constructed rate determination model in engineering practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0100] Figure 1 It is a schematic diagram of the overall process proposed in the embodiment of the present invention.

[0101] Figure 2 It is a schematic diagram of a high-rise building construction risk index system proposed in an embodiment of the present invention.

[0102] Figure 3 4 is a schematic diagram of comprehensive cloud comparison proposed in an embodiment of the present invention.

[0103] Figure 4 It is a schematic diagram of the personnel risk A1 evaluation cloud proposed in an embodiment of the present invention.

[0104] Figure 5 It is a schematic diagram of the equipment material risk A2 assessment cloud proposed in an embodiment of the present invention.

[0105] Figure 6 It is a schematic diagram of the management risk A3 evaluation cloud proposed in an embodiment of the present invention.

[0106] Figure 7 It is a schematic diagram of the technical risk A4 evaluation cloud of the solution proposed in the embodiment of the present invention.

[0107] Figure 8 It is a schematic diagram of the environmental risk A5 assessment cloud proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0108] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0109] Reference Figure 1-Figure 2 The present invention provides a method for determining insurance premium rates for high-rise housing construction projects based on construction risk assessment, comprising the following steps:

[0110] S1. Construct a risk indicator system for high-rise building construction;

[0111] S2. Construct a high-rise building construction risk assessment model based on the high-rise building construction risk index system;

[0112] S3. Constructing construction risk assessment based on the risk assessment model for high-rise building construction to determine the engineering insurance premium rate;

[0113] S4. The project insurance premium rate is determined through construction risk assessment, and the project examples are calculated and feasibility is determined.

[0114] For example, in order to solve the problem of calculating the engineering insurance premium rate in my country, the consideration of the risks of construction projects is relatively rough, and it is often obtained based on the previous experience of setting and paying the rates of similar projects. The obtained rates cannot well reflect the actual construction risk level, and the rates of similar projects tend to be homogenized. By constructing a risk indicator system, a risk assessment model and an insurance premium setting model, a comprehensive quantitative evaluation of construction risks and an accurate setting of insurance premiums can be achieved. This method not only improves the scientificity and rationality of insurance premiums, but also provides strong support for risk management and insurance protection of high-rise housing construction projects.

[0115] Step S1 also includes the following steps:

[0116] S11. Identify the initial risk factors in the construction process of high-rise buildings by adopting the grounded theory risk identification method;

[0117] S12. Filter and screen initial risk factors through questionnaire survey;

[0118] S13, based on the Cronbach reliability coefficient method, the questionnaire survey was used to test the relevance and consistency of the questionnaire survey data;

[0119] S14. Construct a high-rise building construction risk index system based on the relevance and consistency of the completed inspection questionnaire survey data.

[0120] Initial risk factors include personnel risk, equipment and material risk, management risk, program and technology risk, and environmental risk.

[0121] For example, the grounded theory qualitative research method is used to identify and analyze the risk factors of high-rise building construction, and the five risk factors of "personnel, equipment and materials, management, scheme technology, and environment" are preliminarily identified. Because the grounded theory qualitative research method relies more on the experience and knowledge of the researcher, in order to reduce errors and improve the scientific rationality of the indicator system, the initial construction risk factors are screened, filtered and optimized by combining the questionnaire. The results show that the reliability of the questionnaire has passed the reliability analysis in SPSS software, and the questionnaire is reliable and credible. Finally, a high-rise building construction risk indicator system within the risk range of engineering insurance is constructed. For the initial risk factor filtering, the initial risk indicator factors identified by the grounded theory qualitative research method are mixed with the researcher's greater subjectivity, and at the same time, the researcher's experience and knowledge are tested. In the process of open coding conceptualization and categorization and main axis coding, there may be problems of inappropriate concept extraction and incorrect expression. In addition, the identified initial risk indicator factors may have a very low probability of occurrence and a small degree of impact, or the identified initial risk indicator factors are not within the risk range of the signed engineering insurance policy. Therefore, it is necessary to screen, filter or re-extract the initial risk indicator factors with problems, and construct the final high-rise building construction risk indicator system within the insurance risk range of the insured project. For the questionnaire survey design, in order to effectively screen, filter and optimize the initial risk indicator factors, the initial risk indicator factors are designed into a questionnaire, and experts and scholars who have participated in the construction management and related research of high-rise buildings and practitioners in related industries are invited to distribute the questionnaires. The respondents judge the importance of the initial risk indicator factors. The larger the value assigned to the initial risk indicator factor, the more important the factor is. The assignment adopts a five-meter scale, 1-5 represents: "unimportant", "less important", "generally important", "important", and "very important". For reliability analysis, in order to test the reliability of the effectively recovered questionnaires, reliability analysis is usually used to test. Reliability analysis is an analytical method to measure the stability and reliability of the questionnaire results. The Cronbach reliability coefficient method of reliability analysis in SPSS software is used to detect the relevance and consistency of the questionnaire data, thereby verifying the reliability of the effectively recovered questionnaires. The Cronbach's alpha coefficient was proposed by Lee Cronbach in 1951 and is a common reliability analysis indicator. It is affected by the number of analyzed items and the average value of the correlation coefficient. When the number of analyzed items is constant, the correlation coefficient is positively correlated with the reliability and Cronbach's alpha coefficient. That is, the reliability of the questionnaire data can be measured by the size of the Cronbach's alpha coefficient. The higher the Cronbach's alpha coefficient, the higher the reliability of the questionnaire. The present invention believes that if the Cronbach's alpha coefficient is greater than 0.7, the questionnaire is credible. The specific coefficient range and connotation are shown in the table below.

[0122]

[0123] The specific meaning of Cronbach's α coefficient

[0124] ① Cronbach's α coefficient value: If Cronbach's α>0.7, the questionnaire results are credible. If 0.6≤Cronbach's α<0.7, it is necessary to ask the respondents for their opinions and fine-tune the questionnaire. If Cronbach's α<0.6, it indicates that there are major problems with the questionnaire and it is necessary to redesign and modify the questionnaire and conduct the survey again.

[0125] ②Correlation between the corrected item and the total (CITC value): The CITC value is between 0 and 1. The closer it is to 1, the stronger the correlation between the indicator and the total. If it is lower than 0.3, it indicates that the correlation between the indicator and the total is weak, and consider deleting the indicator.

[0126] ③ Cronbach's α coefficient after deleting an item: that is, the Cronbach's α coefficient of the remaining indicators as a whole after deleting an indicator. If the "Cronbach's α coefficient after deleting an item" of an indicator is higher than the "Cronbach's α coefficient", it means that the reliability of the questionnaire will increase after deleting the indicator, and consider deleting the indicator.

[0127] For the 52 validly collected questionnaires, the reliability analysis function of SPSS software was used to analyze the scale made based on the questionnaire data. The analysis results are shown in the table below.

[0128]

[0129] Table Cronbach's α analysis statistics

[0130] From the table analysis statistics, it can be seen that the overall Cronbach's α coefficient is 0.839>0.8, indicating that the questionnaire has high reliability and the questionnaire data is credible and reliable. Among them, the CITC value of the "A23 material reliability" indicator is less than 0.3, indicating that the correlation between this indicator and the total is very weak, and this indicator is considered to be deleted. In addition, the "Cronbach's α coefficient after deleting the item" is 0.845, which is greater than the "Cronbach's α coefficient" of 0.839, indicating that the Cronbach's α coefficient will increase overall after the indicator is deleted, and this indicator is considered to be deleted. Through further analysis of this indicator and consulting experts and scholars and comparing the scope of risks assumed by engineering insurance, it is found that the "A23 material reliability" indicator generally refers to raw material defects, and the risk accident losses caused by raw material defects are not included in the scope of engineering insurance underwriting risks. However, in the construction of high-rise buildings, the material management risk is one of the larger risks in recent years. Therefore, the indicator "A23 material reliability" is retained after comprehensive consideration, but the connotation of this indicator is changed to the material reliability problem caused by material management risk. At the same time, during the questionnaire survey, the expressions of some risk indicator factors were revised according to the suggestions of the respondents, and for the sake of simplicity of the indicators, the "A42 high-rise construction technology maturity" and "A44 high-rise construction plan rationality" were merged into "plan technology maturity".

[0131] In step S2, the following steps are also included:

[0132] S21, calculate the subjective weight in the indicator by using the ordinal relationship method G1;

[0133] S22. Calculate the objective weights in the indicators by entropy weight method;

[0134] S23. Calculate the combined weights of subjective and objective weights through game theory;

[0135] S24. A high-rise building construction risk assessment model is constructed based on the cloud model and combined weights.

[0136] In step S21, the subjective weight in the index is calculated by the ordinal relationship method G1 as follows:

[0137] Determine the order relationship between the indicators at the indicator layer and the criterion layer, and set the risk indicator of a certain indicator layer as an initial indicator set X = {x 1 ,x 2 ,x 3 ,…,x n}, experts sort the importance of n indicators in the initial indicator set. If x i The importance of x is greater than j , then recorded as n indices can determine a unique order relationship, denoted as

[0138] Determine the importance of adjacent indicators, assign importance values ​​between adjacent indicators through experts, and use r k express,

[0139]

[0140] In the formula, r k The assignment of refers to the following table, are the independent weights of the kth and k-1th indicators respectively;

[0141]

[0142] r k Assignment reference table

[0143] Calculate the weight of each indicator, assuming that the experts assign the importance of two adjacent indicators to r k , calculate the more important indicator among the two adjacent indicators, that is, the independent weight of the kth indicator

[0144]

[0145] Group weighted fusion uses the weighted average method to fuse the values ​​assigned by different experts for each indicator to obtain the subjective weighted value.

[0146]

[0147] In the formula, m is the number of experts;

[0148] In step S22, the objective weight in the index is calculated by the entropy weight method as follows:

[0149] Assuming that there are m evaluation experts invited and there are n evaluation indicators in a certain evaluation indicator system, the original judgment matrix X is constructed as:

[0150]

[0151] In the formula, x ij is the value of the jth evaluation index by the i-th evaluation expert,

[0152] The original judgment matrix is ​​standardized and the calculation formula is as follows:

[0153] Positive indicators:

[0154] Negative indicators:

[0155] Calculate the entropy e of the jth evaluation index j

[0156]

[0157] In the formula, m is the number of evaluation experts; To ensure that each value is meaningful, when P ij When =0, shift to the right by 0.0001.

[0158] Calculate the weight of evaluation index

[0159]

[0160] In step S23, the combined weight calculation of the subjective and objective weights is performed through game theory as follows:

[0161] Construct a basic weight vector set. If L methods are used to calculate multiple weights of n indicators, the vector set is:

[0162] w j ={w j1 ,w j2 ,w j3 ,…,w jn},(j=1,2,…,L)

[0163] Then any linear combination of L weight vectors is:

[0164]

[0165] In the formula, α i is the combination coefficient, α i >0 and α 1 +α 2 +…+α L =1,

[0166] Optimizing the combination coefficient The optimization objective function is:

[0167]

[0168] Through the differential characteristics, the optimal linear equations equivalent to the objective function are obtained:

[0169]

[0170] Determine the optimal combination weight W, and calculate the optimal combination coefficient (α 1 ,α 2 ,…,α L ), and normalized to obtain Finally, the optimal weight W is calculated

[0171]

[0172] In step S24, a high-rise building construction risk assessment model is constructed based on the cloud model and the combined weights. Specifically,

[0173] The three cloud digital features of expectation Ex, entropy En, and super entropy He are transformed from qualitative fuzzy concepts to quantitative data through the forward cloud generator, and converted into cloud drop drop (x i ,μ(x i )), (i=1,2,3,…,n), the cloud model is drawn by the generated n cloud droplets, and the steps of the forward cloud generator algorithm are as follows:

[0174] ① Calculate the characteristic values ​​Ex, En and the number of cloud droplets to be generated n, where n = 3000;

[0175] ② Generate a normal random number x with expected value and standard deviation Ex and He respectively i ;

[0176] ③Calculate the normal random number x j The corresponding membership μ(x i );

[0177]

[0178] Let (x i ,μ(x i )) is a cloud droplet, which represents the specific embodiment of the language value represented by the cloud droplet in quantity. Repeat steps ①-③ until n cloud droplets are generated;

[0179] The quantitative data is transformed into qualitative fuzzy concepts through the reverse cloud generator. The calculation steps of the reverse cloud generator are as follows:

[0180] ①Calculate the mean of sample data

[0181]

[0182] ② Calculate the absolute center distance A of the sample:

[0183]

[0184] ③Calculate the sample variance S 2 :

[0185]

[0186] Calculate cloud digital features (Ex, En, He) based on the basic features of sample data:

[0187] ① Calculate the expected Ex:

[0188]

[0189] ② Calculate entropy En:

[0190]

[0191] ③ Calculate the super entropy He:

[0192]

[0193] Determine the evaluation level. The evaluation level describes the risk level faced during the construction period of high-rise buildings. In order to set the evaluation level to fit the actual risk of high-rise building construction, refer to the "Construction Project Management Code" GB / T50326-2017 and existing literature, divide the risk level of high-rise building construction into 5 levels, the risk level set V = {low risk, lower risk, medium risk, higher risk, high risk}, and the evaluation standard cloud. According to the risk evaluation level and risk evaluation characteristics, a domain space U with bilateral constraints is set. The domain space U is defined as [0,10] and is divided into 5 sub-intervals according to the risk level, of which the i-th sub-interval is Specifically, they are [0,2], [2,4], [4,6], [6,8], and [8,10]. According to the divided sub-intervals, the following formula is used:

[0194]

[0195] Calculate the cloud digital characteristics of each risk level. The calculation results are shown in Table

[0196]

[0197] Evaluation level cloud model digital feature table

[0198] Where s is a constant, which is obtained based on the aggregation degree of the debugged cloud droplets, where s = 0.07;

[0199] Calculate the index cloud parameters, invite experts to assign values ​​according to the actual situation of the risk index, and use the reverse cloud generator to obtain the variance of the risk index and the cloud digital characteristics.

[0200] Let the jth cloud digital feature be C j (Ex j ,En j ,He j ), the formula is as follows:

[0201]

[0202] In the formula, p is the number of experts, x ij represents the value assigned by the ith expert to indicator j, is the variance;

[0203] Determine the evaluation comprehensive cloud and risk level, input the calculated combined weight value and cloud digital features into the reverse cloud generator, and calculate the comprehensive cloud digital features (Ex, En, He), as follows:

[0204]

[0205] The digital features of the comprehensive cloud are input into the forward cloud generator, and the evaluation comprehensive cloud map is generated using MATLAB software. The risk level of high-rise building construction is comprehensively determined by comparing and analyzing the relative positions of the standard cloud and the comprehensive cloud and combining the similarity between the comprehensive cloud and the standard cloud. The similarity between the comprehensive cloud and the standard cloud is calculated using the Euclidean distance, and the calculation formula is as follows:

[0206]

[0207] In step S3, the engineering insurance premium rate for construction risk assessment based on the high-rise building construction risk assessment model is determined as follows:

[0208] Calculate a basic rate determination value within the basic rate range;

[0209] According to the subdomain interval corresponding to the risk level, the adjustment coefficient is calculated using the linear interpolation method;

[0210] Substitute the accurate values ​​of the basic rate and adjustment coefficient into the general formula for determining the increase and decrease method to calculate the determined pure rate of engineering insurance. The pure rate of high-rise building construction engineering insurance is determined by the increase and decrease method. The pure rate calculation formula of the increase and decrease method is as follows:

[0211] r=r emin / max ×(1+α) (4-23)

[0212] Where: r emin / max is the basic rate value range, (1+α) is the adjustment coefficient, and α is the risk impact coefficient.

[0213] In order to prevent the premium from being too uniform, the insurance company has developed a reference table of engineering insurance rates based on the previous experience of setting rates for different insured projects. The insured projects can obtain the basic rate value range according to the nature of the project, that is, r emin / max The following table shows the construction project all-risk premium rate table of a certain insurance company.

[0214]

[0215] Reference table of all risks premium rates for construction projects (material loss part)

[0216] Since the insured projects have different risks in different regions, construction environments, construction plans, etc., in order to achieve the differentiation of insurance premiums, the actual construction risk level of the insured projects is converted into the risk impact coefficient α by evaluating the actual construction risk level of the insured projects, and the basic rate is corrected to obtain the final pure rate of engineering insurance. According to the principle that the floating value of the risk impact coefficient should not be too large or too small, if the floating value is set too large, the span of the adjustment coefficient will become larger, and the set rate will easily exceed the reasonable value range of the rate reference table; if the floating value is set too small, it will not be easy to reflect the actual risk situation of the project in the set rate. Therefore, after comprehensive consideration and reference to relevant literature, the floating maximum value of the risk impact coefficient α is set to ±10%, and according to the cloud model domain allocation method, it is divided into five risk impact coefficient intervals in equal proportion. When the construction risk evaluation level of the insured project is high, the risk impact coefficient α takes a positive value, and when the risk evaluation level is low, it takes a negative value. The specific values ​​are shown in the table below.

[0217]

[0218] Adjustment coefficient reference table

[0219] In step S4, the project insurance premium rate is determined through construction risk assessment to calculate the project example and determine the feasibility of the project. Specifically,

[0220] The engineering examples based on trial calculations are identified through the use of the order relationship method G1 to determine the subjective weights, the entropy weight method to determine the objective weights, the game theory combination weights, the cloud model comprehensive evaluation and the engineering insurance premium rate determination.

[0221] For example, the risk assessment index system for high-rise building construction is constructed, from which the construction risk index set of the project can be determined. The first-level index consists of 5 indicators: A = (A 1 ,A 2 ,A 3 ,A 4 ,A 5 ) = (personnel risk, mechanical equipment risk, management risk, solution technology risk, environmental risk), where each first-level indicator contains multiple second-level indicator factors: A 1 =(A 11 ,A 12 ,A 13 ,A 14 ) = (manager's ability level, construction workers' safety awareness, workers' physiological and psychological state, workers' operating proficiency), A 2 =(A 21 ,A 22 ,A 23 ,A 24 ) = (equipment operation reliability, protective equipment configuration, material reliability, equipment maintenance and upkeep), A 3 =(A 31 ,A32 ,A 33 ,A 34 ,A 35 ) = (high-level accident emergency plan, risk management system defects, management communication information distortion, personnel safety education and training, safety inspection and prevention), A 4 =(A 41 ,A 42 ,A 43 ) = (construction period, technical maturity of the scheme, implementation of technical disclosure), A 5 =(A 51 ,A 52 ,A 53 ,A 54 ,A 55 ) = (influence of surrounding buildings and facilities, hydrogeological conditions, accidents, natural disasters, construction environment and climate).

[0222] The G1 method is used to calculate the subjective weight of risk indicators, and each indicator set is ranked according to its importance and k The reference table assigns importance to adjacent indicators, and uses the weighted average method to organically integrate the values ​​assigned by different group members for each indicator, so as to obtain a more reasonable subjective weight value. The hierarchical relationship of the criteria given by the group member is A 1 >A 3 >A 5 >A 2 >A 4 , the order relation is recorded as The importance is assigned as:

[0223]

[0224] Calculated by formula 4-2

[0225]

[0226] Calculate the weights of the remaining indicators of the first-level indicator according to formula 4-3:

[0227]

[0228] In summary, the weight value assigned by the team member to the first-level indicator is:

[0229]

[0230] Similarly, the weights of the first-level indicators assigned by the other evaluation team members can be obtained as shown in the table below. Finally, the weighted average method is used to obtain the final subjective weight of the first-level indicator.

[0231]

[0232] Group-level indicator weight table

[0233] Use formula 4-4 to get the final subjective weight of the first-level indicator:

[0234]

[0235] Repeat the above steps to calculate the weights, and calculate the weights of each secondary indicator. The weights of each indicator layer and the final group fusion subjective weights are as follows:

[0236] 1. Calculate A 1 Subjective weight of personnel risk:

[0237]

[0238]

[0239] A 1 The personnel risk group indicator weight table uses formula 4-4 to obtain A 1 Subjective weight of personnel risk:

[0240]

[0241] 2. Calculate A 2 Subjective weight of equipment and material risk:

[0242]

[0243] A 2 The weight table of equipment and material risk group indicators is obtained by using formula 4-4. 2 Subjective weight of equipment and material risk:

[0244]

[0245] 3. Calculate A 3 Managing risk subjective weights:

[0246]

[0247] A 3 The weight table of management risk group indicators is obtained by using formula 4-4. 3 Managing risk subjective weights:

[0248]

[0249] 4. Calculate A 4 Subjective weight of technical risk of the solution:

[0250]

[0251] A 4The weight table of technical risk group indicators of the solution is obtained by using formula 4-4. 4 Subjective weight of technical risk of the solution:

[0252]

[0253] 5. Calculate A 5 Subjective weight of environmental risk:

[0254]

[0255] A 5 The weight table of environmental risk group indicators is obtained by using formula 4-4. 5 Subjective weight of environmental risk:

[0256]

[0257] Finally, the subjective weight values ​​of each indicator layer are summarized as shown in the table below.

[0258]

[0259]

[0260] Indicator subjective weight table

[0261] An evaluation team consisting of 10 experts and project construction management personnel was invited to score the questionnaire. The scoring value is [0,10]. The evaluation level corresponding to each score corresponds to the evaluation level in the cloud model. Due to the high sensitivity of the entropy weight method, in order to obtain reliable objective weights, the evaluation team is required to score as accurately as possible. The specific scores are shown in the table below.

[0262]

[0263]

[0264] Index importance assignment table

[0265] The recovered scoring values ​​are used as the data basis for the entropy weight method, and the objective weights are calculated using Excel input formulas (4-5)-(4-9). The results are shown in the table below.

[0266]

[0267] Objective weight table of entropy weight method

[0268] The subjective weight calculated by the G1 method and the objective weight calculated by the entropy weight method are combined through game theory. Let the subjective weight be w 1 , the optimal linear combination coefficient is α 1 , the objective weight is w 2, the optimal linear combination coefficient is α 2 , taking the first-level index as an example, use formulas (4-10)-(4-12) to calculate the combined weight w. The calculation is as follows:

[0269] First, calculate the weight vector product:

[0270]

[0271] Compute the optimal linear combination:

[0272]

[0273] Solving for α 1 ≈0.6796,α 2 ≈0.3207, after normalization, the optimal linear combination is α 1 =0.679, α 2 =0.321. In the case of negative numbers in the calculated linear combination of secondary indicators, it is necessary to add a constant to the obtained linear combination and normalize it to obtain the optimal linear combination.

[0274] Finally, calculate the optimal combination weight of the first-level indicators:

[0275] w=α 1 w 1 +α 2 w 2 =0.679×(0.231,0.190,0.207,0.170,0.202)

[0276] +0.321×(0.236, 0.197, 0.192, 0.177, 0.198)

[0277] =(0.233,0.193,0.202,0.172,0.201)

[0278] Similarly, the optimal linear coefficient and combination weight of the secondary index are obtained. The calculated combination weights are summarized in the following table.

[0279]

[0280]

[0281] Game Theory Combination Weight Table

[0282] The cloud model is comprehensively evaluated to calculate the cloud digital features of the secondary indicators. The evaluation team assigns the importance of the secondary indicators as the data source for the comprehensive evaluation of the cloud model. The specific table shows the indicator importance assignment. According to the expert scores, the reverse cloud generator is used to calculate the cloud digital features of the secondary indicators using formulas (4-13)-(4-18). Take the secondary indicator A11 as an example:

[0283] ①Calculate the average score

[0284]

[0285] ② Calculate the absolute center distance A of the sample:

[0286]

[0287] ③Calculate the variance S 2 :

[0288]

[0289] ④ Computing cloud digital features (Ex, En, He):

[0290]

[0291]

[0292] That is, the secondary indicator A 11 The cloud digital features of are (4.49, 0.165, 0.069). Similarly, the cloud digital features of the remaining secondary indicators are calculated based on the scores of each indicator.

[0293] Calculate the digital features of the primary index cloud and the digital features of the comprehensive cloud

[0294] The combined weights of each secondary index and the cloud digital features are used to calculate the cloud digital features of each primary index using formula (4-21). Similarly, the comprehensive cloud digital features are calculated. 1 For example:

[0295] Level 1 indicator A 1 The weight of each secondary indicator is: 1 =(0.197,0.212,0.333,0.257)

[0296] The digital characteristics of the secondary indicator cloud are: A 11 =(4.49,0.1654,0.0689),A 12 =(7.02,0.2306,0.0688),A 13 =(7.95,0.1880,0.0862),A 14=(7.21,0.2657,0.0980).

[0297] Calculate the first-level index A 1 Cloud Digital Features:

[0298]

[0299] Similarly, the remaining first-level indicator cloud digital features and comprehensive cloud digital features can be calculated, as summarized in the following table:

[0300]

[0301]

[0302] Cloud Digital Feature Table

[0303] By comparing and analyzing the digital characteristics of the comprehensive cloud and the indicator cloud, the values ​​of entropy and super entropy are close to those of the standard cloud, indicating that the cloud droplet thickness, condensation degree, etc. of the subsequently generated comprehensive cloud map and indicator cloud map are good. Based on this, it can be considered that the construction risk assessment results based on the combined weighted-cloud model have certain stability and reliability.

[0304] Comprehensive evaluation of cloud model, input the digital features of comprehensive cloud and primary indicator cloud into the forward cloud generator, i.e. MATLAB2018b software, and generate comprehensive evaluation cloud and primary indicator evaluation clouds based on the standard cloud. The blue cloud is the standard cloud, from left to right they are "low risk", "lower risk", "medium risk", "higher risk" and "high risk" respectively, and the red cloud is the evaluation cloud. The risk level of each evaluation cloud can be intuitively judged based on the standard cloud.

[0305] analyze Figure 3 From the comprehensive cloud comparison chart, it can be seen that most of the cloud droplets of the comprehensive cloud are at medium risk, and the expectation of the most representative qualitative concept in the digital characteristics of the comprehensive cloud is between [4,6]. Therefore, it can be intuitively judged that the risk level of the high-rise building construction is "medium risk".

[0306] Calculate the similarity between the synthetic cloud and the standard cloud to verify the intuitively judged risk level. The calculation results are shown in the table below.

[0307]

[0308] Similarity table between synthetic cloud and standard cloud

[0309] According to the similarity between the comprehensive cloud and the standard cloud in the table, the maximum similarity between the comprehensive cloud and the standard cloud is 0.625, that is, the similarity with the medium risk is the largest, which verifies the risk level determined intuitively above. Combining intuitive judgment and similarity calculation, the risk level of the high-rise building construction is determined to be "medium risk", which is consistent with the actual project. Although the overall construction risk level of the project is average, risks are everywhere during the construction process, and the risk level of personnel and environment in the first-level indicators is at a high risk, and the evaluation level of management is at a medium risk. Therefore, project risk management personnel need to be vigilant at all times, do a good job in risk management, strictly control safety, and ensure the smooth completion of the project. The following is a comparison chart and analysis of the evaluation clouds of each first-level indicator.

[0310] (1) Personnel risk assessment cloud comparison chart and analysis

[0311] analyze Figure 4 Personnel Risk A 1 From the comparison chart of the evaluation cloud, it can be seen that most of the cloud droplets in the evaluation cloud are at "higher risk", and the value that best represents the qualitative concept in the digital characteristics of the evaluation cloud is between [6,8]. Therefore, the risk level of the first-level indicator personnel risk is intuitively judged to be "higher risk".

[0312]

[0313] Similarity table between primary index evaluation cloud and standard cloud

[0314] According to the similarity table of the first-level indicator evaluation cloud and the standard cloud, the maximum similarity between the evaluation cloud and the standard cloud is 0.854, that is, the similarity with "higher risk" is the largest, which verifies the risk level of the above intuitive judgment. Combining intuitive judgment and similarity calculation, the risk level of the first-level indicator personnel risk is determined to be "higher risk". Analyzing the digital characteristics of each second-level indicator cloud under personnel risk, the risk level of the two indicators of construction personnel safety awareness and construction personnel physiological and psychological state has reached a high risk level. The construction personnel have a low level of cultural knowledge, and there are obstacles in safety training communication. They cannot fully recognize the types of various safety risks, and there are great safety risks. In addition, since the project is located in Guangdong Province, and the temperature in Guangdong Province is high all year round, it is easy to cause construction personnel to suffer from heat stroke, impetuous mood and other adverse conditions. Project managers should carry out targeted risk control, such as arranging working hours reasonably and working hard on safety training.

[0315] (2) Equipment and material risk assessment cloud comparison chart and analysis

[0316] analyze Figure 5 Equipment Material A 2From the comparison chart of the evaluation cloud, it can be seen that most of the cloud droplets in the evaluation cloud are at "medium risk", and the value that best represents the qualitative concept in the digital characteristics of the evaluation cloud is between [4,6]. It can be intuitively judged that the risk level of the mechanical equipment risk is "medium risk".

[0317] According to the similarity table between the first-level indicator evaluation cloud and the standard cloud, the maximum similarity between the evaluation cloud and the standard cloud is 0.537, that is, the similarity with "medium risk" is the largest, which verifies the risk level determined intuitively above. Combining intuitive judgment and similarity calculation, the risk level of the first-level indicator equipment and material risk is determined to be "medium risk". Analysis of the digital characteristics of each second-level indicator cloud under the equipment and material risk shows that, except for the indicator "material reliability" which is at medium risk, the other second-level indicator factors are at low risk. The equipment in this project has a short service life, maintenance is also carried out simultaneously, and safety protection equipment is complete. In addition, among the construction risks of high-rise buildings, the management risk of materials has been one of the larger risks in recent years, especially the insulation materials in high-rise buildings. Therefore, we still cannot take it lightly, and continue to do a good job in various management and control work to ensure the safety of construction workers.

[0318] (3) Management risk assessment cloud comparison chart and analysis

[0319] analyze Figure 6 From the comparison chart of the management risk A3 evaluation cloud, it can be seen that most of the cloud droplets in the evaluation cloud are at "medium risk", and the expected value of the digital characteristics of the evaluation cloud that best represents the qualitative concept is between [4,6]. It can be intuitively judged that the risk level of the management risk is "medium risk".

[0320] According to the similarity table of the first-level indicator evaluation cloud and the standard cloud, the maximum similarity between the evaluation cloud and the standard cloud is 0.692, that is, the similarity with "medium risk" is the largest, which verifies the risk level determined intuitively above. Combining intuitive judgment and similarity calculation, the risk level of the first-level indicator management risk is determined to be "medium risk". Analysis of the digital characteristics of each second-level indicator cloud under management risk shows that the soundness and implementation of the risk management system and the safety education and training of construction personnel are at a high risk, and the rest are at medium risk. This shows that managers still need to increase their implementation efforts in terms of systems and safety training, fulfill their responsibilities, and not just go through the motions, threatening the safety of life and property.

[0321] (4) Comparison chart and analysis of technical risk assessment of solutions Figure 7 Solution Technical Risk A 4 From the comparison chart of the evaluation cloud, it can be seen that most of the cloud droplets in the evaluation cloud are at "medium risk", and the expected value of the digital characteristics of the evaluation cloud that best represents the qualitative concept is between [4,6]. Intuitively judging that the risk level of the technical risk of the solution is "medium risk".

[0322] According to the similarity table of the first-level indicator evaluation cloud and the standard cloud, the maximum similarity between the evaluation cloud and the standard cloud is 0.708, that is, the similarity with "medium risk" is the largest, which verifies the risk level determined intuitively above. Combining intuitive judgment and similarity calculation, the risk level of the technical risk of the first-level indicator solution is determined to be "medium risk". Analysis of the digital characteristics of each second-level indicator cloud under management risk shows that the construction period is at a high risk. Risks develop in multiple directions as construction progresses, brewing greater or generating new risks, with greater randomness, reminding managers to always monitor changes in all aspects of the construction site and respond in a timely manner to control risks within an acceptable range.

[0323] (5) Environmental risk assessment cloud comparison chart and analysis

[0324] analyze Figure 8 Environmental Risk A 5 From the comparison chart of the evaluation cloud, it can be seen that most of the cloud droplets in the evaluation cloud are at "higher risk", and the expected value of the digital characteristics of the evaluation cloud that best represents the qualitative concept is between [6,8]. It can be intuitively judged that the risk level of the environmental risk is "medium risk".

[0325] According to the similarity table between the first-level indicator evaluation cloud and the standard cloud, the maximum similarity between the evaluation cloud and the standard cloud is 0.652, that is, the similarity with "higher risk" is the largest, which verifies the risk level determined intuitively above. Combining intuitive judgment and similarity calculation, the risk level of the first-level indicator management risk is determined to be "higher risk". The second-level management risk indicators include: the impact of surrounding buildings and roads, hydrogeological conditions, material and construction stacking, natural disasters and construction site environment and climate conditions, among which natural disasters and construction site environment and climate conditions are risk factors that are difficult to control. The project is located in an area close to the sea, with a hot climate, typhoons and rainstorms, and many other adverse environments. Project managers need to prepare emergency management plans for adverse conditions and strengthen the drills of emergency plans to minimize losses.

[0326] Engineering Insurance Rate Determination

[0327] The calculation of the pure insurance rate of the Children's Health Building project adopts the increase and decrease method based on the construction risk evaluation results. The selection of the basic rate and adjustment coefficient refers to the reference table of all-risk rates for construction projects (material loss part) and the reference table of adjustment coefficients. The specific value is determined through the construction risk level and comprehensive cloud digital characteristics to obtain the pure insurance rate of the project.

[0328] Through the comprehensive evaluation of the cloud model, it is concluded that the construction risk level of the Children's Health Building project is "medium risk", and the project belongs to the nature of a hospital. The basic rate range r is selected. emin / maxis 2.1‰-2.8‰, and the adjustment coefficient (1+α) is [0.98,1.02). The basic rate and adjustment coefficient obtained are still in the range of values. The specific value is determined by the expected value that best represents the qualitative concept in the comprehensive cloud digital feature. The comprehensive cloud digital feature is (5.5888,0.2161,0.0657). The basic rate range corresponds to the construction risk level range, that is, [0,10]; the adjustment coefficient range corresponds to the medium risk sub-range, that is, [4,6]. The specific value of the pure rate of engineering insurance is calculated by the linear interpolation method. The calculation is as follows:

[0329]

[0330] Calculate the specific value r e =2.4912‰, (1+α)=1.0118, and then use formula 4.23 to calculate the engineering insurance net rate:

[0331] r=r e ×(1+α)=2.4912‰×1.0118=2.5206‰

[0332] Therefore, the pure premium rate of the Children's Health Building project for all risks of construction engineering is 2.5206‰. From a qualitative perspective, the higher the project construction risk level, the closer the pure premium rate value is to the maximum value of the rate value range; on the contrary, the lower the risk level, the closer the pure premium rate value is to the minimum value. Through comprehensive evaluation, the construction risk level of the project is medium risk, and the expected value that best represents the qualitative concept is between [4,6] and close to high risk. The calculated pure premium rate is located above the middle value of the value range in the reference rate table, which realizes that the engineering insurance rate calculated by the constructed determination model corresponds to the actual construction risk situation of the project, which can show the feasibility of the rate determination model and provide a calculation model for the determination of insurance rates for high-rise housing construction projects.

[0333] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The insurance premium rate of high-rise building construction projects based on construction risk assessment is characterized by: The following steps are involved: S1. Construct a risk indicator system for high-rise building construction; S2. Construct a high-rise building construction risk assessment model based on the high-rise building construction risk index system; S3. Constructing construction risk assessment based on the risk assessment model for high-rise building construction to determine the engineering insurance premium rate; S4. The project insurance premium rate is determined through construction risk assessment, and the project examples are calculated and feasibility is determined.

2. The high-rise building construction project insurance premium rate determination based on construction risk assessment according to claim 1 is characterized in that: Step S1 also includes the following steps: S11. Identify the initial risk factors in the construction process of high-rise buildings by adopting the grounded theory risk identification method; S12. Filter and screen initial risk factors through questionnaire survey; S13, based on the Cronbach reliability coefficient method, the questionnaire survey was used to test the relevance and consistency of the questionnaire survey data; S14. Construct a high-rise building construction risk index system based on the relevance and consistency of the completed inspection questionnaire survey data.

3. The high-rise building construction project insurance premium rate determination based on construction risk assessment according to claim 2 is characterized in that: Initial risk factors include personnel risk, equipment and material risk, management risk, program and technology risk, and environmental risk.

4. The high-rise building construction project insurance premium rate determination based on construction risk assessment according to claim 3 is characterized in that: In step S2, the following steps are also included: S21, calculate the subjective weight in the indicator by using the ordinal relationship method G1; S22. Calculate the objective weights in the indicators by entropy weight method; S23. Calculate the combined weights of subjective and objective weights through game theory; S24. A high-rise building construction risk assessment model is constructed based on the cloud model and combined weights.

5. The high-rise building construction project insurance premium rate determination based on construction risk assessment according to claim 4 is characterized in that: In step S21, the subjective weight in the index is calculated by the ordinal relationship method G1 as follows: Determine the order relationship between the indicators at the indicator layer and the criterion layer, and set the risk indicator of a certain indicator layer as an initial indicator set X = {x1, x2, x3, ..., x n }, experts sort the importance of n indicators in the initial indicator set. If x i The importance of x is greater than j , then recorded as n indices can determine a unique order relationship, denoted as Determine the importance of adjacent indicators, assign importance values ​​between adjacent indicators through experts, and use r k express, In the formula, are the independent weights of the kth and k-1th indicators respectively; Calculate the weight of each indicator, assuming that the experts assign the importance of two adjacent indicators to r k , calculate the more important indicator among the two adjacent indicators, that is, the independent weight of the kth indicator Group weighted fusion uses the weighted average method to fuse the values ​​assigned by different experts for each indicator to obtain the subjective weighted value. In the formula, m is the number of experts; In step S22, the objective weight in the index is calculated by the entropy weight method as follows: Assuming that there are m evaluation experts invited and there are n evaluation indicators in a certain evaluation indicator system, the original judgment matrix X is constructed as: In the formula, x ij is the value of the jth evaluation index by the i-th evaluation expert, The original judgment matrix is ​​standardized and the calculation formula is as follows: Positive indicators: Negative indicators: Calculate the entropy e of the jth evaluation index j In the formula, m is the number of evaluation experts; Calculate the weight of evaluation index In step S23, the combined weight calculation of the subjective and objective weights is performed through game theory as follows: Construct a basic weight vector set. If L methods are used to calculate multiple weights of n indicators, the vector set is: w j ={w j1 ,w j2 ,w j3 ,…,w jn },(j=1,2,…,L) Then any linear combination of L weight vectors is: In the formula, α i is the combination coefficient, α i >0 and α1+α2+…+α L =1, Optimizing the combination coefficient The optimization objective function is: Through the differential characteristics, the optimal linear equations equivalent to the objective function are obtained: Determine the optimal combination weight W and find the optimal combination coefficient (α1, α2,…, α L ), and normalized to obtain Finally, the optimal weight W is calculated In step S24, a high-rise building construction risk assessment model is constructed based on the cloud model and the combined weights. Specifically, The three cloud digital features of expectation Ex, entropy En, and super entropy He are transformed from qualitative fuzzy concepts to quantitative data through the forward cloud generator, and converted into cloud drop drop (x i ,μ(x i )), (i=1,2,3,…,n), the cloud model is drawn by the generated n cloud droplets, and the steps of the forward cloud generator algorithm are as follows: ① Calculate the characteristic values ​​Ex, En and the number of cloud droplets to be generated n, where n = 3000; ② Generate a normal random number x with expected value and standard deviation Ex and He respectively i ; ③Calculate the normal random number x j The corresponding membership μ(x i ); Let (x i ,μ(x i )) is a cloud droplet, which represents the specific embodiment of the language value represented by the cloud droplet in quantity. Repeat steps ①-③ until n cloud droplets are generated; The quantitative data is transformed into qualitative fuzzy concepts through the reverse cloud generator. The calculation steps of the reverse cloud generator are as follows: ①Calculate the mean of sample data ② Calculate the absolute center distance A of the sample: ③Calculate the sample variance S 2 : Calculate cloud digital features (Ex, En, He) based on the basic features of sample data: ① Calculate the expected Ex: ② Calculate entropy En: ③Calculate the super entropy He: Determine the evaluation level, divide the construction risk level of high-rise building projects into 5 levels, the risk level set V = {low risk, lower risk, medium risk, higher risk, high risk}, and the evaluation standard cloud. According to the risk evaluation level and risk evaluation characteristics, set a domain space U with bilateral constraints. Define the domain space U = [0,10] and divide it into 5 sub-intervals according to the risk level, where the i-th sub-interval is Specifically, they are [0,2], [2,4], [4,6], [6,8], and [8,10]. Based on the divided sub-intervals, the following formula is used to calculate the cloud digital features of each risk level: Where s is a constant, which is obtained based on the aggregation degree of the debugged cloud droplets, where s = 0.07; Calculate the index cloud parameters, invite experts to assign values ​​according to the actual situation of the risk index, and use the reverse cloud generator to obtain the variance of the risk index and the cloud digital characteristics. Let the jth cloud digital feature be C j (Ex j ,En j ,He j ), the formula is as follows: In the formula, p is the number of experts, x ij represents the value assigned by the ith expert to indicator j, is the variance; Determine the evaluation comprehensive cloud and risk level, input the calculated combined weight value and cloud digital features into the reverse cloud generator, and calculate the comprehensive cloud digital features (Ex, En, He), as follows: The digital features of the comprehensive cloud are input into the forward cloud generator, and the evaluation comprehensive cloud map is generated using MATLAB software. The risk level of high-rise building construction is comprehensively determined by comparing and analyzing the relative positions of the standard cloud and the comprehensive cloud and combining the similarity between the comprehensive cloud and the standard cloud. The similarity between the comprehensive cloud and the standard cloud is calculated using the Euclidean distance, and the calculation formula is as follows:

6. The high-rise building construction project insurance premium rate determination based on construction risk assessment according to claim 5 is characterized in that: In step S3, the engineering insurance premium rate for construction risk assessment based on the high-rise building construction risk assessment model is determined as follows: Calculate a basic rate determination value within the basic rate range; According to the subdomain interval corresponding to the risk level, the adjustment coefficient is calculated using the linear interpolation method; Substitute the accurate values ​​of the basic rate and adjustment coefficient into the general formula for determining the increase and decrease method to calculate the determined pure rate of engineering insurance. The pure rate of high-rise building construction engineering insurance is determined by the increase and decrease method. The pure rate calculation formula of the increase and decrease method is as follows: r＝r emin / max ×(1+α) Where: r emin / max is the basic rate value range, (1+α) is the adjustment coefficient, and α is the risk impact coefficient.

7. The high-rise building construction project insurance premium rate determination based on construction risk assessment according to claim 6 is characterized in that: In step S4, the project insurance premium rate is determined through construction risk assessment to calculate the project example and determine the feasibility of the project. Specifically, The engineering examples based on trial calculations are identified by determining subjective weights through the order relationship method G1, objective weights through the entropy weight method, combined weights through game theory, comprehensive evaluation through cloud models and engineering insurance premium rate determination.

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