Project performance-based evaluation method for assembly-based construction technology system

Abstract

The application discloses a kind of based on project performance assembly construction technology system evaluation method, belong to civil engineering project management field.The application solves the problem of poor accuracy caused by subjectivity in the existing assembly construction technology system evaluation method using qualitative evaluation.The application divides assembly construction technology system project into three stages, constructs multiple input-output evaluation index system, collects input and output data of different stages of completed assembly construction project of target structure system;Three-stage super-efficiency EBM model is constructed, and the total performance and construction stage performance of the completed assembly construction project of the target structure system are obtained by using the three-stage super-efficiency EBM model and the collected data;The different assembly construction technology systems used by the target structure system are compared and evaluated according to the total performance and construction stage performance.The application is mainly used for the evaluation of assembly construction technology system.

Description

Technical Field

[0001] The invention belongs to the field of civil engineering project management. Background Art

[0002] With the rapid advancement of global urbanization, the demand for infrastructure and residential construction is increasing. Prefabricated construction, due to its advantages in saving labor resources, shortening construction time, and significantly improving building quality, has become a development trend in the global construction industry. Existing research mainly qualitatively evaluates the advantages and disadvantages of different prefabricated construction technology systems based on cost, construction quality, and supply chain resilience. However, these qualitative evaluation methods can lead to inaccurate results due to their subjectivity. Therefore, quantitatively evaluating technology systems through project performance measurement results and analyzing the driving factors that influence project performance of different technology systems are currently a hot topic in prefabricated construction project management research.

[0003] Currently, there are two main methods for quantitatively measuring the performance of prefabricated construction projects: the balanced scorecard (BSC) and data envelopment analysis (DEA). The DEA method selects appropriate input and output indicators, collects and analyzes data from each project, and constructs a linear programming model to compare the inputs and outputs of decision-making units, thereby evaluating the relative efficiency of multiple decision-making units. Compared with the BSC method, the DEA method does not require the assumption of a specific production function, saving considerable time, resources, and personnel in designing indicators, collecting data, and evaluating their value. This reduces measurement complexity and avoids the impact of subjective decision-making on measurement results. However, the DEA method has significant shortcomings. It ignores the fact that prefabricated construction projects are complex production systems, encompassing the production, transportation, and assembly of prefabricated components, and that inputs and outputs in each link may vary radially and non-radially. At the same time, the DEA method fails to consider that prefabricated construction projects are a complex process encompassing the production, transportation, and construction of prefabricated components. Furthermore, the construction processes of prefabricated construction projects with different technical systems vary significantly. Therefore, a method that can accurately measure the performance of prefabricated construction projects is needed to improve the effectiveness of the evaluation. Summary of the Invention

[0004] In order to solve the problem that the existing evaluation method of prefabricated construction relies on subjective qualitative analysis, resulting in inaccurate efficiency measurement, the present invention provides an evaluation method for prefabricated construction technology system based on project performance.

[0005] The present invention provides a method for evaluating an assembly construction technology system based on project performance, comprising:

[0006] Step A1: Divide the prefabricated construction technology system project into three phases, construct a multi-input and multi-output evaluation index system, and collect input and output data from different phases of historical construction projects with the same target structural system based on the evaluation index system;

[0007] The three phases mentioned include the production phase, transportation phase and construction phase of a building project;

[0008] Step A2: construct a three-stage super-efficiency EBM model, and use the input and output data of the different stages combined with the three-stage super-efficiency EBM model to obtain the total performance and efficiency of the historical construction projects with the same target structural system at different stages;

[0009] Step A3: Compare the overall performance and performance at different stages of historical construction projects with the same target structural system, obtain the overall performance ranking of different technical systems under the target structural system and the performance differences at different stages of different technical systems, and conduct an overall and phased evaluation of the technical system for constructing the target structural system.

[0010] Furthermore, in the present invention, in step A1, the multi-input-multi-output evaluation index system includes:

[0011]

[0012] Furthermore, in the present invention, in step A2, the formula of the three-stage super-efficiency EBM model is:

[0013]

[0014] Where r * For prefabricated construction project performance; i is the slack variable of the i-th input factor; j is the decision-making unit; n is the total number of DMUs; w i The importance of input indicators, satisfying X ij and Y rj are the i-th input factor and the r-th output factor of decision-making unit j respectively; x ik and y rk are the i-th input factor and the r-th output factor of decision-making unit k; m and s are the number of inputs and outputs respectively; θ is the planning parameter of the radial part; λ j is the linear combination coefficient; ε x is the key parameter, satisfying 0≤ε x ≤1.

[0015] Furthermore, the present invention also includes a method for analyzing factors affecting performance, which includes:

[0016] Step B1: Compare the differences between all technical systems at different stages of building the target structural system and extract factors that affect performance;

[0017] Step B3: Construct a Tobit model. Utilize the factors influencing performance and the performance of historical construction projects at different stages described in Step A2 in combination with the Tobit model to obtain regression coefficients of the influencing factors. Utilize the regression coefficients to determine the degree of influence of different influencing factors on performance at different stages.

[0018] Furthermore, in the present invention, in step B2, the method for extracting factors that affect performance is: comparing and analyzing differences in production methods, transportation methods, and construction methods to obtain factors that affect performance.

[0019] Furthermore, in the present invention, in step B2, the formula of the Tobit model is:

[0020]

[0021] Where Y l Represents the project performance results, x l is the quantitative index of each influencing factor, l is the decision-making unit number, β T is the regression coefficient of the influencing factor, ε l is the error term.

[0022] The method of the present invention includes collecting data on prefabricated construction projects; constructing a multi-input, multi-output evaluation index system based on the prefabricated construction project data; calculating the performance of prefabricated construction projects using a three-stage super-efficiency EBM model and the multi-input, multi-output evaluation index system; evaluating technical systems based on the performance measurement results of prefabricated construction projects; comparing the differences between different technical systems at different stages of construction to extract influencing factors; and using a Tobit regression model to calculate the parameter values, sample means, and variances of the Tobit model to identify dominant factors and determine the main driving factors of the prefabricated construction project performance. This method effectively improves the accuracy of performance measurement and the speed of comparative evaluation of multiple technical systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of the method of the present invention;

[0024] Figure 2 It is the schematic diagram of the three-stage super-efficiency EBM model;

[0025] Figure 3 This is a comparison diagram of the component forms of the two technical systems. The left side of the figure is the component form diagram of the PC technical system, and the right side is the component form diagram of the PMC technical system.

[0026] Figure 4This is a comparison chart of component transportation methods. The left side of the figure shows the transportation method diagram of the PC technology system, and the right side shows the transportation method diagram of the PMC technology system.

[0027] Figure 5 This is a comparison diagram of the connection methods of components of two technical systems. The left side of the figure shows the connection method of components of the PC technical system, and the right side shows the connection method of components of the PMC technical system. DETAILED DESCRIPTION

[0028] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other in the absence of conflict.

[0029] Specific implementation method 1: refer to Figure 1 Specifically describing this embodiment, a method for evaluating an assembly construction technology system based on project performance includes:

[0030] Step A1: Divide the prefabricated construction technology system project into three phases, construct a multi-input and multi-output evaluation index system, and collect input and output data from different phases of historical construction projects with the same target structural system based on the evaluation index system;

[0031] The three phases mentioned include the production phase, transportation phase and construction phase of a building project;

[0032] Step A2: construct a three-stage super-efficiency EBM model, and use the input and output data of the different stages combined with the three-stage super-efficiency EBM model to obtain the total performance and efficiency of the historical construction projects with the same target structural system at different stages;

[0033] Step A3: Compare the overall performance and performance at different stages of historical construction projects with the same target structural system, obtain the overall performance ranking of different technical systems under the target structural system and the performance differences at different stages of different technical systems, and conduct an overall and phased evaluation of the technical system for constructing the target structural system.

[0034] Furthermore, in the present invention, in step A1, the multi-input-multi-output evaluation index system includes:

[0035]

[0036] Furthermore, in the present invention, in step A2, the formula of the three-stage super-efficiency EBM model is:

[0037]

[0038] Where r * For prefabricated construction project performance; i is the slack variable of the i-th input factor; j is the decision-making unit; n is the total number of DMUs; w i The importance of input indicators, satisfying X ij and Y rj are the i-th input factor and the r-th output factor of decision-making unit j respectively; x ik and y rk are the i-th input factor and the r-th output factor of decision-making unit k; m and s are the number of inputs and outputs respectively; θ is the planning parameter of the radial part; λ j is the linear combination coefficient; ε x is the key parameter, satisfying 0≤ε x ≤1.

[0039] Furthermore, the present invention also includes a method for analyzing factors affecting performance, which includes:

[0040] Step B1: Compare the differences between all technical systems at different stages of building the target structural system and extract factors that affect performance;

[0041] Step B3: Construct a Tobit model. Utilize the factors influencing performance and the performance of historical construction projects at different stages described in Step A2 in combination with the Tobit model to obtain regression coefficients of the influencing factors. Utilize the regression coefficients to determine the degree of influence of different influencing factors on performance at different stages.

[0042] Furthermore, in the present invention, in step B2, the method for extracting factors that affect performance is: comparing and analyzing differences in production methods, transportation methods, and construction methods to obtain factors that affect performance.

[0043] Furthermore, in the present invention, in step B2, the formula of the Tobit model is:

[0044]

[0045] Where Y l Represents the project performance results, x l is the quantitative index of each influencing factor, l is the decision-making unit number, β T is the regression coefficient of the influencing factor, ε l is the error term.

[0046] The present application avoids the traditional performance measurement method which usually regards each stage in the assembly construction project as a "black box", ignores the internal complex production process and stage difference, by constructing a three-stage super-efficiency EBM model, so that the assembly construction project performance measurement result is more accurate, and the quantitative evaluation of assembly construction technology is realized. The main driving factors affecting the performance of assembly construction project are identified by using Tobit regression model and calculating the fitted parameter value and statistical value. The implementation results not only improve the accuracy of performance measurement, but also provide more targeted suggestions for project managers in the selection of technical system in the project planning stage.

[0047] In the present example, 30 shear wall structure projects are selected as research objects, which are completed by using prefabricated concrete technology system (PC) and prefabricated reinforced block masonry technology system (PCM), among which DMU1-14 is built by PC technology system, and DMU15-30 is built by PCM technology system. The two technology systems have been accepted in accordance with relevant standards in each link from component production to construction, as shown in Table 1. Except for the construction technology specification, the acceptance standards of the two technology systems are consistent, and the project quality is considered to be the same.

[0048] Table 1 Comparison of technology system reference specifications and standards

[0049]

[0050] 2. Construction of project performance measurement model

[0051] The three-stage super-efficiency EBM model is established in the present study, so as to better evaluate the performance of assembly construction project. The "black box" in the traditional DEA model is divided into three sequentially connected subsystems, which correspond to the three stages of evaluating the performance of assembly construction project. The first stage is the prefabricated component production stage, which takes the resource consumption dimension required for component production as input, takes the production component output as output, and mainly measures the resource utilization in the component production stage. The second stage is the prefabricated component transportation stage after production, which takes the resources required for transporting components to the site as input, takes the amount of components transported to the site as output, and mainly measures the resource allocation in the transportation stage. The third stage is the prefabricated component construction stage after transportation to the site, which takes the resources required for assembling the prefabricated components transported to the site as input, takes the amount of prefabricated components assembled in the project and the content of prefabricated components per square meter as output, and measures the resources required for installing prefabricated components in the assembly construction project. The principle diagram is shown in Figure 2 .

[0052] 3. Construction of multi-input-multi-output evaluation index system

[0053] The multi-input-multi-output evaluation index system for the performance of assembly construction project includes:

[0054]

[0055] The present invention uses MAXDEA 9.0 software to solve and construct a three-stage super-efficiency EBM model. Table 2 summarizes the comprehensive efficiency and three-stage efficiency values ​​and rankings of 30 projects.

[0056] Table 2 Performance scores and rankings of prefabricated construction projects

[0057]

[0058]

[0059] The results show that among the 30 DMUs, eight prefabricated projects achieved performance scores greater than 1, with the highest efficiency score being 1.174 (DMU 26) and the lowest being 0.499 (DMU 14), representing a 57.5% difference in efficiency. Fourteen of the top 15 projects utilized the PCM technology system, indicating that in prefabricated concrete shear wall structural systems, PCM technology systems generally outperform PC technology systems, resulting in higher resource efficiency.

[0060] The three-stage super-efficiency EBM model measures the efficiency of each stage in project performance, thereby more clearly identifying the impact of each stage on the overall efficiency of prefabricated construction project performance. The results show that in the prefabricated component production stage, nine projects had efficiency values ​​greater than 1, with the highest efficiency value being 1.421 and the lowest being 0.957, representing a difference of 32.7%. Thirteen of the top 15 projects employed the PCM technology system. Notably, the top two DMUs employed the PC technology system, with efficiency values ​​of 1.421 and 1.363, significantly exceeding the efficiency values ​​of the other DMUs. In the prefabricated component transportation stage, six projects had efficiency values ​​greater than 1, with the highest efficiency value being 1.064 and the lowest being 0.546, representing a difference of 48.7%. Thirteen of the top 15 projects employed the PCM technology system. In the prefabricated component construction stage, 11 projects had efficiency values ​​greater than 1, with the highest efficiency value being 1.255 and the lowest being 0.367, representing a difference of 70.8%. The top 15 projects all applied the PCM technology system.

[0061] Overall, in prefabricated shear wall structural system projects, the performance of prefabricated construction projects using the PCM technology system is significantly better than that of the PC technology system. A comparison of the efficiency differences between the three phases of prefabricated construction projects revealed that the prefabricated component construction phase is the primary factor contributing to the superior performance of prefabricated construction projects using the PCM technology system.

[0062] 4. Extract influencing factors

[0063] The differences in production methods, transportation methods, and construction methods of the two technical systems are extracted as influencing factors.

[0064] In the component production stage, it is necessary to first clarify the production process of prefabricated concrete construction technology (PC) and prefabricated reinforced block masonry construction technology (PCM). The production methods of the two technical systems are fundamentally different. Prefabricated concrete construction technology (PC) produces different size molds in advance according to the component layout after receiving the production task, and production personnel carry out production according to their own procedures on the assembly line. Prefabricated reinforced block masonry technology (PCM) first produces four standard hollow blocks after receiving the production task, then arranges the components according to the component size using standard blocks, and production personnel use unit production method to build components and independently complete the production of each component. The difference in production methods of different technical systems leads to the difference in labor cost and material cost. PC technical system produces prefabricated components that require custom molds, while PCM technical system uses four standard blocks to produce prefabricated components and does not require additional custom molds of other sizes. This difference not only affects the efficiency of production materials, but also affects the production efficiency of workers due to the greater variety of molds. In addition, the main materials required for PC technical system prefabricated component production are concrete, while the main materials required for PCM technical system prefabricated component production are standard hollow blocks with 45% porosity and concrete. This results in a difference in the amount of main materials per cubic meter of component, with the main material usage of PCM technical system prefabricated components increasing by 45% compared to PC technical system, and the price of main materials being different. Although the unit price of steel bars is the same, the difference in steel bar usage will affect the production cost. The prefabricated components produced by PC technical system are all "one-character type", while the components produced by PCM technical system have more styles, including "one-character type", "I type", "T type", etc. (see Figure 3 ), and the volume of individual components is significantly different from that of PC technical system produced components, which may also affect the efficiency of resource utilization in the production stage. Therefore, the number of molds, the price of steel bars per cubic meter of component, the price of main materials per cubic meter of component, the amount of main materials per cubic meter of component, and the volume of individual components are selected as the influencing factors of prefabricated component production stage assembly construction performance.

[0065] In the component transportation stage, PC and PCM prefabricated components have obvious differences in transportation methods. PC prefabricated components use vertical or side-standing transportation methods and are placed in special transportation racks for fixation (see Figure 4 left side figure); PCM prefabricated components are self-stabilizing based on their component shape and do not require special transportation racks for fixation, and can be placed in the transportation vehicle based on this feature (see Figure 4(right figure), resulting in significant differences in the transport space utilization of prefabricated components during transportation between the two technology systems. Considering transport load restrictions, the number of components transported per trip is significantly related to component weight and volume. Therefore, this study selected transport space utilization, individual component volume, component weight per cubic meter, and individual component weight as factors influencing the performance of prefabricated construction projects during the component transportation phase.

[0066] During the component construction phase, PC prefabricated shear wall components use a grouting sleeve connection construction process. First, use a straightening plate to locate and straighten the vertical steel bars reserved for inserting into the prefabricated shear wall. Then align them with the reserved channels of the prefabricated shear wall. After removing the straightening plate, cast-in-place is carried out. The connection process of PCM prefabricated shear wall components uses a "rebar penetration" construction process. There is no need to locate the reserved steel bars. Instead, the steel bars are inserted into the guide tubes reserved in the shear wall components. After the prefabricated shear wall components are placed, the guide tubes are removed and cast-in-place is carried out. The connection methods of prefabricated shear wall components of the two technical systems are shown in Figure 5 . The connection accuracy requirements of prefabricated components affect the construction difficulty and quality, and the connection accuracy requirements of prefabricated shear wall components are caused by the difficulty of connecting the vertical steel bars with the reserved channels. Therefore, this study proposes the concept of installation tolerance, which is defined as the ratio of the allowable movement range of adjacent vertical steel bars to the size of the reserved channels. In addition, the frequency of component installation affects the difficulty of sequencing construction shifts, and affects both the construction labor cost and the construction time cost. Therefore, this paper selects the installation tolerance, the number of component installations per unit volume, and the number of component installations as factors affecting project performance during the construction phase.

[0067] Factors that affect performance:

[0068]

[0069] 5. Tobit model results

[0070] To identify the core influencing factors of prefabricated construction project performance, Stata17 was used to run the Tobit model to conduct an empirical analysis of 30 DMUs. The results are shown in Tables 3-5.

[0071] Table 3 Results of factors affecting the production stage

[0072] variable name Regression coefficient Standard deviation Statistics p-value Number of molds -0.126 0.024 -5.245 0.000 Number of components 0.004 0.013 0.318 0.751 Price of steel bars per cubic meter of components -0.199 0.029 -6.757 0.000 Price of main materials per cubic meter of components 0.587 117.52 0.005 0.996 Main material consumption per cubic meter of component -1.146 47.61 -0.024 0.981 Volume of a single component 0.138 0.041 3.32 0.001

[0073] Table 4 Results of factors affecting the transportation stage

[0074]

[0075] Table 5 Results of factors affecting the construction stage

[0076] variable name Regression coefficient Standard deviation Statistics p-value Component installation quantity 0.000 0.000 -0.745 0.456 Number of component installations per unit volume -0.250 0.048 -5.165 0.000

[0077] The results show that in the prefabricated component production stage, the number of molds, the price of reinforcing steel per cubic meter of component, and the volume of individual components pass the significance test and present a highly significant state (0.01≤p≤0.5), and the regression coefficients are -0.126, -0.199, and 0.138, respectively. In the prefabricated component transportation stage, the space utilization rate of the vehicle, the weight of a single cubic meter of component, and the volume of individual components present a highly significant state, and the regression coefficients are 0.764, -0.253, and -0.214, respectively. In the prefabricated component construction stage, the installation fault tolerance and the installation frequency of a single cubic meter of component present a highly significant state, and the regression coefficients are 2.660 and 0.354, respectively.

[0078] In the prefabricated component production stage, the regression coefficient of the price of reinforcing steel per cubic meter of component is -0.199, which is the highest among the significant influencing factors, indicating that it has the greatest negative impact on the performance of the production stage of the assembly construction project. The regression coefficient of the number of molds is -0.126, indicating that the more the number of molds, the lower the performance of the production stage of the assembly construction project. The regression coefficient of the volume of individual components is 0.138, which indicates that the larger the volume of individual components, the higher the performance of the production stage of the assembly construction project.

[0079] In the prefabricated component transportation stage, the regression coefficient of the space utilization rate of the vehicle is the largest at 0.764 and passes the significance test, which indicates that the space utilization rate of the vehicle has the greatest impact on the performance of this stage of the assembly construction. The regression coefficient of the weight of a single cubic meter of component is -0.253 and passes the significance test, indicating that the smaller the weight of a single cubic meter of component, the higher the performance of the assembly construction project in the transportation stage. Considering the space utilization rate and the weight limit of the vehicle, the smaller the volume of individual components, the more advantageous the performance of the assembly construction in the transportation stage (the regression coefficient is -0.214).

[0080] In the prefabricated component construction stage, the regression coefficient of the installation fault tolerance is the largest at 0.905, which is the largest among the influencing factors in the construction stage, indicating that the installation fault tolerance has the most significant impact on the performance of the assembly construction in the construction stage. The regression coefficient of the installation frequency of a single cubic meter of component is -0.250, indicating that the fewer the installation frequency of a single cubic meter of component, the more advantageous the performance of the assembly construction project in the construction stage.

[0081] While the application has been described with reference to particular embodiments thereof, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present application. It will be apparent to those skilled in the art that numerous modifications can be made within the scope of the present application as defined by the appended claims. It is intended that all such modification fall within the spirit and scope of the present application. It will be understood that the features described in connection with one embodiment can be used in connection with another embodiment.

Claims

1. A method for evaluating an assembly construction technology system based on project performance, characterized in that: include: Step A1: Divide the prefabricated construction project into three stages, construct a multi-input-multi-output evaluation index system, and collect input and output data from different stages of completed prefabricated construction projects of the target structural system based on the evaluation index system; The three stages include: prefabricated component production stage, prefabricated component transportation stage and prefabricated component construction stage; Step A2: constructing a three-stage super-efficiency EBM model, using the input and output data of the different stages combined with the three-stage super-efficiency EBM model to obtain the total performance and performance of each construction stage of the completed prefabricated construction project of the target structural system; The specific method for obtaining the overall performance and performance of each construction stage of the completed prefabricated construction project of the target structural system by using the input and output data of the different stages combined with the three-stage super-efficiency EBM model is as follows: The first stage: prefabricated component production stage, the resource consumption dimension required for component production is used as the input of the three-stage super-efficiency EBM model, and the component output is used as the output to measure the resource utilization in the component production stage; The second stage is the transportation stage of prefabricated components after production is completed. The resources required to transport the components to the site are used as the input of the three-stage super-efficiency EBM model, and the number of components delivered to the site is used as the output to measure the resource allocation during the transportation stage. Phase 3: Delivery to on-site construction phase. This phase uses the resources required to complete the delivery and assembly of components on-site as input, and the volume of prefabricated components assembled in the project and the prefabricated component content per square meter as output. This phase measures the resources required to complete the installation of the prefabricated components in the prefabricated construction project. Step A3: Analyze the overall performance and performance of each stage of completed prefabricated construction projects of the target structural system, obtain the overall performance ranking and performance ranking of each stage of the prefabricated construction projects, and conduct an overall and phased evaluation of the projects of different technical systems used in the completed prefabricated construction projects of the target structural system; In step A2, the formula of the three-stage super-efficiency EBM model is: Where r * For prefabricated construction project performance; i is the slack variable of the i-th input factor; j is the decision-making unit; n is the total number of DMUs; w i The importance of input indicators, satisfying X ij and Y rj are the i-th input factor and the r-th output factor of decision-making unit j respectively; x ik and y rk are the i-th input factor and the r-th output factor of decision-making unit k; m and s are the number of inputs and outputs respectively; θ is the planning parameter of the radial part; λ j is the linear combination coefficient; ε x is the key parameter, satisfying 0≤ε x ≤1.

2. The method for evaluating an assembly construction technology system based on project performance according to claim 1, characterized in that: It also includes a method for analyzing factors affecting performance, including: Step B1: Compare the differences in the construction phases of all technical systems in completed prefabricated construction projects and extract factors that affect performance; Step B2: Construct a Tobit model. Use the factors that affect performance and the performance of the completed prefabricated construction project at different stages described in Step A2 in combination with the Tobit model to obtain the regression coefficients of the influencing factors; use the regression coefficients to obtain the degree of influence of different influencing factors on the performance at different stages.

3. The method for evaluating an assembly construction technology system based on project performance according to claim 2, characterized in that: In step B2, the method for extracting factors that affect performance is to compare and analyze differences in prefabricated component production methods, prefabricated component transportation methods, and prefabricated component construction methods to obtain factors that affect performance.

4. The method for evaluating an assembly construction technology system based on project performance according to claim 3 is characterized in that: In step B2, the formula of the Tobit model is: Where Y l Represents the project performance results, x l is the quantitative index of each influencing factor, l is the decision-making unit number, β T is the regression coefficient of the influencing factor, ε l is the error term.

Images